A Discrete-Event Network Simulator
API
three-gpp-channel-model.cc
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1/* -*- Mode: C++; c-file-style: "gnu"; indent-tabs-mode:nil; -*- */
2/*
3 * Copyright (c) 2019 SIGNET Lab, Department of Information Engineering,
4 * University of Padova
5 * Copyright (c) 2015, NYU WIRELESS, Tandon School of Engineering,
6 * New York University
7 *
8 * This program is free software; you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License version 2 as
10 * published by the Free Software Foundation;
11 *
12 * This program is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with this program; if not, write to the Free Software
19 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
20 *
21 */
22
23#include "three-gpp-channel-model.h"
24#include "ns3/log.h"
25#include "ns3/phased-array-model.h"
26#include "ns3/node.h"
27#include "ns3/double.h"
28#include "ns3/string.h"
29#include "ns3/integer.h"
30#include <algorithm>
31#include <random>
32#include "ns3/log.h"
33#include <ns3/simulator.h>
34#include "ns3/mobility-model.h"
35#include "ns3/pointer.h"
36
37namespace ns3 {
38
39NS_LOG_COMPONENT_DEFINE ("ThreeGppChannelModel");
40
41NS_OBJECT_ENSURE_REGISTERED (ThreeGppChannelModel);
42
44static const double offSetAlpha[20] = {
45 0.0447, -0.0447, 0.1413, -0.1413, 0.2492, -0.2492, 0.3715, -0.3715, 0.5129, -0.5129,
46 0.6797, -0.6797, 0.8844, -0.8844, 1.1481, -1.1481, 1.5195, -1.5195, 2.1551, -2.1551};
47
56static const double sqrtC_RMa_LOS[7][7] = {
57 {1, 0, 0, 0, 0, 0, 0},
58 {0, 1, 0, 0, 0, 0, 0},
59 {-0.5, 0, 0.866025, 0, 0, 0, 0},
60 {0, 0, 0, 1, 0, 0, 0},
61 {0, 0, 0, 0, 1, 0, 0},
62 {0.01, 0, -0.0519615, 0.73, -0.2, 0.651383, 0},
63 {-0.17, -0.02, 0.21362, -0.14, 0.24, 0.142773, 0.909661},
64};
65
75static const double sqrtC_RMa_NLOS[6][6] = {
76 {1, 0, 0, 0, 0, 0},
77 {-0.5, 0.866025, 0, 0, 0, 0},
78 {0.6, -0.11547, 0.791623, 0, 0, 0},
79 {0, 0, 0, 1, 0, 0},
80 {-0.04, -0.138564, 0.540662, -0.18, 0.809003, 0},
81 {-0.25, -0.606218, -0.240013, 0.26, -0.231685, 0.625392},
82};
83
92static const double sqrtC_RMa_O2I[6][6] = {
93 {1, 0, 0, 0, 0, 0},
94 {0, 1, 0, 0, 0, 0},
95 {0, 0, 1, 0, 0, 0},
96 {0, 0, -0.7, 0.714143, 0, 0},
97 {0, 0, 0.66, -0.123225, 0.741091, 0},
98 {0, 0, 0.47, 0.152631, -0.393194, 0.775373},
99};
100
109static const double sqrtC_UMa_LOS[7][7] = {
110 {1, 0, 0, 0, 0, 0, 0},
111 {0, 1, 0, 0, 0, 0, 0},
112 {-0.4, -0.4, 0.824621, 0, 0, 0, 0},
113 {-0.5, 0, 0.242536, 0.83137, 0, 0, 0},
114 {-0.5, -0.2, 0.630593, -0.484671, 0.278293, 0, 0},
115 {0, 0, -0.242536, 0.672172, 0.642214, 0.27735, 0},
116 {-0.8, 0, -0.388057, -0.367926, 0.238537, -3.58949e-15, 0.130931},
117};
118
128static const double sqrtC_UMa_NLOS[6][6] = {
129 {1, 0, 0, 0, 0, 0},
130 {-0.4, 0.916515, 0, 0, 0, 0},
131 {-0.6, 0.174574, 0.78072, 0, 0, 0},
132 {0, 0.654654, 0.365963, 0.661438, 0, 0},
133 {0, -0.545545, 0.762422, 0.118114, 0.327327, 0},
134 {-0.4, -0.174574, -0.396459, 0.392138, 0.49099, 0.507445},
135};
136
145static const double sqrtC_UMa_O2I[6][6] = {
146 {1, 0, 0, 0, 0, 0},
147 {-0.5, 0.866025, 0, 0, 0, 0},
148 {0.2, 0.57735, 0.791623, 0, 0, 0},
149 {0, 0.46188, -0.336861, 0.820482, 0, 0},
150 {0, -0.69282, 0.252646, 0.493742, 0.460857, 0},
151 {0, -0.23094, 0.16843, 0.808554, -0.220827, 0.464515},
152
153};
154
163static const double sqrtC_UMi_LOS[7][7] = {
164 {1, 0, 0, 0, 0, 0, 0},
165 {0.5, 0.866025, 0, 0, 0, 0, 0},
166 {-0.4, -0.57735, 0.711805, 0, 0, 0, 0},
167 {-0.5, 0.057735, 0.468293, 0.726201, 0, 0, 0},
168 {-0.4, -0.11547, 0.805464, -0.23482, 0.350363, 0, 0},
169 {0, 0, 0, 0.688514, 0.461454, 0.559471, 0},
170 {0, 0, 0.280976, 0.231921, -0.490509, 0.11916, 0.782603},
171};
172
182static const double sqrtC_UMi_NLOS[6][6] = {
183 {1, 0, 0, 0, 0, 0},
184 {-0.7, 0.714143, 0, 0, 0, 0},
185 {0, 0, 1, 0, 0, 0},
186 {-0.4, 0.168034, 0, 0.90098, 0, 0},
187 {0, -0.70014, 0.5, 0.130577, 0.4927, 0},
188 {0, 0, 0.5, 0.221981, -0.566238, 0.616522},
189};
190
199static const double sqrtC_UMi_O2I[6][6] = {
200 {1, 0, 0, 0, 0, 0},
201 {-0.5, 0.866025, 0, 0, 0, 0},
202 {0.2, 0.57735, 0.791623, 0, 0, 0},
203 {0, 0.46188, -0.336861, 0.820482, 0, 0},
204 {0, -0.69282, 0.252646, 0.493742, 0.460857, 0},
205 {0, -0.23094, 0.16843, 0.808554, -0.220827, 0.464515},
206};
207
216static const double sqrtC_office_LOS[7][7] = {
217 {1, 0, 0, 0, 0, 0, 0},
218 {0.5, 0.866025, 0, 0, 0, 0, 0},
219 {-0.8, -0.11547, 0.588784, 0, 0, 0, 0},
220 {-0.4, 0.23094, 0.520847, 0.717903, 0, 0, 0},
221 {-0.5, 0.288675, 0.73598, -0.348236, 0.0610847, 0, 0},
222 {0.2, -0.11547, 0.418943, 0.541106, 0.219905, 0.655744, 0},
223 {0.3, -0.057735, 0.73598, -0.348236, 0.0610847, -0.304997, 0.383375},
224};
225
235static const double sqrtC_office_NLOS[6][6] = {
236 {1, 0, 0, 0, 0, 0},
237 {-0.5, 0.866025, 0, 0, 0, 0},
238 {0, 0.46188, 0.886942, 0, 0, 0},
239 {-0.4, -0.23094, 0.120263, 0.878751, 0, 0},
240 {0, -0.311769, 0.55697, -0.249198, 0.728344, 0},
241 {0, -0.069282, 0.295397, 0.430696, 0.468462, 0.709214},
242};
243
244ThreeGppChannelModel::ThreeGppChannelModel ()
245{
246 NS_LOG_FUNCTION (this);
247 m_uniformRv = CreateObject<UniformRandomVariable> ();
248 m_uniformRvShuffle = CreateObject<UniformRandomVariable> ();
249 m_uniformRvDoppler = CreateObject<UniformRandomVariable> ();
250
251 m_normalRv = CreateObject<NormalRandomVariable> ();
252 m_normalRv->SetAttribute ("Mean", DoubleValue (0.0));
253 m_normalRv->SetAttribute ("Variance", DoubleValue (1.0));
254}
255
256ThreeGppChannelModel::~ThreeGppChannelModel ()
257{
258 NS_LOG_FUNCTION (this);
259}
260
261void
262ThreeGppChannelModel::DoDispose ()
263{
264 NS_LOG_FUNCTION (this);
265 if (m_channelConditionModel)
266 {
267 m_channelConditionModel->Dispose ();
268 }
269 m_channelMatrixMap.clear ();
270 m_channelParamsMap.clear ();
271 m_channelConditionModel = nullptr;
272}
273
274TypeId
275ThreeGppChannelModel::GetTypeId (void)
276{
277 static TypeId tid = TypeId ("ns3::ThreeGppChannelModel")
278 .SetGroupName ("Spectrum")
279 .SetParent<MatrixBasedChannelModel> ()
280 .AddConstructor<ThreeGppChannelModel> ()
281 .AddAttribute ("Frequency",
282 "The operating Frequency in Hz",
283 DoubleValue (500.0e6),
284 MakeDoubleAccessor (&ThreeGppChannelModel::SetFrequency,
285 &ThreeGppChannelModel::GetFrequency),
286 MakeDoubleChecker<double> ())
287 .AddAttribute ("Scenario",
288 "The 3GPP scenario (RMa, UMa, UMi-StreetCanyon, InH-OfficeOpen, InH-OfficeMixed)",
289 StringValue ("UMa"),
290 MakeStringAccessor (&ThreeGppChannelModel::SetScenario,
291 &ThreeGppChannelModel::GetScenario),
292 MakeStringChecker ())
293 .AddAttribute ("ChannelConditionModel",
294 "Pointer to the channel condition model",
295 PointerValue (),
296 MakePointerAccessor (&ThreeGppChannelModel::SetChannelConditionModel,
297 &ThreeGppChannelModel::GetChannelConditionModel),
298 MakePointerChecker<ChannelConditionModel> ())
299 .AddAttribute ("UpdatePeriod",
300 "Specify the channel coherence time",
301 TimeValue (MilliSeconds (0)),
302 MakeTimeAccessor (&ThreeGppChannelModel::m_updatePeriod),
303 MakeTimeChecker ())
304 // attributes for the blockage model
305 .AddAttribute ("Blockage",
306 "Enable blockage model A (sec 7.6.4.1)",
307 BooleanValue (false),
308 MakeBooleanAccessor (&ThreeGppChannelModel::m_blockage),
309 MakeBooleanChecker ())
310 .AddAttribute ("NumNonselfBlocking",
311 "number of non-self-blocking regions",
312 IntegerValue (4),
313 MakeIntegerAccessor (&ThreeGppChannelModel::m_numNonSelfBlocking),
314 MakeIntegerChecker<uint16_t> ())
315 .AddAttribute ("PortraitMode",
316 "true for portrait mode, false for landscape mode",
317 BooleanValue (true),
318 MakeBooleanAccessor (&ThreeGppChannelModel::m_portraitMode),
319 MakeBooleanChecker ())
320 .AddAttribute ("BlockerSpeed",
321 "The speed of moving blockers, the unit is m/s",
322 DoubleValue (1),
323 MakeDoubleAccessor (&ThreeGppChannelModel::m_blockerSpeed),
324 MakeDoubleChecker<double> ())
325 .AddAttribute ("vScatt",
326 "Maximum speed of the vehicle in the layout (see 3GPP TR 37.885 v15.3.0, Sec. 6.2.3)."
327 "Used to compute the additional contribution for the Doppler of"
328 "delayed (reflected) paths",
329 DoubleValue (0.0),
330 MakeDoubleAccessor (&ThreeGppChannelModel::m_vScatt),
331 MakeDoubleChecker<double> (0.0))
332
333 ;
334 return tid;
335}
336
337void
338ThreeGppChannelModel::SetChannelConditionModel (Ptr<ChannelConditionModel> model)
339{
340 NS_LOG_FUNCTION (this);
341 m_channelConditionModel = model;
342}
343
344Ptr<ChannelConditionModel>
345ThreeGppChannelModel::GetChannelConditionModel () const
346{
347 NS_LOG_FUNCTION (this);
348 return m_channelConditionModel;
349}
350
351void
352ThreeGppChannelModel::SetFrequency (double f)
353{
354 NS_LOG_FUNCTION (this);
355 NS_ASSERT_MSG (f >= 500.0e6 && f <= 100.0e9, "Frequency should be between 0.5 and 100 GHz but is " << f);
356 m_frequency = f;
357}
358
359double
360ThreeGppChannelModel::GetFrequency () const
361{
362 NS_LOG_FUNCTION (this);
363 return m_frequency;
364}
365
366void
367ThreeGppChannelModel::SetScenario (const std::string &scenario)
368{
369 NS_LOG_FUNCTION (this);
370 NS_ASSERT_MSG (scenario == "RMa" || scenario == "UMa" || scenario == "UMi-StreetCanyon"
371 || scenario == "InH-OfficeOpen" || scenario == "InH-OfficeMixed"
372 || scenario == "V2V-Urban" || scenario == "V2V-Highway",
373 "Unknown scenario, choose between: RMa, UMa, UMi-StreetCanyon, "
374 "InH-OfficeOpen, InH-OfficeMixed, V2V-Urban or V2V-Highway");
375 m_scenario = scenario;
376}
377
378std::string
379ThreeGppChannelModel::GetScenario () const
380{
381 NS_LOG_FUNCTION (this);
382 return m_scenario;
383}
384
385Ptr<const ThreeGppChannelModel::ParamsTable>
386ThreeGppChannelModel::GetThreeGppTable (Ptr<const ChannelCondition> channelCondition, double hBS,
387 double hUT, double distance2D) const
388{
389 NS_LOG_FUNCTION (this);
390
391 double fcGHz = m_frequency / 1e9;
392 Ptr<ParamsTable> table3gpp = Create<ParamsTable> ();
393 // table3gpp includes the following parameters:
394 // numOfCluster, raysPerCluster, uLgDS, sigLgDS, uLgASD, sigLgASD,
395 // uLgASA, sigLgASA, uLgZSA, sigLgZSA, uLgZSD, sigLgZSD, offsetZOD,
396 // cDS, cASD, cASA, cZSA, uK, sigK, rTau, uXpr, sigXpr, shadowingStd
397
398 bool los = channelCondition->IsLos ();
399 bool o2i = channelCondition->IsO2i ();
400
401 // In NLOS case, parameter uK and sigK are not used and they are set to 0
402 if (m_scenario == "RMa")
403 {
404 if (los && !o2i)
405 {
406 // 3GPP mentioned that 3.91 ns should be used when the Cluster DS (cDS)
407 // entry is N/A.
408 table3gpp->m_numOfCluster = 11;
409 table3gpp->m_raysPerCluster = 20;
410 table3gpp->m_uLgDS = -7.49;
411 table3gpp->m_sigLgDS = 0.55;
412 table3gpp->m_uLgASD = 0.90;
413 table3gpp->m_sigLgASD = 0.38;
414 table3gpp->m_uLgASA = 1.52;
415 table3gpp->m_sigLgASA = 0.24;
416 table3gpp->m_uLgZSA = 0.47;
417 table3gpp->m_sigLgZSA = 0.40;
418 table3gpp->m_uLgZSD = 0.34;
419 table3gpp->m_sigLgZSD = std::max (-1.0, -0.17 * (distance2D / 1000) - 0.01 * (hUT - 1.5) + 0.22);
420 table3gpp->m_offsetZOD = 0;
421 table3gpp->m_cDS = 3.91e-9;
422 table3gpp->m_cASD = 2;
423 table3gpp->m_cASA = 3;
424 table3gpp->m_cZSA = 3;
425 table3gpp->m_uK = 7;
426 table3gpp->m_sigK = 4;
427 table3gpp->m_rTau = 3.8;
428 table3gpp->m_uXpr = 12;
429 table3gpp->m_sigXpr = 4;
430 table3gpp->m_perClusterShadowingStd = 3;
431
432 for (uint8_t row = 0; row < 7; row++)
433 {
434 for (uint8_t column = 0; column < 7; column++)
435 {
436 table3gpp->m_sqrtC[row][column] = sqrtC_RMa_LOS[row][column];
437 }
438 }
439 }
440 else if (!los && !o2i)
441 {
442 table3gpp->m_numOfCluster = 10;
443 table3gpp->m_raysPerCluster = 20;
444 table3gpp->m_uLgDS = -7.43;
445 table3gpp->m_sigLgDS = 0.48;
446 table3gpp->m_uLgASD = 0.95;
447 table3gpp->m_sigLgASD = 0.45;
448 table3gpp->m_uLgASA = 1.52;
449 table3gpp->m_sigLgASA = 0.13;
450 table3gpp->m_uLgZSA = 0.58;
451 table3gpp->m_sigLgZSA = 0.37;
452 table3gpp->m_uLgZSD = std::max (-1.0, -0.19 * (distance2D / 1000) - 0.01 * (hUT - 1.5) + 0.28);
453 table3gpp->m_sigLgZSD = 0.30;
454 table3gpp->m_offsetZOD = atan ((35 - 3.5) / distance2D) - atan ((35 - 1.5) / distance2D);
455 table3gpp->m_cDS = 3.91e-9;
456 table3gpp->m_cASD = 2;
457 table3gpp->m_cASA = 3;
458 table3gpp->m_cZSA = 3;
459 table3gpp->m_uK = 0;
460 table3gpp->m_sigK = 0;
461 table3gpp->m_rTau = 1.7;
462 table3gpp->m_uXpr = 7;
463 table3gpp->m_sigXpr = 3;
464 table3gpp->m_perClusterShadowingStd = 3;
465
466 for (uint8_t row = 0; row < 6; row++)
467 {
468 for (uint8_t column = 0; column < 6; column++)
469 {
470 table3gpp->m_sqrtC[row][column] = sqrtC_RMa_NLOS[row][column];
471 }
472 }
473 }
474 else // o2i
475 {
476 table3gpp->m_numOfCluster = 10;
477 table3gpp->m_raysPerCluster = 20;
478 table3gpp->m_uLgDS = -7.47;
479 table3gpp->m_sigLgDS = 0.24;
480 table3gpp->m_uLgASD = 0.67;
481 table3gpp->m_sigLgASD = 0.18;
482 table3gpp->m_uLgASA = 1.66;
483 table3gpp->m_sigLgASA = 0.21;
484 table3gpp->m_uLgZSA = 0.93;
485 table3gpp->m_sigLgZSA = 0.22;
486 table3gpp->m_uLgZSD = std::max (-1.0, -0.19 * (distance2D / 1000) - 0.01 * (hUT - 1.5) + 0.28);
487 table3gpp->m_sigLgZSD = 0.30;
488 table3gpp->m_offsetZOD = atan ((35 - 3.5) / distance2D) - atan ((35 - 1.5) / distance2D);
489 table3gpp->m_cDS = 3.91e-9;
490 table3gpp->m_cASD = 2;
491 table3gpp->m_cASA = 3;
492 table3gpp->m_cZSA = 3;
493 table3gpp->m_uK = 0;
494 table3gpp->m_sigK = 0;
495 table3gpp->m_rTau = 1.7;
496 table3gpp->m_uXpr = 7;
497 table3gpp->m_sigXpr = 3;
498 table3gpp->m_perClusterShadowingStd = 3;
499
500 for (uint8_t row = 0; row < 6; row++)
501 {
502 for (uint8_t column = 0; column < 6; column++)
503 {
504 table3gpp->m_sqrtC[row][column] = sqrtC_RMa_O2I[row][column];
505 }
506 }
507 }
508 }
509 else if (m_scenario == "UMa")
510 {
511 if (los && !o2i)
512 {
513 table3gpp->m_numOfCluster = 12;
514 table3gpp->m_raysPerCluster = 20;
515 table3gpp->m_uLgDS = -6.955 - 0.0963 * log10 (fcGHz);
516 table3gpp->m_sigLgDS = 0.66;
517 table3gpp->m_uLgASD = 1.06 + 0.1114 * log10 (fcGHz);
518 table3gpp->m_sigLgASD = 0.28;
519 table3gpp->m_uLgASA = 1.81;
520 table3gpp->m_sigLgASA = 0.20;
521 table3gpp->m_uLgZSA = 0.95;
522 table3gpp->m_sigLgZSA = 0.16;
523 table3gpp->m_uLgZSD = std::max (-0.5, -2.1 * distance2D / 1000 - 0.01 * (hUT - 1.5) + 0.75);
524 table3gpp->m_sigLgZSD = 0.40;
525 table3gpp->m_offsetZOD = 0;
526 table3gpp->m_cDS = std::max (0.25, -3.4084 * log10 (fcGHz) + 6.5622) * 1e-9;
527 table3gpp->m_cASD = 5;
528 table3gpp->m_cASA = 11;
529 table3gpp->m_cZSA = 7;
530 table3gpp->m_uK = 9;
531 table3gpp->m_sigK = 3.5;
532 table3gpp->m_rTau = 2.5;
533 table3gpp->m_uXpr = 8;
534 table3gpp->m_sigXpr = 4;
535 table3gpp->m_perClusterShadowingStd = 3;
536
537 for (uint8_t row = 0; row < 7; row++)
538 {
539 for (uint8_t column = 0; column < 7; column++)
540 {
541 table3gpp->m_sqrtC[row][column] = sqrtC_UMa_LOS[row][column];
542 }
543 }
544 }
545 else
546 {
547 double uLgZSD = std::max (-0.5, -2.1 * distance2D / 1000 - 0.01 * (hUT - 1.5) + 0.9);
548
549 double afc = 0.208 * log10 (fcGHz) - 0.782;
550 double bfc = 25;
551 double cfc = -0.13 * log10 (fcGHz) + 2.03;
552 double efc = 7.66 * log10 (fcGHz) - 5.96;
553
554 double offsetZOD = efc - std::pow (10, afc * log10 (std::max (bfc, distance2D)) + cfc);
555
556 if (!los && !o2i)
557 {
558 table3gpp->m_numOfCluster = 20;
559 table3gpp->m_raysPerCluster = 20;
560 table3gpp->m_uLgDS = -6.28 - 0.204 * log10 (fcGHz);
561 table3gpp->m_sigLgDS = 0.39;
562 table3gpp->m_uLgASD = 1.5 - 0.1144 * log10 (fcGHz);
563 table3gpp->m_sigLgASD = 0.28;
564 table3gpp->m_uLgASA = 2.08 - 0.27 * log10 (fcGHz);
565 table3gpp->m_sigLgASA = 0.11;
566 table3gpp->m_uLgZSA = -0.3236 * log10 (fcGHz) + 1.512;
567 table3gpp->m_sigLgZSA = 0.16;
568 table3gpp->m_uLgZSD = uLgZSD;
569 table3gpp->m_sigLgZSD = 0.49;
570 table3gpp->m_offsetZOD = offsetZOD;
571 table3gpp->m_cDS = std::max (0.25, -3.4084 * log10 (fcGHz) + 6.5622) * 1e-9;
572 table3gpp->m_cASD = 2;
573 table3gpp->m_cASA = 15;
574 table3gpp->m_cZSA = 7;
575 table3gpp->m_uK = 0;
576 table3gpp->m_sigK = 0;
577 table3gpp->m_rTau = 2.3;
578 table3gpp->m_uXpr = 7;
579 table3gpp->m_sigXpr = 3;
580 table3gpp->m_perClusterShadowingStd = 3;
581
582 for (uint8_t row = 0; row < 6; row++)
583 {
584 for (uint8_t column = 0; column < 6; column++)
585 {
586 table3gpp->m_sqrtC[row][column] = sqrtC_UMa_NLOS[row][column];
587 }
588 }
589 }
590 else //(o2i)
591 {
592 table3gpp->m_numOfCluster = 12;
593 table3gpp->m_raysPerCluster = 20;
594 table3gpp->m_uLgDS = -6.62;
595 table3gpp->m_sigLgDS = 0.32;
596 table3gpp->m_uLgASD = 1.25;
597 table3gpp->m_sigLgASD = 0.42;
598 table3gpp->m_uLgASA = 1.76;
599 table3gpp->m_sigLgASA = 0.16;
600 table3gpp->m_uLgZSA = 1.01;
601 table3gpp->m_sigLgZSA = 0.43;
602 table3gpp->m_uLgZSD = uLgZSD;
603 table3gpp->m_sigLgZSD = 0.49;
604 table3gpp->m_offsetZOD = offsetZOD;
605 table3gpp->m_cDS = 11e-9;
606 table3gpp->m_cASD = 5;
607 table3gpp->m_cASA = 8;
608 table3gpp->m_cZSA = 3;
609 table3gpp->m_uK = 0;
610 table3gpp->m_sigK = 0;
611 table3gpp->m_rTau = 2.2;
612 table3gpp->m_uXpr = 9;
613 table3gpp->m_sigXpr = 5;
614 table3gpp->m_perClusterShadowingStd = 4;
615
616 for (uint8_t row = 0; row < 6; row++)
617 {
618 for (uint8_t column = 0; column < 6; column++)
619 {
620 table3gpp->m_sqrtC[row][column] = sqrtC_UMa_O2I[row][column];
621 }
622 }
623
624 }
625
626 }
627
628 }
629 else if (m_scenario == "UMi-StreetCanyon")
630 {
631 if (los && !o2i)
632 {
633 table3gpp->m_numOfCluster = 12;
634 table3gpp->m_raysPerCluster = 20;
635 table3gpp->m_uLgDS = -0.24 * log10 (1 + fcGHz) - 7.14;
636 table3gpp->m_sigLgDS = 0.38;
637 table3gpp->m_uLgASD = -0.05 * log10 (1 + fcGHz) + 1.21;
638 table3gpp->m_sigLgASD = 0.41;
639 table3gpp->m_uLgASA = -0.08 * log10 (1 + fcGHz) + 1.73;
640 table3gpp->m_sigLgASA = 0.014 * log10 (1 + fcGHz) + 0.28;
641 table3gpp->m_uLgZSA = -0.1 * log10 (1 + fcGHz) + 0.73;
642 table3gpp->m_sigLgZSA = -0.04 * log10 (1 + fcGHz) + 0.34;
643 table3gpp->m_uLgZSD = std::max (-0.21, -14.8 * distance2D / 1000 + 0.01 * std::abs (hUT - hBS) + 0.83);
644 table3gpp->m_sigLgZSD = 0.35;
645 table3gpp->m_offsetZOD = 0;
646 table3gpp->m_cDS = 5e-9;
647 table3gpp->m_cASD = 3;
648 table3gpp->m_cASA = 17;
649 table3gpp->m_cZSA = 7;
650 table3gpp->m_uK = 9;
651 table3gpp->m_sigK = 5;
652 table3gpp->m_rTau = 3;
653 table3gpp->m_uXpr = 9;
654 table3gpp->m_sigXpr = 3;
655 table3gpp->m_perClusterShadowingStd = 3;
656
657 for (uint8_t row = 0; row < 7; row++)
658 {
659 for (uint8_t column = 0; column < 7; column++)
660 {
661 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_LOS[row][column];
662 }
663 }
664 }
665 else
666 {
667 double uLgZSD = std::max (-0.5, -3.1 * distance2D / 1000 + 0.01 * std::max (hUT - hBS, 0.0) + 0.2);
668 double offsetZOD = -1 * std::pow (10, -1.5 * log10 (std::max (10.0, distance2D)) + 3.3);
669 if (!los && !o2i)
670 {
671 table3gpp->m_numOfCluster = 19;
672 table3gpp->m_raysPerCluster = 20;
673 table3gpp->m_uLgDS = -0.24 * log10 (1 + fcGHz) - 6.83;
674 table3gpp->m_sigLgDS = 0.16 * log10 (1 + fcGHz) + 0.28;
675 table3gpp->m_uLgASD = -0.23 * log10 (1 + fcGHz) + 1.53;
676 table3gpp->m_sigLgASD = 0.11 * log10 (1 + fcGHz) + 0.33;
677 table3gpp->m_uLgASA = -0.08 * log10 (1 + fcGHz) + 1.81;
678 table3gpp->m_sigLgASA = 0.05 * log10 (1 + fcGHz) + 0.3;
679 table3gpp->m_uLgZSA = -0.04 * log10 (1 + fcGHz) + 0.92;
680 table3gpp->m_sigLgZSA = -0.07 * log10 (1 + fcGHz) + 0.41;
681 table3gpp->m_uLgZSD = uLgZSD;
682 table3gpp->m_sigLgZSD = 0.35;
683 table3gpp->m_offsetZOD = offsetZOD;
684 table3gpp->m_cDS = 11e-9;
685 table3gpp->m_cASD = 10;
686 table3gpp->m_cASA = 22;
687 table3gpp->m_cZSA = 7;
688 table3gpp->m_uK = 0;
689 table3gpp->m_sigK = 0;
690 table3gpp->m_rTau = 2.1;
691 table3gpp->m_uXpr = 8;
692 table3gpp->m_sigXpr = 3;
693 table3gpp->m_perClusterShadowingStd = 3;
694
695 for (uint8_t row = 0; row < 6; row++)
696 {
697 for (uint8_t column = 0; column < 6; column++)
698 {
699 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_NLOS[row][column];
700 }
701 }
702 }
703 else //(o2i)
704 {
705 table3gpp->m_numOfCluster = 12;
706 table3gpp->m_raysPerCluster = 20;
707 table3gpp->m_uLgDS = -6.62;
708 table3gpp->m_sigLgDS = 0.32;
709 table3gpp->m_uLgASD = 1.25;
710 table3gpp->m_sigLgASD = 0.42;
711 table3gpp->m_uLgASA = 1.76;
712 table3gpp->m_sigLgASA = 0.16;
713 table3gpp->m_uLgZSA = 1.01;
714 table3gpp->m_sigLgZSA = 0.43;
715 table3gpp->m_uLgZSD = uLgZSD;
716 table3gpp->m_sigLgZSD = 0.35;
717 table3gpp->m_offsetZOD = offsetZOD;
718 table3gpp->m_cDS = 11e-9;
719 table3gpp->m_cASD = 5;
720 table3gpp->m_cASA = 8;
721 table3gpp->m_cZSA = 3;
722 table3gpp->m_uK = 0;
723 table3gpp->m_sigK = 0;
724 table3gpp->m_rTau = 2.2;
725 table3gpp->m_uXpr = 9;
726 table3gpp->m_sigXpr = 5;
727 table3gpp->m_perClusterShadowingStd = 4;
728
729 for (uint8_t row = 0; row < 6; row++)
730 {
731 for (uint8_t column = 0; column < 6; column++)
732 {
733 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_O2I[row][column];
734 }
735 }
736 }
737 }
738 }
739 else if (m_scenario == "InH-OfficeMixed" || m_scenario == "InH-OfficeOpen")
740 {
741 NS_ASSERT_MSG (!o2i, "The indoor scenario does out support outdoor to indoor");
742 if (los)
743 {
744 table3gpp->m_numOfCluster = 15;
745 table3gpp->m_raysPerCluster = 20;
746 table3gpp->m_uLgDS = -0.01 * log10 (1 + fcGHz) - 7.692;
747 table3gpp->m_sigLgDS = 0.18;
748 table3gpp->m_uLgASD = 1.60;
749 table3gpp->m_sigLgASD = 0.18;
750 table3gpp->m_uLgASA = -0.19 * log10 (1 + fcGHz) + 1.781;
751 table3gpp->m_sigLgASA = 0.12 * log10 (1 + fcGHz) + 0.119;
752 table3gpp->m_uLgZSA = -0.26 * log10 (1 + fcGHz) + 1.44;
753 table3gpp->m_sigLgZSA = -0.04 * log10 (1 + fcGHz) + 0.264;
754 table3gpp->m_uLgZSD = -1.43 * log10 (1 + fcGHz) + 2.228;
755 table3gpp->m_sigLgZSD = 0.13 * log10 (1 + fcGHz) + 0.30;
756 table3gpp->m_offsetZOD = 0;
757 table3gpp->m_cDS = 3.91e-9;
758 table3gpp->m_cASD = 5;
759 table3gpp->m_cASA = 8;
760 table3gpp->m_cZSA = 9;
761 table3gpp->m_uK = 7;
762 table3gpp->m_sigK = 4;
763 table3gpp->m_rTau = 3.6;
764 table3gpp->m_uXpr = 11;
765 table3gpp->m_sigXpr = 4;
766 table3gpp->m_perClusterShadowingStd = 6;
767
768 for (uint8_t row = 0; row < 7; row++)
769 {
770 for (uint8_t column = 0; column < 7; column++)
771 {
772 table3gpp->m_sqrtC[row][column] = sqrtC_office_LOS[row][column];
773 }
774 }
775 }
776 else
777 {
778 table3gpp->m_numOfCluster = 19;
779 table3gpp->m_raysPerCluster = 20;
780 table3gpp->m_uLgDS = -0.28 * log10 (1 + fcGHz) - 7.173;
781 table3gpp->m_sigLgDS = 0.1 * log10 (1 + fcGHz) + 0.055;
782 table3gpp->m_uLgASD = 1.62;
783 table3gpp->m_sigLgASD = 0.25;
784 table3gpp->m_uLgASA = -0.11 * log10 (1 + fcGHz) + 1.863;
785 table3gpp->m_sigLgASA = 0.12 * log10 (1 + fcGHz) + 0.059;
786 table3gpp->m_uLgZSA = -0.15 * log10 (1 + fcGHz) + 1.387;
787 table3gpp->m_sigLgZSA = -0.09 * log10 (1 + fcGHz) + 0.746;
788 table3gpp->m_uLgZSD = 1.08;
789 table3gpp->m_sigLgZSD = 0.36;
790 table3gpp->m_offsetZOD = 0;
791 table3gpp->m_cDS = 3.91e-9;
792 table3gpp->m_cASD = 5;
793 table3gpp->m_cASA = 11;
794 table3gpp->m_cZSA = 9;
795 table3gpp->m_uK = 0;
796 table3gpp->m_sigK = 0;
797 table3gpp->m_rTau = 3;
798 table3gpp->m_uXpr = 10;
799 table3gpp->m_sigXpr = 4;
800 table3gpp->m_perClusterShadowingStd = 3;
801
802 for (uint8_t row = 0; row < 6; row++)
803 {
804 for (uint8_t column = 0; column < 6; column++)
805 {
806 table3gpp->m_sqrtC[row][column] = sqrtC_office_NLOS[row][column];
807 }
808 }
809 }
810 }
811 else if (m_scenario == "V2V-Urban")
812 {
813 if (channelCondition->IsLos ())
814 {
815 // 3GPP mentioned that 3.91 ns should be used when the Cluster DS (cDS)
816 // entry is N/A.
817 table3gpp->m_numOfCluster = 12;
818 table3gpp->m_raysPerCluster = 20;
819 table3gpp->m_uLgDS = -0.2 * log10 (1 + fcGHz) - 7.5;
820 table3gpp->m_sigLgDS = 0.1;
821 table3gpp->m_uLgASD = -0.1 * log10 (1 + fcGHz) + 1.6;
822 table3gpp->m_sigLgASD = 0.1;
823 table3gpp->m_uLgASA = -0.1 * log10 (1 + fcGHz) + 1.6;
824 table3gpp->m_sigLgASA = 0.1;
825 table3gpp->m_uLgZSA = -0.1 * log10 (1 + fcGHz) + 0.73;
826 table3gpp->m_sigLgZSA = -0.04 * log10 (1 + fcGHz) + 0.34;
827 table3gpp->m_uLgZSD = -0.1 * log10 (1 + fcGHz) + 0.73;
828 table3gpp->m_sigLgZSD = -0.04 * log10 (1 + fcGHz) + 0.34;
829 table3gpp->m_offsetZOD = 0;
830 table3gpp->m_cDS = 5;
831 table3gpp->m_cASD = 17;
832 table3gpp->m_cASA = 17;
833 table3gpp->m_cZSA = 7;
834 table3gpp->m_uK = 3.48;
835 table3gpp->m_sigK = 2;
836 table3gpp->m_rTau = 3;
837 table3gpp->m_uXpr = 9;
838 table3gpp->m_sigXpr = 3;
839 table3gpp->m_perClusterShadowingStd = 4;
840
841 for (uint8_t row = 0; row < 7; row++)
842 {
843 for (uint8_t column = 0; column < 7; column++)
844 {
845 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_LOS[row][column];
846 }
847 }
848 }
849 else if (channelCondition->IsNlos ())
850 {
851 table3gpp->m_numOfCluster = 19;
852 table3gpp->m_raysPerCluster = 20;
853 table3gpp->m_uLgDS = -0.3 * log10 (1 + fcGHz) - 7;
854 table3gpp->m_sigLgDS = 0.28;
855 table3gpp->m_uLgASD = -0.08 * log10 (1 + fcGHz) + 1.81;
856 table3gpp->m_sigLgASD = 0.05 * log10 (1 + fcGHz) + 0.3;
857 table3gpp->m_uLgASA = -0.08 * log10 (1 + fcGHz) + 1.81;
858 table3gpp->m_sigLgASA = 0.05 * log10 (1 + fcGHz) + 0.3;
859 table3gpp->m_uLgZSA = -0.04 * log10 (1 + fcGHz) + 0.92;
860 table3gpp->m_sigLgZSA = -0.07 * log10 (1 + fcGHz) + 0.41;
861 table3gpp->m_uLgZSD = -0.04 * log10 (1 + fcGHz) + 0.92;
862 table3gpp->m_sigLgZSD = -0.07 * log10 (1 + fcGHz) + 0.41;
863 table3gpp->m_offsetZOD = 0;
864 table3gpp->m_cDS = 11;
865 table3gpp->m_cASD = 22;
866 table3gpp->m_cASA = 22;
867 table3gpp->m_cZSA = 7;
868 table3gpp->m_uK = 0; // N/A
869 table3gpp->m_sigK = 0; // N/A
870 table3gpp->m_rTau = 2.1;
871 table3gpp->m_uXpr = 8;
872 table3gpp->m_sigXpr = 3;
873 table3gpp->m_perClusterShadowingStd = 4;
874
875 for (uint8_t row = 0; row < 6; row++)
876 {
877 for (uint8_t column = 0; column < 6; column++)
878 {
879 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_NLOS[row][column];
880 }
881 }
882 }
883 else if (channelCondition->IsNlosv ())
884 {
885 table3gpp->m_numOfCluster = 19;
886 table3gpp->m_raysPerCluster = 20;
887 table3gpp->m_uLgDS = -0.4 * log10 (1 + fcGHz) - 7;
888 table3gpp->m_sigLgDS = 0.1;
889 table3gpp->m_uLgASD = -0.1 * log10 (1 + fcGHz) + 1.7;
890 table3gpp->m_sigLgASD = 0.1;
891 table3gpp->m_uLgASA = -0.1 * log10 (1 + fcGHz) + 1.7;
892 table3gpp->m_sigLgASA = 0.1;
893 table3gpp->m_uLgZSA = -0.04 * log10 (1 + fcGHz) + 0.92;
894 table3gpp->m_sigLgZSA = -0.07 * log10 (1 + fcGHz) + 0.41;
895 table3gpp->m_uLgZSD = -0.04 * log10 (1 + fcGHz) + 0.92;
896 table3gpp->m_sigLgZSD = -0.07 * log10 (1 + fcGHz) + 0.41;
897 table3gpp->m_offsetZOD = 0;
898 table3gpp->m_cDS = 11;
899 table3gpp->m_cASD = 22;
900 table3gpp->m_cASA = 22;
901 table3gpp->m_cZSA = 7;
902 table3gpp->m_uK = 0;
903 table3gpp->m_sigK = 4.5;
904 table3gpp->m_rTau = 2.1;
905 table3gpp->m_uXpr = 8;
906 table3gpp->m_sigXpr = 3;
907 table3gpp->m_perClusterShadowingStd = 4;
908
909 for (uint8_t row = 0; row < 6; row++)
910 {
911 for (uint8_t column = 0; column < 6; column++)
912 {
913 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_LOS[row][column];
914 }
915 }
916 }
917 else
918 {
919 NS_FATAL_ERROR ("Unknown channel condition");
920 }
921 }
922 else if (m_scenario == "V2V-Highway")
923 {
924 if (channelCondition->IsLos ())
925 {
926 table3gpp->m_numOfCluster = 12;
927 table3gpp->m_raysPerCluster = 20;
928 table3gpp->m_uLgDS = -8.3;
929 table3gpp->m_sigLgDS = 0.2;
930 table3gpp->m_uLgASD = 1.4;
931 table3gpp->m_sigLgASD = 0.1;
932 table3gpp->m_uLgASA = 1.4;
933 table3gpp->m_sigLgASA = 0.1;
934 table3gpp->m_uLgZSA = -0.1 * log10 (1 + fcGHz) + 0.73;
935 table3gpp->m_sigLgZSA = -0.04 * log10 (1 + fcGHz) + 0.34;
936 table3gpp->m_uLgZSD = -0.1 * log10 (1 + fcGHz) + 0.73;
937 table3gpp->m_sigLgZSD = -0.04 * log10 (1 + fcGHz) + 0.34;
938 table3gpp->m_offsetZOD = 0;
939 table3gpp->m_cDS = 5;
940 table3gpp->m_cASD = 17;
941 table3gpp->m_cASA = 17;
942 table3gpp->m_cZSA = 7;
943 table3gpp->m_uK = 9;
944 table3gpp->m_sigK = 3.5;
945 table3gpp->m_rTau = 3;
946 table3gpp->m_uXpr = 9;
947 table3gpp->m_sigXpr = 3;
948 table3gpp->m_perClusterShadowingStd = 4;
949
950 for (uint8_t row = 0; row < 7; row++)
951 {
952 for (uint8_t column = 0; column < 7; column++)
953 {
954 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_LOS[row][column];
955 }
956 }
957 }
958 else if (channelCondition->IsNlosv ())
959 {
960 table3gpp->m_numOfCluster = 19;
961 table3gpp->m_raysPerCluster = 20;
962 table3gpp->m_uLgDS = -8.3;
963 table3gpp->m_sigLgDS = 0.3;
964 table3gpp->m_uLgASD = 1.5;
965 table3gpp->m_sigLgASD = 0.1;
966 table3gpp->m_uLgASA = 1.5;
967 table3gpp->m_sigLgASA = 0.1;
968 table3gpp->m_uLgZSA = -0.04 * log10 (1 + fcGHz) + 0.92;
969 table3gpp->m_sigLgZSA = -0.07 * log10 (1 + fcGHz) + 0.41;
970 table3gpp->m_uLgZSD = -0.04 * log10 (1 + fcGHz) + 0.92;
971 table3gpp->m_sigLgZSD = -0.07 * log10 (1 + fcGHz) + 0.41;
972 table3gpp->m_offsetZOD = 0;
973 table3gpp->m_cDS = 11;
974 table3gpp->m_cASD = 22;
975 table3gpp->m_cASA = 22;
976 table3gpp->m_cZSA = 7;
977 table3gpp->m_uK = 0;
978 table3gpp->m_sigK = 4.5;
979 table3gpp->m_rTau = 2.1;
980 table3gpp->m_uXpr = 8.0;
981 table3gpp->m_sigXpr = 3;
982 table3gpp->m_perClusterShadowingStd = 4;
983
984 for (uint8_t row = 0; row < 6; row++)
985 {
986 for (uint8_t column = 0; column < 6; column++)
987 {
988 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_LOS[row][column];
989 }
990 }
991 }
992 else if (channelCondition->IsNlos ())
993 {
994 NS_LOG_WARN ("The fast fading parameters for the NLOS condition in the Highway scenario are not defined in TR 37.885, use the ones defined in TDoc R1-1803671 instead");
995
996 table3gpp->m_numOfCluster = 19;
997 table3gpp->m_raysPerCluster = 20;
998 table3gpp->m_uLgDS = -0.3 * log10 (1 + fcGHz) - 7;
999 table3gpp->m_sigLgDS = 0.28;
1000 table3gpp->m_uLgASD = -0.08 * log10 (1 + fcGHz) + 1.81;
1001 table3gpp->m_sigLgASD = 0.05 * log10 (1 + fcGHz) + 0.3;
1002 table3gpp->m_uLgASA = -0.08 * log10 (1 + fcGHz) + 1.81;
1003 table3gpp->m_sigLgASA = 0.05 * log10 (1 + fcGHz) + 0.3;
1004 table3gpp->m_uLgZSA = -0.04 * log10 (1 + fcGHz) + 0.92;
1005 table3gpp->m_sigLgZSA = -0.07 * log10 (1 + fcGHz) + 0.41;
1006 table3gpp->m_uLgZSD = -0.04 * log10 (1 + fcGHz) + 0.92;
1007 table3gpp->m_sigLgZSD = -0.07 * log10 (1 + fcGHz) + 0.41;
1008 table3gpp->m_offsetZOD = 0;
1009 table3gpp->m_cDS = 11;
1010 table3gpp->m_cASD = 22;
1011 table3gpp->m_cASA = 22;
1012 table3gpp->m_cZSA = 7;
1013 table3gpp->m_uK = 0; // N/A
1014 table3gpp->m_sigK = 0; // N/A
1015 table3gpp->m_rTau = 2.1;
1016 table3gpp->m_uXpr = 8;
1017 table3gpp->m_sigXpr = 3;
1018 table3gpp->m_perClusterShadowingStd = 4;
1019
1020 for (uint8_t row = 0; row < 6; row++)
1021 {
1022 for (uint8_t column = 0; column < 6; column++)
1023 {
1024 table3gpp->m_sqrtC[row][column] = sqrtC_UMi_NLOS[row][column];
1025 }
1026 }
1027 }
1028 else
1029 {
1030 NS_FATAL_ERROR ("Unknown channel condition");
1031 }
1032 }
1033 else
1034 {
1035 NS_FATAL_ERROR ("unkonw scenarios");
1036 }
1037
1038 return table3gpp;
1039}
1040
1041bool
1042ThreeGppChannelModel::ChannelParamsNeedsUpdate (Ptr<const ThreeGppChannelParams> channelParams,
1043 Ptr<const ChannelCondition> channelCondition) const
1044{
1045 NS_LOG_FUNCTION (this);
1046
1047 bool update = false;
1048
1049 // if the channel condition is different the channel has to be updated
1050 if (!channelCondition->IsEqual (channelParams->m_losCondition, channelParams->m_o2iCondition))
1051 {
1052 NS_LOG_DEBUG ("Update the channel condition");
1053 update = true;
1054 }
1055
1056 // if the coherence time is over the channel has to be updated
1057 if (!m_updatePeriod.IsZero () && Simulator::Now () - channelParams->m_generatedTime > m_updatePeriod)
1058 {
1059 NS_LOG_DEBUG ("Generation time " << channelParams->m_generatedTime.As (Time::NS) << " now " << Now ().As (Time::NS));
1060 update = true;
1061 }
1062
1063 return update;
1064}
1065
1066
1067bool
1068ThreeGppChannelModel::ChannelMatrixNeedsUpdate (Ptr<const ThreeGppChannelParams> channelParams, Ptr<const ChannelMatrix> channelMatrix)
1069{
1070
1071 if (channelParams->m_generatedTime > channelMatrix->m_generatedTime)
1072 {
1073 return true;
1074 }
1075 else
1076 {
1077 return false;
1078 }
1079}
1080
1081Ptr<const MatrixBasedChannelModel::ChannelMatrix>
1082ThreeGppChannelModel::GetChannel (Ptr<const MobilityModel> aMob,
1083 Ptr<const MobilityModel> bMob,
1084 Ptr<const PhasedArrayModel> aAntenna,
1085 Ptr<const PhasedArrayModel> bAntenna)
1086{
1087 NS_LOG_FUNCTION (this);
1088
1089 // Compute the channel params key. The key is reciprocal, i.e., key (a, b) = key (b, a)
1090 uint64_t channelParamsKey = GetKey (aMob->GetObject<Node> ()->GetId (), bMob->GetObject<Node> ()->GetId ());
1091 // Compute the channel matrix key. The key is reciprocal, i.e., key (a, b) = key (b, a)
1092 uint64_t channelMatrixKey = GetKey (aAntenna->GetId (), bAntenna->GetId ());
1093
1094 // retrieve the channel condition
1095 Ptr<const ChannelCondition> condition = m_channelConditionModel->GetChannelCondition (aMob, bMob);
1096
1097 // Check if the channel is present in the map and return it, otherwise
1098 // generate a new channel
1099 bool updateParams = false;
1100 bool updateMatrix = false;
1101 bool notFoundParams = false;
1102 bool notFoundMatrix = false;
1103 Ptr<ChannelMatrix> channelMatrix;
1104 Ptr<ThreeGppChannelParams> channelParams;
1105
1106
1107 if (m_channelParamsMap.find (channelParamsKey) != m_channelParamsMap.end ())
1108 {
1109 channelParams = m_channelParamsMap[channelParamsKey];
1110 // check if it has to be updated
1111 updateParams = ChannelParamsNeedsUpdate (channelParams, condition);
1112 }
1113 else
1114 {
1115 NS_LOG_DEBUG ("channel params not found");
1116 notFoundParams = true;
1117 }
1118
1119 double x = aMob->GetPosition ().x - bMob->GetPosition ().x;
1120 double y = aMob->GetPosition ().y - bMob->GetPosition ().y;
1121 double distance2D = sqrt (x * x + y * y);
1122
1123 // NOTE we assume hUT = min (height(a), height(b)) and
1124 // hBS = max (height (a), height (b))
1125 double hUt = std::min (aMob->GetPosition ().z, bMob->GetPosition ().z);
1126 double hBs = std::max (aMob->GetPosition ().z, bMob->GetPosition ().z);
1127
1128 // get the 3GPP parameters
1129 Ptr<const ParamsTable> table3gpp = GetThreeGppTable (condition, hBs, hUt, distance2D);
1130
1131 if (notFoundParams || updateParams)
1132 {
1133 //Step 4: Generate large scale parameters. All LSPS are uncorrelated.
1134 //Step 5: Generate Delays.
1135 //Step 6: Generate cluster powers.
1136 //Step 7: Generate arrival and departure angles for both azimuth and elevation.
1137 //Step 8: Coupling of rays within a cluster for both azimuth and elevation
1138 //shuffle all the arrays to perform random coupling
1139 //Step 9: Generate the cross polarization power ratios
1140 //Step 10: Draw initial phases
1141 channelParams = GenerateChannelParameters (condition, table3gpp, aMob, bMob);
1142 // store or replace the channel parameters
1143 m_channelParamsMap[channelParamsKey] = channelParams;
1144 }
1145
1146 if (m_channelMatrixMap.find (channelMatrixKey) != m_channelMatrixMap.end ())
1147 {
1148 // channel matrix present in the map
1149 NS_LOG_DEBUG ("channel matrix present in the map");
1150 channelMatrix = m_channelMatrixMap[channelMatrixKey];
1151 updateMatrix = ChannelMatrixNeedsUpdate (channelParams, channelMatrix);
1152 }
1153 else
1154 {
1155 NS_LOG_DEBUG ("channel matrix not found");
1156 notFoundMatrix = true;
1157 }
1158
1159 // If the channel is not present in the map or if it has to be updated
1160 // generate a new realization
1161 if (notFoundMatrix || updateMatrix)
1162 {
1163 // channel matrix not found or has to be updated, generate a new one
1164 channelMatrix = GetNewChannel (channelParams, table3gpp, aMob, bMob, aAntenna, bAntenna);
1165 channelMatrix->m_antennaPair = std::make_pair (aAntenna->GetId (), bAntenna->GetId ()); // save antenna pair, with the exact order of s and u antennas at the moment of the channel generation
1166
1167 // store or replace the channel matrix in the channel map
1168 m_channelMatrixMap[channelMatrixKey] = channelMatrix;
1169 }
1170
1171 return channelMatrix;
1172}
1173
1174Ptr<const MatrixBasedChannelModel::ChannelParams>
1175ThreeGppChannelModel::GetParams (Ptr<const MobilityModel> aMob,
1176 Ptr<const MobilityModel> bMob) const
1177{
1178 NS_LOG_FUNCTION (this);
1179
1180 // Compute the channel key. The key is reciprocal, i.e., key (a, b) = key (b, a)
1181 uint64_t channelParamsKey = GetKey (aMob->GetObject<Node> ()->GetId (), bMob->GetObject<Node> ()->GetId ());
1182
1183 if (m_channelParamsMap.find (channelParamsKey) != m_channelParamsMap.end ())
1184 {
1185 return m_channelParamsMap.find (channelParamsKey)->second;
1186 }
1187 else
1188 {
1189 NS_LOG_WARN ("Channel params map not found. Returning a nullptr.");
1190 return nullptr;
1191 }
1192}
1193
1194
1195Ptr<ThreeGppChannelModel::ThreeGppChannelParams>
1196ThreeGppChannelModel::GenerateChannelParameters (const Ptr<const ChannelCondition> channelCondition,
1197 const Ptr<const ParamsTable> table3gpp,
1198 const Ptr<const MobilityModel> aMob,
1199 const Ptr<const MobilityModel> bMob) const
1200{
1201
1202 NS_LOG_FUNCTION (this);
1203 // create a channel matrix instance
1204 Ptr<ThreeGppChannelParams> channelParams = Create<ThreeGppChannelParams> ();
1205 channelParams->m_generatedTime = Simulator::Now ();
1206 channelParams->m_nodeIds = std::make_pair (aMob->GetObject<Node> ()->GetId (), bMob->GetObject<Node> ()->GetId ());
1207 channelParams->m_losCondition = channelCondition->GetLosCondition ();
1208 channelParams->m_o2iCondition = channelCondition->GetO2iCondition ();
1209
1210 //Step 4: Generate large scale parameters. All LSPS are uncorrelated.
1211 DoubleVector LSPsIndep, LSPs;
1212 uint8_t paramNum = 6;
1213 if (channelParams->m_losCondition == ChannelCondition::LOS)
1214 {
1215 paramNum = 7;
1216 }
1217
1218 //Generate paramNum independent LSPs.
1219 for (uint8_t iter = 0; iter < paramNum; iter++)
1220 {
1221 LSPsIndep.push_back (m_normalRv->GetValue ());
1222 }
1223 for (uint8_t row = 0; row < paramNum; row++)
1224 {
1225 double temp = 0;
1226 for (uint8_t column = 0; column < paramNum; column++)
1227 {
1228 temp += table3gpp->m_sqrtC[row][column] * LSPsIndep[column];
1229 }
1230 LSPs.push_back (temp);
1231 }
1232
1233 // NOTE the shadowing is generated in the propagation loss model
1234 double DS, ASD, ASA, ZSA, ZSD, kFactor = 0;
1235 if (channelParams->m_losCondition == ChannelCondition::LOS)
1236 {
1237 kFactor = LSPs[1] * table3gpp->m_sigK + table3gpp->m_uK;
1238 DS = pow (10, LSPs[2] * table3gpp->m_sigLgDS + table3gpp->m_uLgDS);
1239 ASD = pow (10, LSPs[3] * table3gpp->m_sigLgASD + table3gpp->m_uLgASD);
1240 ASA = pow (10, LSPs[4] * table3gpp->m_sigLgASA + table3gpp->m_uLgASA);
1241 ZSD = pow (10, LSPs[5] * table3gpp->m_sigLgZSD + table3gpp->m_uLgZSD);
1242 ZSA = pow (10, LSPs[6] * table3gpp->m_sigLgZSA + table3gpp->m_uLgZSA);
1243 }
1244 else
1245 {
1246 DS = pow (10, LSPs[1] * table3gpp->m_sigLgDS + table3gpp->m_uLgDS);
1247 ASD = pow (10, LSPs[2] * table3gpp->m_sigLgASD + table3gpp->m_uLgASD);
1248 ASA = pow (10, LSPs[3] * table3gpp->m_sigLgASA + table3gpp->m_uLgASA);
1249 ZSD = pow (10, LSPs[4] * table3gpp->m_sigLgZSD + table3gpp->m_uLgZSD);
1250 ZSA = pow (10, LSPs[5] * table3gpp->m_sigLgZSA + table3gpp->m_uLgZSA);
1251
1252 }
1253 ASD = std::min (ASD, 104.0);
1254 ASA = std::min (ASA, 104.0);
1255 ZSD = std::min (ZSD, 52.0);
1256 ZSA = std::min (ZSA, 52.0);
1257
1258 // save DS and K_factor parameters in the structure
1259 channelParams->m_DS = DS;
1260 channelParams->m_K_factor = kFactor;
1261
1262 NS_LOG_INFO ("K-factor=" << kFactor << ", DS=" << DS << ", ASD=" << ASD << ", ASA=" << ASA
1263 << ", ZSD=" << ZSD << ", ZSA=" << ZSA);
1264
1265 //Step 5: Generate Delays.
1266 DoubleVector clusterDelay;
1267 double minTau = 100.0;
1268 for (uint8_t cIndex = 0; cIndex < table3gpp->m_numOfCluster; cIndex++)
1269 {
1270 double tau = -1 * table3gpp->m_rTau * DS * log (m_uniformRv->GetValue (0, 1)); //(7.5-1)
1271 if (minTau > tau)
1272 {
1273 minTau = tau;
1274 }
1275 clusterDelay.push_back (tau);
1276 }
1277
1278 for (uint8_t cIndex = 0; cIndex < table3gpp->m_numOfCluster; cIndex++)
1279 {
1280 clusterDelay[cIndex] -= minTau;
1281 }
1282 std::sort (clusterDelay.begin (), clusterDelay.end ()); //(7.5-2)
1283
1284 /* since the scaled Los delays are not to be used in cluster power generation,
1285 * we will generate cluster power first and resume to compute Los cluster delay later.*/
1286
1287 //Step 6: Generate cluster powers.
1288 DoubleVector clusterPower;
1289 double powerSum = 0;
1290 for (uint8_t cIndex = 0; cIndex < table3gpp->m_numOfCluster; cIndex++)
1291 {
1292 double power = exp (-1 * clusterDelay[cIndex] * (table3gpp->m_rTau - 1) / table3gpp->m_rTau / DS) *
1293 pow (10, -1 * m_normalRv->GetValue () * table3gpp->m_perClusterShadowingStd / 10); //(7.5-5)
1294 powerSum += power;
1295 clusterPower.push_back (power);
1296 }
1297 channelParams->m_clusterPower = clusterPower;
1298
1299 double powerMax = 0;
1300
1301 for (uint8_t cIndex = 0; cIndex < table3gpp->m_numOfCluster; cIndex++)
1302 {
1303 channelParams->m_clusterPower[cIndex] = channelParams->m_clusterPower[cIndex] / powerSum; //(7.5-6)
1304 }
1305
1306 DoubleVector clusterPowerForAngles; // this power is only for equation (7.5-9) and (7.5-14), not for (7.5-22)
1307 if (channelParams->m_losCondition == ChannelCondition::LOS)
1308 {
1309 double kLinear = pow (10, kFactor / 10);
1310
1311 for (uint8_t cIndex = 0; cIndex < table3gpp->m_numOfCluster; cIndex++)
1312 {
1313 if (cIndex == 0)
1314 {
1315 clusterPowerForAngles.push_back (channelParams->m_clusterPower[cIndex] / (1 + kLinear) +
1316 kLinear / (1 + kLinear)); //(7.5-8)
1317 }
1318 else
1319 {
1320 clusterPowerForAngles.push_back (channelParams->m_clusterPower[cIndex] / (1 + kLinear)); //(7.5-8)
1321 }
1322 if (powerMax < clusterPowerForAngles[cIndex])
1323 {
1324 powerMax = clusterPowerForAngles[cIndex];
1325 }
1326 }
1327 }
1328 else
1329 {
1330 for (uint8_t cIndex = 0; cIndex < table3gpp->m_numOfCluster; cIndex++)
1331 {
1332 clusterPowerForAngles.push_back (channelParams->m_clusterPower[cIndex]); //(7.5-6)
1333 if (powerMax < clusterPowerForAngles[cIndex])
1334 {
1335 powerMax = clusterPowerForAngles[cIndex];
1336 }
1337 }
1338 }
1339
1340 //remove clusters with less than -25 dB power compared to the maxim cluster power;
1341 //double thresh = pow(10, -2.5);
1342 double thresh = 0.0032;
1343 for (uint8_t cIndex = table3gpp->m_numOfCluster; cIndex > 0; cIndex--)
1344 {
1345 if (clusterPowerForAngles[cIndex - 1] < thresh * powerMax )
1346 {
1347 clusterPowerForAngles.erase (clusterPowerForAngles.begin () + cIndex - 1);
1348 channelParams->m_clusterPower.erase (channelParams->m_clusterPower.begin () + cIndex - 1);
1349 clusterDelay.erase (clusterDelay.begin () + cIndex - 1);
1350 }
1351 }
1352
1353 NS_ASSERT (channelParams->m_clusterPower.size () < UINT8_MAX);
1354 channelParams->m_reducedClusterNumber = channelParams->m_clusterPower.size ();
1355 // Resume step 5 to compute the delay for LoS condition.
1356 if (channelParams->m_losCondition == ChannelCondition::LOS)
1357 {
1358 double cTau = 0.7705 - 0.0433 * kFactor + 2e-4 * pow (kFactor,2) + 17e-6 * pow (kFactor,3); //(7.5-3)
1359 for (uint8_t cIndex = 0; cIndex < channelParams->m_reducedClusterNumber; cIndex++)
1360 {
1361 clusterDelay[cIndex] = clusterDelay[cIndex] / cTau; //(7.5-4)
1362 }
1363 }
1364
1365 //Step 7: Generate arrival and departure angles for both azimuth and elevation.
1366
1367 double cNlos;
1368 //According to table 7.5-6, only cluster number equals to 8, 10, 11, 12, 19 and 20 is valid.
1369 //Not sure why the other cases are in Table 7.5-2.
1370 switch (table3gpp->m_numOfCluster) // Table 7.5-2
1371 {
1372 case 4:
1373 cNlos = 0.779;
1374 break;
1375 case 5:
1376 cNlos = 0.860;
1377 break;
1378 case 8:
1379 cNlos = 1.018;
1380 break;
1381 case 10:
1382 cNlos = 1.090;
1383 break;
1384 case 11:
1385 cNlos = 1.123;
1386 break;
1387 case 12:
1388 cNlos = 1.146;
1389 break;
1390 case 14:
1391 cNlos = 1.190;
1392 break;
1393 case 15:
1394 cNlos = 1.221;
1395 break;
1396 case 16:
1397 cNlos = 1.226;
1398 break;
1399 case 19:
1400 cNlos = 1.273;
1401 break;
1402 case 20:
1403 cNlos = 1.289;
1404 break;
1405 default:
1406 NS_FATAL_ERROR ("Invalid cluster number");
1407 }
1408
1409 double cPhi = cNlos;
1410
1411 if (channelParams->m_losCondition == ChannelCondition::LOS)
1412 {
1413 cPhi *= (1.1035 - 0.028 * kFactor - 2e-3 * pow (kFactor, 2) + 1e-4 * pow (kFactor, 3)); //(7.5-10))
1414 }
1415
1416 switch (table3gpp->m_numOfCluster) //Table 7.5-4
1417 {
1418 case 8:
1419 cNlos = 0.889;
1420 break;
1421 case 10:
1422 cNlos = 0.957;
1423 break;
1424 case 11:
1425 cNlos = 1.031;
1426 break;
1427 case 12:
1428 cNlos = 1.104;
1429 break;
1430 case 15:
1431 cNlos = 1.1088;
1432 break;
1433 case 19:
1434 cNlos = 1.184;
1435 break;
1436 case 20:
1437 cNlos = 1.178;
1438 break;
1439 default:
1440 NS_FATAL_ERROR ("Invalid cluster number");
1441 }
1442
1443 double cTheta = cNlos;
1444 if (channelCondition->IsLos())
1445 {
1446 cTheta *= (1.3086 + 0.0339 * kFactor - 0.0077 * pow (kFactor, 2) + 2e-4 * pow (kFactor, 3)); //(7.5-15)
1447 }
1448
1449 DoubleVector clusterAoa, clusterAod, clusterZoa, clusterZod;
1450 for (uint8_t cIndex = 0; cIndex < channelParams->m_reducedClusterNumber; cIndex++)
1451 {
1452 double logCalc = -1 * log (clusterPowerForAngles[cIndex] / powerMax);
1453 double angle = 2 * sqrt (logCalc) / 1.4 / cPhi; //(7.5-9)
1454 clusterAoa.push_back (ASA * angle);
1455 clusterAod.push_back (ASD * angle);
1456 angle = logCalc / cTheta; //(7.5-14)
1457 clusterZoa.push_back (ZSA * angle);
1458 clusterZod.push_back (ZSD * angle);
1459 }
1460
1461 Angles sAngle (bMob->GetPosition (), aMob->GetPosition ());
1462 Angles uAngle (aMob->GetPosition (), bMob->GetPosition ());
1463
1464 for (uint8_t cIndex = 0; cIndex < channelParams->m_reducedClusterNumber; cIndex++)
1465 {
1466 int Xn = 1;
1467 if (m_uniformRv->GetValue (0, 1) < 0.5)
1468 {
1469 Xn = -1;
1470 }
1471 clusterAoa[cIndex] = clusterAoa[cIndex] * Xn + (m_normalRv->GetValue () * ASA / 7) + RadiansToDegrees (uAngle.GetAzimuth ()); //(7.5-11)
1472 clusterAod[cIndex] = clusterAod[cIndex] * Xn + (m_normalRv->GetValue () * ASD / 7) + RadiansToDegrees (sAngle.GetAzimuth ());
1473 if (channelCondition->IsO2i ())
1474 {
1475 clusterZoa[cIndex] = clusterZoa[cIndex] * Xn + (m_normalRv->GetValue () * ZSA / 7) + 90; //(7.5-16)
1476 }
1477 else
1478 {
1479 clusterZoa[cIndex] = clusterZoa[cIndex] * Xn + (m_normalRv->GetValue () * ZSA / 7) + RadiansToDegrees (uAngle.GetInclination ()); //(7.5-16)
1480 }
1481 clusterZod[cIndex] = clusterZod[cIndex] * Xn + (m_normalRv->GetValue () * ZSD / 7) + RadiansToDegrees (sAngle.GetInclination ()) + table3gpp->m_offsetZOD; //(7.5-19)
1482 }
1483
1484 if (channelParams->m_losCondition == ChannelCondition::LOS)
1485 {
1486 // The 7.5-12 can be rewrite as Theta_n,ZOA = Theta_n,ZOA - (Theta_1,ZOA - Theta_LOS,ZOA) = Theta_n,ZOA - diffZOA,
1487 // Similar as AOD, ZSA and ZSD.
1488 double diffAoa = clusterAoa[0] - RadiansToDegrees (uAngle.GetAzimuth ());
1489 double diffAod = clusterAod[0] - RadiansToDegrees (sAngle.GetAzimuth ());
1490 double diffZsa = clusterZoa[0] - RadiansToDegrees (uAngle.GetInclination ());
1491 double diffZsd = clusterZod[0] - RadiansToDegrees (sAngle.GetInclination ());
1492
1493 for (uint8_t cIndex = 0; cIndex < channelParams->m_reducedClusterNumber; cIndex++)
1494 {
1495 clusterAoa[cIndex] -= diffAoa; //(7.5-12)
1496 clusterAod[cIndex] -= diffAod;
1497 clusterZoa[cIndex] -= diffZsa; //(7.5-17)
1498 clusterZod[cIndex] -= diffZsd;
1499 }
1500 }
1501
1502 double sizeTemp = clusterZoa.size ();
1503 for (uint8_t ind = 0; ind < 4; ind++)
1504 {
1505 DoubleVector angleDegree;
1506 switch (ind)
1507 {
1508 case 0:
1509 angleDegree = clusterAoa;
1510 break;
1511 case 1:
1512 angleDegree = clusterZoa;
1513 break;
1514 case 2:
1515 angleDegree = clusterAod;
1516 break;
1517 case 3:
1518 angleDegree = clusterZod;
1519 break;
1520 default:
1521 NS_FATAL_ERROR ("Programming Error");
1522 }
1523 for (uint8_t nIndex = 0; nIndex < sizeTemp; nIndex++)
1524 {
1525 while (angleDegree[nIndex] > 360)
1526 {
1527 angleDegree[nIndex] -= 360;
1528 }
1529
1530 while (angleDegree[nIndex] < 0)
1531 {
1532 angleDegree[nIndex] += 360;
1533 }
1534
1535 if (ind == 1 || ind == 3)
1536 {
1537 if (angleDegree[nIndex] > 180)
1538 {
1539 angleDegree[nIndex] = 360 - angleDegree[nIndex];
1540 }
1541 }
1542 }
1543 switch (ind)
1544 {
1545 case 0:
1546 clusterAoa = angleDegree;
1547 break;
1548 case 1:
1549 clusterZoa = angleDegree;
1550 break;
1551 case 2:
1552 clusterAod = angleDegree;
1553 break;
1554 case 3:
1555 clusterZod = angleDegree;
1556 break;
1557 default:
1558 NS_FATAL_ERROR ("Programming Error");
1559 }
1560 }
1561
1562 DoubleVector attenuationDb;
1563 if (m_blockage)
1564 {
1565 attenuationDb = CalcAttenuationOfBlockage (channelParams, clusterAoa, clusterZoa);
1566 for (uint8_t cInd = 0; cInd < channelParams->m_reducedClusterNumber; cInd++)
1567 {
1568 channelParams->m_clusterPower[cInd] = channelParams->m_clusterPower[cInd] / pow (10,attenuationDb[cInd] / 10);
1569 }
1570 }
1571 else
1572 {
1573 attenuationDb.push_back (0);
1574 }
1575
1576 // store attenuation
1577 channelParams->m_attenuation_dB = attenuationDb;
1578
1579 //Step 8: Coupling of rays within a cluster for both azimuth and elevation
1580 //shuffle all the arrays to perform random coupling
1581 MatrixBasedChannelModel::Double2DVector rayAoaRadian (channelParams->m_reducedClusterNumber, DoubleVector (table3gpp->m_raysPerCluster, 0)); //rayAoaRadian[n][m], where n is cluster index, m is ray index
1582 MatrixBasedChannelModel::Double2DVector rayAodRadian (channelParams->m_reducedClusterNumber, DoubleVector (table3gpp->m_raysPerCluster, 0)); //rayAodRadian[n][m], where n is cluster index, m is ray index
1583 MatrixBasedChannelModel::Double2DVector rayZoaRadian (channelParams->m_reducedClusterNumber, DoubleVector (table3gpp->m_raysPerCluster, 0)); //rayZoaRadian[n][m], where n is cluster index, m is ray index
1584 MatrixBasedChannelModel::Double2DVector rayZodRadian (channelParams->m_reducedClusterNumber, DoubleVector (table3gpp->m_raysPerCluster, 0)); //rayZodRadian[n][m], where n is cluster index, m is ray index
1585
1586 for (uint8_t nInd = 0; nInd < channelParams->m_reducedClusterNumber; nInd++)
1587 {
1588 for (uint8_t mInd = 0; mInd < table3gpp->m_raysPerCluster; mInd++)
1589 {
1590 double tempAoa = clusterAoa[nInd] + table3gpp->m_cASA * offSetAlpha[mInd]; //(7.5-13)
1591 double tempZoa = clusterZoa[nInd] + table3gpp->m_cZSA * offSetAlpha[mInd]; //(7.5-18)
1592 std::tie (rayAoaRadian[nInd][mInd], rayZoaRadian[nInd][mInd]) = WrapAngles (DegreesToRadians (tempAoa), DegreesToRadians (tempZoa));
1593
1594 double tempAod = clusterAod[nInd] + table3gpp->m_cASD * offSetAlpha[mInd]; //(7.5-13)
1595 double tempZod = clusterZod[nInd] + 0.375 * pow (10,table3gpp->m_uLgZSD) * offSetAlpha[mInd]; //(7.5-20)
1596 std::tie (rayAodRadian[nInd][mInd], rayZodRadian[nInd][mInd]) = WrapAngles (DegreesToRadians (tempAod), DegreesToRadians (tempZod));
1597 }
1598 }
1599
1600 for (uint8_t cIndex = 0; cIndex < channelParams->m_reducedClusterNumber; cIndex++)
1601 {
1602 Shuffle (&rayAodRadian[cIndex][0], &rayAodRadian[cIndex][table3gpp->m_raysPerCluster]);
1603 Shuffle (&rayAoaRadian[cIndex][0], &rayAoaRadian[cIndex][table3gpp->m_raysPerCluster]);
1604 Shuffle (&rayZodRadian[cIndex][0], &rayZodRadian[cIndex][table3gpp->m_raysPerCluster]);
1605 Shuffle (&rayZoaRadian[cIndex][0], &rayZoaRadian[cIndex][table3gpp->m_raysPerCluster]);
1606 }
1607
1608 // store values
1609 channelParams->m_rayAodRadian = rayAodRadian;
1610 channelParams->m_rayAoaRadian = rayAoaRadian;
1611 channelParams->m_rayZodRadian = rayZodRadian;
1612 channelParams->m_rayZoaRadian = rayZoaRadian;
1613
1614 //Step 9: Generate the cross polarization power ratios
1615 //Step 10: Draw initial phases
1616 Double2DVector crossPolarizationPowerRatios; // vector containing the cross polarization power ratios, as defined by 7.5-21
1617 Double3DVector clusterPhase; //rayAoaRadian[n][m], where n is cluster index, m is ray index
1618 for (uint8_t nInd = 0; nInd < channelParams->m_reducedClusterNumber; nInd++)
1619 {
1620 DoubleVector temp; // used to store the XPR values
1621 Double2DVector temp2; // used to store the PHI values for all the possible combination of polarization
1622 for (uint8_t mInd = 0; mInd < table3gpp->m_raysPerCluster; mInd++)
1623 {
1624 double uXprLinear = pow (10, table3gpp->m_uXpr / 10); // convert to linear
1625 double sigXprLinear = pow (10, table3gpp->m_sigXpr / 10); // convert to linear
1626
1627 temp.push_back (std::pow (10, (m_normalRv->GetValue () * sigXprLinear + uXprLinear) / 10));
1628 DoubleVector temp3; // used to store the PHI valuse
1629 for (uint8_t pInd = 0; pInd < 4; pInd++)
1630 {
1631 temp3.push_back (m_uniformRv->GetValue (-1 * M_PI, M_PI));
1632 }
1633 temp2.push_back (temp3);
1634 }
1635 crossPolarizationPowerRatios.push_back (temp);
1636 clusterPhase.push_back (temp2);
1637 }
1638 // store the cluster phase
1639 channelParams->m_clusterPhase = clusterPhase;
1640 channelParams->m_crossPolarizationPowerRatios = crossPolarizationPowerRatios;
1641
1642 uint8_t cluster1st = 0, cluster2nd = 0; // first and second strongest cluster;
1643 double maxPower = 0;
1644 for (uint8_t cIndex = 0; cIndex < channelParams->m_reducedClusterNumber; cIndex++)
1645 {
1646 if (maxPower < channelParams->m_clusterPower[cIndex])
1647 {
1648 maxPower = channelParams->m_clusterPower[cIndex];
1649 cluster1st = cIndex;
1650 }
1651 }
1652 channelParams->m_cluster1st = cluster1st;
1653 maxPower = 0;
1654 for (uint8_t cIndex = 0; cIndex < channelParams->m_reducedClusterNumber; cIndex++)
1655 {
1656 if (maxPower < channelParams->m_clusterPower[cIndex] && cluster1st != cIndex)
1657 {
1658 maxPower = channelParams->m_clusterPower[cIndex];
1659 cluster2nd = cIndex;
1660 }
1661 }
1662 channelParams->m_cluster2nd = cluster2nd;
1663
1664 NS_LOG_INFO ("1st strongest cluster:" << +cluster1st
1665 << ", 2nd strongest cluster:" << +cluster2nd);
1666
1667 // store the delays and the angles for the subclusters
1668 if (cluster1st == cluster2nd)
1669 {
1670 clusterDelay.push_back (clusterDelay[cluster1st] + 1.28 * table3gpp->m_cDS);
1671 clusterDelay.push_back (clusterDelay[cluster1st] + 2.56 * table3gpp->m_cDS);
1672
1673 clusterAoa.push_back (clusterAoa[cluster1st]);
1674 clusterAoa.push_back (clusterAoa[cluster1st]);
1675
1676 clusterZoa.push_back (clusterZoa[cluster1st]);
1677 clusterZoa.push_back (clusterZoa[cluster1st]);
1678
1679 clusterAod.push_back (clusterAod[cluster1st]);
1680 clusterAod.push_back (clusterAod[cluster1st]);
1681
1682 clusterZod.push_back (clusterZod[cluster1st]);
1683 clusterZod.push_back (clusterZod[cluster1st]);
1684 }
1685 else
1686 {
1687 double min, max;
1688 if (cluster1st < cluster2nd)
1689 {
1690 min = cluster1st;
1691 max = cluster2nd;
1692 }
1693 else
1694 {
1695 min = cluster2nd;
1696 max = cluster1st;
1697 }
1698 clusterDelay.push_back (clusterDelay[min] + 1.28 * table3gpp->m_cDS);
1699 clusterDelay.push_back (clusterDelay[min] + 2.56 * table3gpp->m_cDS);
1700 clusterDelay.push_back (clusterDelay[max] + 1.28 * table3gpp->m_cDS);
1701 clusterDelay.push_back (clusterDelay[max] + 2.56 * table3gpp->m_cDS);
1702
1703 clusterAoa.push_back (clusterAoa[min]);
1704 clusterAoa.push_back (clusterAoa[min]);
1705 clusterAoa.push_back (clusterAoa[max]);
1706 clusterAoa.push_back (clusterAoa[max]);
1707
1708 clusterZoa.push_back (clusterZoa[min]);
1709 clusterZoa.push_back (clusterZoa[min]);
1710 clusterZoa.push_back (clusterZoa[max]);
1711 clusterZoa.push_back (clusterZoa[max]);
1712
1713 clusterAod.push_back (clusterAod[min]);
1714 clusterAod.push_back (clusterAod[min]);
1715 clusterAod.push_back (clusterAod[max]);
1716 clusterAod.push_back (clusterAod[max]);
1717
1718 clusterZod.push_back (clusterZod[min]);
1719 clusterZod.push_back (clusterZod[min]);
1720 clusterZod.push_back (clusterZod[max]);
1721 clusterZod.push_back (clusterZod[max]);
1722
1723
1724 }
1725
1726 channelParams->m_delay = clusterDelay;
1727 channelParams->m_angle.clear ();
1728 channelParams->m_angle.push_back (clusterAoa);
1729 channelParams->m_angle.push_back (clusterZoa);
1730 channelParams->m_angle.push_back (clusterAod);
1731 channelParams->m_angle.push_back (clusterZod);
1732
1733 // Compute alpha and D as described in 3GPP TR 37.885 v15.3.0, Sec. 6.2.3
1734 // These terms account for an additional Doppler contribution due to the
1735 // presence of moving objects in the surrounding environment, such as in
1736 // vehicular scenarios.
1737 // This contribution is applied only to the delayed (reflected) paths and
1738 // must be properly configured by setting the value of
1739 // m_vScatt, which is defined as "maximum speed of the vehicle in the
1740 // layout".
1741 // By default, m_vScatt is set to 0, so there is no additional Doppler
1742 // contribution.
1743
1744 DoubleVector dopplerTermAlpha, dopplerTermD;
1745
1746 // 2 or 4 is added to account for additional subrays for the 1st and 2nd clusters, if there is only one cluster then would be added 2 more subrays (see creation of Husn channel matrix)
1747 uint8_t updatedClusterNumber = (channelParams->m_reducedClusterNumber == 1) ? channelParams->m_reducedClusterNumber + 2 : channelParams->m_reducedClusterNumber + 4;
1748
1749 for (uint8_t cIndex = 0; cIndex < updatedClusterNumber ; cIndex++)
1750 {
1751 double alpha = 0;
1752 double D = 0;
1753 if (cIndex != 0)
1754 {
1755 alpha = m_uniformRvDoppler->GetValue (-1, 1);
1756 D = m_uniformRvDoppler->GetValue (-m_vScatt, m_vScatt);
1757 }
1758 dopplerTermAlpha.push_back (alpha);
1759 dopplerTermD.push_back (D);
1760 }
1761 channelParams->m_alpha = dopplerTermAlpha;
1762 channelParams->m_D = dopplerTermD;
1763
1764 return channelParams;
1765}
1766
1767Ptr<MatrixBasedChannelModel::ChannelMatrix>
1768ThreeGppChannelModel::GetNewChannel (Ptr<const ThreeGppChannelParams> channelParams,
1769 Ptr<const ParamsTable> table3gpp,
1770 const Ptr<const MobilityModel> sMob,
1771 const Ptr<const MobilityModel> uMob,
1772 Ptr<const PhasedArrayModel> sAntenna,
1773 Ptr<const PhasedArrayModel> uAntenna
1774 ) const
1775{
1776 NS_LOG_FUNCTION (this);
1777
1778 NS_ASSERT_MSG (m_frequency > 0.0, "Set the operating frequency first!");
1779
1780 // create a channel matrix instance
1781 Ptr<ChannelMatrix> channelMatrix = Create<ChannelMatrix> ();
1782 channelMatrix->m_generatedTime = Simulator::Now ();
1783 // save in which order is generated this matrix
1784 channelMatrix->m_nodeIds = std::make_pair (sMob->GetObject<Node> ()->GetId (), uMob->GetObject<Node> ()->GetId ());
1785 // check if channelParams structure is generated in direction s-to-u or u-to-s
1786 bool isSameDirection = (channelParams->m_nodeIds == channelMatrix->m_nodeIds);
1787
1788 MatrixBasedChannelModel::Double2DVector rayAodRadian;
1789 MatrixBasedChannelModel::Double2DVector rayAoaRadian;
1790 MatrixBasedChannelModel::Double2DVector rayZodRadian;
1791 MatrixBasedChannelModel::Double2DVector rayZoaRadian;
1792
1793 // if channel params is generated in the same direction in which we
1794 // generate the channel matrix, angles and zenit od departure and arrival are ok,
1795 // just set them to corresponding variable that will be used for the generation
1796 // of channel matrix, otherwise we need to flip angles and zenits of departure and arrival
1797 if (isSameDirection)
1798 {
1799 rayAodRadian = channelParams->m_rayAodRadian;
1800 rayAoaRadian = channelParams->m_rayAoaRadian;
1801 rayZodRadian = channelParams->m_rayZodRadian;
1802 rayZoaRadian = channelParams->m_rayZoaRadian;
1803 }
1804 else
1805 {
1806 rayAodRadian = channelParams->m_rayAoaRadian;
1807 rayAoaRadian = channelParams->m_rayAodRadian;
1808 rayZodRadian = channelParams->m_rayZoaRadian;
1809 rayZoaRadian = channelParams->m_rayZodRadian;
1810 }
1811
1812 //Step 11: Generate channel coefficients for each cluster n and each receiver
1813 // and transmitter element pair u,s.
1814 Complex3DVector hUsn; //channel coffecient hUsn[u][s][n];
1815 // where u and s are receive and transmit antenna element, n is cluster index.
1816 // NOTE Since each of the strongest 2 clusters are divided into 3 sub-clusters,
1817 // the total cluster will be numReducedCLuster + 4.
1818 uint64_t uSize = uAntenna->GetNumberOfElements ();
1819 uint64_t sSize = sAntenna->GetNumberOfElements ();
1820
1821 hUsn.resize (uSize);
1822 for (uint64_t uIndex = 0; uIndex < uSize; uIndex++)
1823 {
1824 hUsn[uIndex].resize (sSize);
1825 for (uint64_t sIndex = 0; sIndex < sSize; sIndex++)
1826 {
1827 hUsn[uIndex][sIndex].resize (channelParams->m_reducedClusterNumber);
1828 }
1829 }
1830
1831 NS_ASSERT (channelParams->m_reducedClusterNumber <= channelParams->m_clusterPhase.size ());
1832 NS_ASSERT (channelParams->m_reducedClusterNumber <= channelParams->m_clusterPower.size ());
1833 NS_ASSERT (channelParams->m_reducedClusterNumber <= channelParams->m_crossPolarizationPowerRatios.size ());
1834 NS_ASSERT (channelParams->m_reducedClusterNumber <= rayZoaRadian.size ());
1835 NS_ASSERT (channelParams->m_reducedClusterNumber <= rayZodRadian.size ());
1836 NS_ASSERT (channelParams->m_reducedClusterNumber <= rayAoaRadian.size ());
1837 NS_ASSERT (channelParams->m_reducedClusterNumber <= rayAodRadian.size ());
1838 NS_ASSERT (table3gpp->m_raysPerCluster <= channelParams->m_clusterPhase[0].size ());
1839 NS_ASSERT (table3gpp->m_raysPerCluster <= channelParams->m_crossPolarizationPowerRatios[0].size ());
1840 NS_ASSERT (table3gpp->m_raysPerCluster <= rayZoaRadian[0].size ());
1841 NS_ASSERT (table3gpp->m_raysPerCluster <= rayZodRadian[0].size ());
1842 NS_ASSERT (table3gpp->m_raysPerCluster <= rayAoaRadian[0].size ());
1843 NS_ASSERT (table3gpp->m_raysPerCluster <= rayAodRadian[0].size ());
1844
1845
1846 double x = sMob->GetPosition ().x - uMob->GetPosition ().x;
1847 double y = sMob->GetPosition ().y - uMob->GetPosition ().y;
1848 double distance2D = sqrt (x * x + y * y);
1849 // NOTE we assume hUT = min (height(a), height(b)) and
1850 // hBS = max (height (a), height (b))
1851 double hUt = std::min (sMob->GetPosition ().z, uMob->GetPosition ().z);
1852 double hBs = std::max (sMob->GetPosition ().z, uMob->GetPosition ().z);
1853 // compute the 3D distance using eq. 7.4-1
1854 double distance3D = std::sqrt (distance2D * distance2D + (hBs - hUt) * (hBs - hUt));
1855
1856 Angles sAngle (uMob->GetPosition (), sMob->GetPosition ());
1857 Angles uAngle (sMob->GetPosition (), uMob->GetPosition ());
1858
1859
1860 // The following for loops computes the channel coefficients
1861 for (uint64_t uIndex = 0; uIndex < uSize; uIndex++)
1862 {
1863 Vector uLoc = uAntenna->GetElementLocation (uIndex);
1864
1865 for (uint64_t sIndex = 0; sIndex < sSize; sIndex++)
1866 {
1867 Vector sLoc = sAntenna->GetElementLocation (sIndex);
1868
1869 for (uint8_t nIndex = 0; nIndex < channelParams->m_reducedClusterNumber; nIndex++)
1870 {
1871 //Compute the N-2 weakest cluster, assuming 0 slant angle and a
1872 //polarization slant angle configured in the array (7.5-22)
1873 if (nIndex != channelParams->m_cluster1st && nIndex != channelParams->m_cluster2nd)
1874 {
1875 std::complex<double> rays (0,0);
1876 for (uint8_t mIndex = 0; mIndex < table3gpp->m_raysPerCluster; mIndex++)
1877 {
1878 DoubleVector initialPhase = channelParams->m_clusterPhase[nIndex][mIndex];
1879 double k = channelParams->m_crossPolarizationPowerRatios[nIndex][mIndex];
1880 //lambda_0 is accounted in the antenna spacing uLoc and sLoc.
1881 double rxPhaseDiff = 2 * M_PI * (sin (rayZoaRadian[nIndex][mIndex]) * cos (rayAoaRadian[nIndex][mIndex]) * uLoc.x
1882 + sin (rayZoaRadian[nIndex][mIndex]) * sin (rayAoaRadian[nIndex][mIndex]) * uLoc.y
1883 + cos (rayZoaRadian[nIndex][mIndex]) * uLoc.z);
1884
1885 double txPhaseDiff = 2 * M_PI * (sin (rayZodRadian[nIndex][mIndex]) * cos (rayAodRadian[nIndex][mIndex]) * sLoc.x
1886 + sin (rayZodRadian[nIndex][mIndex]) * sin (rayAodRadian[nIndex][mIndex]) * sLoc.y
1887 + cos (rayZodRadian[nIndex][mIndex]) * sLoc.z);
1888 // NOTE Doppler is computed in the CalcBeamformingGain function and is simplified to only account for the center angle of each cluster.
1889
1890 double rxFieldPatternPhi, rxFieldPatternTheta, txFieldPatternPhi, txFieldPatternTheta;
1891 std::tie (rxFieldPatternPhi, rxFieldPatternTheta) = uAntenna->GetElementFieldPattern (Angles (channelParams->m_rayAoaRadian[nIndex][mIndex], channelParams->m_rayZoaRadian[nIndex][mIndex]));
1892 std::tie (txFieldPatternPhi, txFieldPatternTheta) = sAntenna->GetElementFieldPattern (Angles (channelParams->m_rayAodRadian[nIndex][mIndex], channelParams->m_rayZodRadian[nIndex][mIndex]));
1893 NS_ASSERT (4 <= initialPhase.size ());
1894 rays += (std::complex<double> (cos (initialPhase[0]), sin (initialPhase[0])) * rxFieldPatternTheta * txFieldPatternTheta +
1895 std::complex<double> (cos (initialPhase[1]), sin (initialPhase[1])) * std::sqrt (1 / k) * rxFieldPatternTheta * txFieldPatternPhi +
1896 std::complex<double> (cos (initialPhase[2]), sin (initialPhase[2])) * std::sqrt (1 / k) * rxFieldPatternPhi * txFieldPatternTheta +
1897 std::complex<double> (cos (initialPhase[3]), sin (initialPhase[3])) * rxFieldPatternPhi * txFieldPatternPhi)
1898 * std::complex<double> (cos (rxPhaseDiff), sin (rxPhaseDiff))
1899 * std::complex<double> (cos (txPhaseDiff), sin (txPhaseDiff));
1900 }
1901 rays *= sqrt (channelParams->m_clusterPower[nIndex] / table3gpp->m_raysPerCluster);
1902 hUsn[uIndex][sIndex][nIndex] = rays;
1903 }
1904 else //(7.5-28)
1905 {
1906 std::complex<double> raysSub1 (0, 0);
1907 std::complex<double> raysSub2 (0, 0);
1908 std::complex<double> raysSub3 (0, 0);
1909
1910 for (uint8_t mIndex = 0; mIndex < table3gpp->m_raysPerCluster; mIndex++)
1911 {
1912 double k = channelParams->m_crossPolarizationPowerRatios[nIndex][mIndex];
1913
1914 //ZML:Just remind me that the angle offsets for the 3 subclusters were not generated correctly.
1915 DoubleVector initialPhase = channelParams->m_clusterPhase[nIndex][mIndex];
1916 NS_ASSERT (4 <= initialPhase.size ());
1917
1918 double rxPhaseDiff = 2 * M_PI * (sin (rayZoaRadian[nIndex][mIndex]) * cos (rayAoaRadian[nIndex][mIndex]) * uLoc.x
1919 + sin (rayZoaRadian[nIndex][mIndex]) * sin (rayAoaRadian[nIndex][mIndex]) * uLoc.y
1920 + cos (rayZoaRadian[nIndex][mIndex]) * uLoc.z);
1921 double txPhaseDiff = 2 * M_PI * (sin (rayZodRadian[nIndex][mIndex]) * cos (rayAodRadian[nIndex][mIndex]) * sLoc.x
1922 + sin (rayZodRadian[nIndex][mIndex]) * sin (rayAodRadian[nIndex][mIndex]) * sLoc.y
1923 + cos (rayZodRadian[nIndex][mIndex]) * sLoc.z);
1924
1925 double rxFieldPatternPhi, rxFieldPatternTheta, txFieldPatternPhi, txFieldPatternTheta;
1926 std::tie (rxFieldPatternPhi, rxFieldPatternTheta) = uAntenna->GetElementFieldPattern (Angles (rayAoaRadian[nIndex][mIndex], rayZoaRadian[nIndex][mIndex]));
1927 std::tie (txFieldPatternPhi, txFieldPatternTheta) = sAntenna->GetElementFieldPattern (Angles (rayAodRadian[nIndex][mIndex], rayZodRadian[nIndex][mIndex]));
1928
1929 std::complex<double> raySub = (std::complex<double> (cos (initialPhase[0]), sin (initialPhase[0])) * rxFieldPatternTheta * txFieldPatternTheta +
1930 std::complex<double> (cos (initialPhase[1]), sin (initialPhase[1])) * sqrt (1 / k) * rxFieldPatternTheta * txFieldPatternPhi +
1931 std::complex<double> (cos (initialPhase[2]), sin (initialPhase[2])) * sqrt (1 / k) * rxFieldPatternPhi * txFieldPatternTheta +
1932 std::complex<double> (cos (initialPhase[3]), sin (initialPhase[3])) * rxFieldPatternPhi * txFieldPatternPhi)
1933 * std::complex<double> (cos (rxPhaseDiff), sin (rxPhaseDiff))
1934 * std::complex<double> (cos (txPhaseDiff), sin (txPhaseDiff));
1935
1936 switch (mIndex)
1937 {
1938 case 9:
1939 case 10:
1940 case 11:
1941 case 12:
1942 case 17:
1943 case 18:
1944 raysSub2 += raySub;
1945 break;
1946 case 13:
1947 case 14:
1948 case 15:
1949 case 16:
1950 raysSub3 += raySub;
1951 break;
1952 default: //case 1,2,3,4,5,6,7,8,19,20
1953 raysSub1 += raySub;
1954 break;
1955 }
1956 }
1957 raysSub1 *= sqrt (channelParams->m_clusterPower[nIndex] / table3gpp->m_raysPerCluster);
1958 raysSub2 *= sqrt (channelParams->m_clusterPower[nIndex] / table3gpp->m_raysPerCluster);
1959 raysSub3 *= sqrt (channelParams->m_clusterPower[nIndex] / table3gpp->m_raysPerCluster);
1960 hUsn[uIndex][sIndex][nIndex] = raysSub1;
1961 hUsn[uIndex][sIndex].push_back (raysSub2);
1962 hUsn[uIndex][sIndex].push_back (raysSub3);
1963 }
1964 }
1965
1966 if (channelParams->m_losCondition == ChannelCondition::LOS) //(7.5-29) && (7.5-30)
1967 {
1968 std::complex<double> ray (0, 0);
1969 double rxPhaseDiff = 2 * M_PI * (sin (uAngle.GetInclination ()) * cos (uAngle.GetAzimuth ()) * uLoc.x
1970 + sin (uAngle.GetInclination ()) * sin (uAngle.GetAzimuth ()) * uLoc.y
1971 + cos (uAngle.GetInclination ()) * uLoc.z);
1972 double txPhaseDiff = 2 * M_PI * (sin (sAngle.GetInclination ()) * cos (sAngle.GetAzimuth ()) * sLoc.x
1973 + sin (sAngle.GetInclination ()) * sin (sAngle.GetAzimuth ()) * sLoc.y
1974 + cos (sAngle.GetInclination ()) * sLoc.z);
1975
1976 double rxFieldPatternPhi, rxFieldPatternTheta, txFieldPatternPhi, txFieldPatternTheta;
1977 std::tie (rxFieldPatternPhi, rxFieldPatternTheta) = uAntenna->GetElementFieldPattern (Angles (uAngle.GetAzimuth (), uAngle.GetInclination ()));
1978 std::tie (txFieldPatternPhi, txFieldPatternTheta) = sAntenna->GetElementFieldPattern (Angles (sAngle.GetAzimuth (), sAngle.GetInclination ()));
1979
1980 double lambda = 3e8 / m_frequency; // the wavelength of the carrier frequency
1981
1982 ray = (rxFieldPatternTheta * txFieldPatternTheta - rxFieldPatternPhi * txFieldPatternPhi)
1983 * std::complex<double> (cos (-2 * M_PI * distance3D / lambda), sin (-2 * M_PI * distance3D / lambda))
1984 * std::complex<double> (cos (rxPhaseDiff), sin (rxPhaseDiff))
1985 * std::complex<double> (cos (txPhaseDiff), sin (txPhaseDiff));
1986
1987 double kLinear = pow (10, channelParams->m_K_factor / 10);
1988 // the LOS path should be attenuated if blockage is enabled.
1989 hUsn[uIndex][sIndex][0] = sqrt (1 / (kLinear + 1)) * hUsn[uIndex][sIndex][0] + sqrt (kLinear / (1 + kLinear)) * ray / pow (10, channelParams->m_attenuation_dB[0] / 10); //(7.5-30) for tau = tau1
1990 double tempSize = hUsn[uIndex][sIndex].size ();
1991 for (uint8_t nIndex = 1; nIndex < tempSize; nIndex++)
1992 {
1993 hUsn[uIndex][sIndex][nIndex] *= sqrt (1 / (kLinear + 1)); //(7.5-30) for tau = tau2...taunN
1994 }
1995 }
1996 }
1997 }
1998
1999 NS_LOG_DEBUG ("Husn (sAntenna, uAntenna):" << sAntenna->GetId () << ", " << uAntenna->GetId ());
2000 for (auto& i:hUsn)
2001 {
2002 for (auto& j:i)
2003 {
2004 for (auto& k:j)
2005 {
2006 NS_LOG_DEBUG (" " << k << ",");
2007 }
2008 }
2009 }
2010 NS_LOG_INFO ("size of coefficient matrix =[" << hUsn.size () << "][" << hUsn[0].size () << "][" << hUsn[0][0].size () << "]");
2011 channelMatrix->m_channel = hUsn;
2012 return channelMatrix;
2013}
2014
2015std::pair<double, double>
2016ThreeGppChannelModel::WrapAngles (double azimuthRad, double inclinationRad)
2017{
2018 inclinationRad = WrapTo2Pi (inclinationRad);
2019 if (inclinationRad > M_PI)
2020 {
2021 // inclination must be in [0, M_PI]
2022 inclinationRad -= M_PI;
2023 azimuthRad += M_PI;
2024 }
2025
2026 azimuthRad = WrapTo2Pi (azimuthRad);
2027
2028 NS_ASSERT_MSG (0 <= inclinationRad && inclinationRad <= M_PI,
2029 "inclinationRad=" << inclinationRad << " not valid, should be in [0, pi]");
2030 NS_ASSERT_MSG (0 <= azimuthRad && azimuthRad <= 2 * M_PI,
2031 "azimuthRad=" << azimuthRad << " not valid, should be in [0, 2*pi]");
2032
2033 return std::make_pair (azimuthRad, inclinationRad);
2034}
2035
2036MatrixBasedChannelModel::DoubleVector
2037ThreeGppChannelModel::CalcAttenuationOfBlockage (const Ptr<ThreeGppChannelModel::ThreeGppChannelParams> channelParams,
2038 const DoubleVector &clusterAOA,
2039 const DoubleVector &clusterZOA) const
2040{
2041 NS_LOG_FUNCTION (this);
2042
2043 DoubleVector powerAttenuation;
2044 uint8_t clusterNum = clusterAOA.size ();
2045 for (uint8_t cInd = 0; cInd < clusterNum; cInd++)
2046 {
2047 powerAttenuation.push_back (0); //Initial power attenuation for all clusters to be 0 dB;
2048 }
2049 //step a: the number of non-self blocking blockers is stored in m_numNonSelfBlocking.
2050
2051 //step b:Generate the size and location of each blocker
2052 //generate self blocking (i.e., for blockage from the human body)
2053 //table 7.6.4.1-1 Self-blocking region parameters.
2054 // Defaults: landscape mode
2055 double phiSb = 40;
2056 double xSb = 160;
2057 double thetaSb = 110;
2058 double ySb = 75;
2059 if (m_portraitMode)
2060 {
2061 phiSb = 260;
2062 xSb = 120;
2063 thetaSb = 100;
2064 ySb = 80;
2065 }
2066
2067 //generate or update non-self blocking
2068 if (channelParams->m_nonSelfBlocking.size () == 0) //generate new blocking regions
2069 {
2070 for (uint16_t blockInd = 0; blockInd < m_numNonSelfBlocking; blockInd++)
2071 {
2072 //draw value from table 7.6.4.1-2 Blocking region parameters
2073 DoubleVector table;
2074 table.push_back (m_normalRv->GetValue ()); //phi_k: store the normal RV that will be mapped to uniform (0,360) later.
2075 if (m_scenario == "InH-OfficeMixed" || m_scenario == "InH-OfficeOpen")
2076 {
2077 table.push_back (m_uniformRv->GetValue (15, 45)); //x_k
2078 table.push_back (90); //Theta_k
2079 table.push_back (m_uniformRv->GetValue (5, 15)); //y_k
2080 table.push_back (2); //r
2081 }
2082 else
2083 {
2084 table.push_back (m_uniformRv->GetValue (5, 15)); //x_k
2085 table.push_back (90); //Theta_k
2086 table.push_back (5); //y_k
2087 table.push_back (10); //r
2088 }
2089 channelParams->m_nonSelfBlocking.push_back (table);
2090 }
2091 }
2092 else
2093 {
2094 double deltaX = sqrt (pow (channelParams->m_preLocUT.x - channelParams->m_locUT.x, 2) + pow (channelParams->m_preLocUT.y - channelParams->m_locUT.y, 2));
2095 //if deltaX and speed are both 0, the autocorrelation is 1, skip updating
2096 if (deltaX > 1e-6 || m_blockerSpeed > 1e-6)
2097 {
2098 double corrDis;
2099 //draw value from table 7.6.4.1-4: Spatial correlation distance for different m_scenarios.
2100 if (m_scenario == "InH-OfficeMixed" || m_scenario == "InH-OfficeOpen")
2101 {
2102 //InH, correlation distance = 5;
2103 corrDis = 5;
2104 }
2105 else
2106 {
2107 if (channelParams->m_o2iCondition == ChannelCondition::O2I) // outdoor to indoor
2108 {
2109 corrDis = 5;
2110 }
2111 else //LOS or NLOS
2112 {
2113 corrDis = 10;
2114 }
2115 }
2116 double R;
2117 if (m_blockerSpeed > 1e-6) // speed not equal to 0
2118 {
2119 double corrT = corrDis / m_blockerSpeed;
2120 R = exp (-1 * (deltaX / corrDis + (Now ().GetSeconds () - channelParams->m_generatedTime.GetSeconds ()) / corrT));
2121 }
2122 else
2123 {
2124 R = exp (-1 * (deltaX / corrDis));
2125 }
2126
2127 NS_LOG_INFO ("Distance change:" << deltaX << " Speed:" << m_blockerSpeed
2128 << " Time difference:" << Now ().GetSeconds () - channelParams->m_generatedTime.GetSeconds ()
2129 << " correlation:" << R);
2130
2131 //In order to generate correlated uniform random variables, we first generate correlated normal random variables and map the normal RV to uniform RV.
2132 //Notice the correlation will change if the RV is transformed from normal to uniform.
2133 //To compensate the distortion, the correlation of the normal RV is computed
2134 //such that the uniform RV would have the desired correlation when transformed from normal RV.
2135
2136 //The following formula was obtained from MATLAB numerical simulation.
2137
2138 if (R * R * (-0.069) + R * 1.074 - 0.002 < 1) //transform only when the correlation of normal RV is smaller than 1
2139 {
2140 R = R * R * (-0.069) + R * 1.074 - 0.002;
2141 }
2142 for (uint16_t blockInd = 0; blockInd < m_numNonSelfBlocking; blockInd++)
2143 {
2144
2145 //Generate a new correlated normal RV with the following formula
2146 channelParams->m_nonSelfBlocking[blockInd][PHI_INDEX] =
2147 R * channelParams->m_nonSelfBlocking[blockInd][PHI_INDEX] + sqrt (1 - R * R) * m_normalRv->GetValue ();
2148 }
2149 }
2150
2151 }
2152
2153 //step c: Determine the attenuation of each blocker due to blockers
2154 for (uint8_t cInd = 0; cInd < clusterNum; cInd++)
2155 {
2156 NS_ASSERT_MSG (clusterAOA[cInd] >= 0 && clusterAOA[cInd] <= 360, "the AOA should be the range of [0,360]");
2157 NS_ASSERT_MSG (clusterZOA[cInd] >= 0 && clusterZOA[cInd] <= 180, "the ZOA should be the range of [0,180]");
2158
2159 //check self blocking
2160 NS_LOG_INFO ("AOA=" << clusterAOA[cInd] << " Block Region[" << phiSb - xSb / 2 << ","
2161 << phiSb + xSb / 2 << "]");
2162 NS_LOG_INFO ("ZOA=" << clusterZOA[cInd] << " Block Region[" << thetaSb - ySb / 2 << ","
2163 << thetaSb + ySb / 2 << "]");
2164 if (std::abs (clusterAOA[cInd] - phiSb) < (xSb / 2)
2165 && std::abs (clusterZOA[cInd] - thetaSb) < (ySb / 2))
2166 {
2167 powerAttenuation[cInd] += 30; //anttenuate by 30 dB.
2168 NS_LOG_INFO ("Cluster[" << +cInd << "] is blocked by self blocking region and reduce 30 dB power,"
2169 "the attenuation is [" << powerAttenuation[cInd] << " dB]");
2170 }
2171
2172 //check non-self blocking
2173 for (uint16_t blockInd = 0; blockInd < m_numNonSelfBlocking; blockInd++)
2174 {
2175 //The normal RV is transformed to uniform RV with the desired correlation.
2176 double phiK = (0.5 * erfc (-1 * channelParams->m_nonSelfBlocking[blockInd][PHI_INDEX] / sqrt (2))) * 360;
2177 while (phiK > 360)
2178 {
2179 phiK -= 360;
2180 }
2181
2182 while (phiK < 0)
2183 {
2184 phiK += 360;
2185 }
2186
2187 double xK = channelParams->m_nonSelfBlocking[blockInd][X_INDEX];
2188 double thetaK = channelParams->m_nonSelfBlocking[blockInd][THETA_INDEX];
2189 double yK = channelParams->m_nonSelfBlocking[blockInd][Y_INDEX];
2190
2191 NS_LOG_INFO ("AOA=" << clusterAOA[cInd] << " Block Region[" << phiK - xK << "," << phiK + xK << "]");
2192 NS_LOG_INFO ("ZOA=" << clusterZOA[cInd] << " Block Region[" << thetaK - yK << "," << thetaK + yK << "]");
2193
2194 if ( std::abs (clusterAOA[cInd] - phiK) < (xK)
2195 && std::abs (clusterZOA[cInd] - thetaK) < (yK))
2196 {
2197 double A1 = clusterAOA[cInd] - (phiK + xK / 2); //(7.6-24)
2198 double A2 = clusterAOA[cInd] - (phiK - xK / 2); //(7.6-25)
2199 double Z1 = clusterZOA[cInd] - (thetaK + yK / 2); //(7.6-26)
2200 double Z2 = clusterZOA[cInd] - (thetaK - yK / 2); //(7.6-27)
2201 int signA1, signA2, signZ1, signZ2;
2202 //draw sign for the above parameters according to table 7.6.4.1-3 Description of signs
2203 if (xK / 2 < clusterAOA[cInd] - phiK && clusterAOA[cInd] - phiK <= xK)
2204 {
2205 signA1 = -1;
2206 }
2207 else
2208 {
2209 signA1 = 1;
2210 }
2211 if (-1 * xK < clusterAOA[cInd] - phiK && clusterAOA[cInd] - phiK <= -1 * xK / 2)
2212 {
2213 signA2 = -1;
2214 }
2215 else
2216 {
2217 signA2 = 1;
2218 }
2219
2220 if (yK / 2 < clusterZOA[cInd] - thetaK && clusterZOA[cInd] - thetaK <= yK)
2221 {
2222 signZ1 = -1;
2223 }
2224 else
2225 {
2226 signZ1 = 1;
2227 }
2228 if (-1 * yK < clusterZOA[cInd] - thetaK && clusterZOA[cInd] - thetaK <= -1 * yK / 2)
2229 {
2230 signZ2 = -1;
2231 }
2232 else
2233 {
2234 signZ2 = 1;
2235 }
2236 double lambda = 3e8 / m_frequency;
2237 double fA1 = atan (signA1 * M_PI / 2 * sqrt (M_PI / lambda *
2238 channelParams->m_nonSelfBlocking[blockInd][R_INDEX] * (1 / cos (DegreesToRadians (A1)) - 1))) / M_PI; //(7.6-23)
2239 double fA2 = atan (signA2 * M_PI / 2 * sqrt (M_PI / lambda *
2240 channelParams->m_nonSelfBlocking[blockInd][R_INDEX] * (1 / cos (DegreesToRadians (A2)) - 1))) / M_PI;
2241 double fZ1 = atan (signZ1 * M_PI / 2 * sqrt (M_PI / lambda *
2242 channelParams->m_nonSelfBlocking[blockInd][R_INDEX] * (1 / cos (DegreesToRadians (Z1)) - 1))) / M_PI;
2243 double fZ2 = atan (signZ2 * M_PI / 2 * sqrt (M_PI / lambda *
2244 channelParams->m_nonSelfBlocking[blockInd][R_INDEX] * (1 / cos (DegreesToRadians (Z2)) - 1))) / M_PI;
2245 double lDb = -20 * log10 (1 - (fA1 + fA2) * (fZ1 + fZ2)); //(7.6-22)
2246 powerAttenuation[cInd] += lDb;
2247 NS_LOG_INFO ("Cluster[" << +cInd << "] is blocked by no-self blocking, the loss is ["
2248 << lDb << "] dB");
2249 }
2250 }
2251 }
2252 return powerAttenuation;
2253}
2254
2255void
2256ThreeGppChannelModel::Shuffle (double * first, double * last) const
2257{
2258 for (auto i = (last - first) - 1; i > 0; --i)
2259 {
2260 std::swap (first[i], first[m_uniformRvShuffle->GetInteger (0, i)]);
2261 }
2262}
2263
2264int64_t
2265ThreeGppChannelModel::AssignStreams (int64_t stream)
2266{
2267 NS_LOG_FUNCTION (this << stream);
2268 m_normalRv->SetStream (stream);
2269 m_uniformRv->SetStream (stream + 1);
2270 m_uniformRvShuffle->SetStream (stream + 2);
2271 m_uniformRvDoppler->SetStream (stream + 3);
2272 return 4;
2273}
2274
2275} // namespace ns3
min
#define min(a, b)
Definition: 80211b.c:42
f
double f(double x, void *params)
Definition: 80211b.c:70
max
#define max(a, b)
Definition: 80211b.c:43
ns3::Angles
Class holding the azimuth and inclination angles of spherical coordinates.
Definition: angles.h:119
ns3::Angles::GetInclination
double GetInclination(void) const
Getter for inclination angle.
Definition: angles.cc:231
ns3::Angles::GetAzimuth
double GetAzimuth(void) const
Getter for azimuth angle.
Definition: angles.cc:224
ns3::BooleanValue
AttributeValue implementation for Boolean.
Definition: boolean.h:37
ns3::DoubleValue
This class can be used to hold variables of floating point type such as 'double' or 'float'.
Definition: double.h:41
ns3::IntegerValue
Hold a signed integer type.
Definition: integer.h:44
ns3::MatrixBasedChannelModel
This is an interface for a channel model that can be described by a channel matrix,...
Definition: matrix-based-channel-model.h:44
ns3::MatrixBasedChannelModel::Complex3DVector
std::vector< Complex2DVector > Complex3DVector
type definition for complex 3D matrices
Definition: matrix-based-channel-model.h:55
ns3::MatrixBasedChannelModel::Double2DVector
std::vector< DoubleVector > Double2DVector
type definition for matrices of doubles
Definition: matrix-based-channel-model.h:52
ns3::MatrixBasedChannelModel::DoubleVector
std::vector< double > DoubleVector
type definition for vectors of doubles
Definition: matrix-based-channel-model.h:51
ns3::MatrixBasedChannelModel::GetKey
static uint64_t GetKey(uint32_t a, uint32_t b)
Generate a unique value for the pair of unsigned integer of 32 bits, where the order does not matter,...
Definition: matrix-based-channel-model.h:148
ns3::MatrixBasedChannelModel::Double3DVector
std::vector< Double2DVector > Double3DVector
type definition for 3D matrices of doubles
Definition: matrix-based-channel-model.h:53
ns3::Node
A network Node.
Definition: node.h:57
ns3::Node::GetId
uint32_t GetId(void) const
Definition: node.cc:109
ns3::PointerValue
Hold objects of type Ptr<T>.
Definition: pointer.h:37
ns3::Ptr
Smart pointer class similar to boost::intrusive_ptr.
Definition: ptr.h:74
ns3::RandomVariableStream::SetStream
void SetStream(int64_t stream)
Specifies the stream number for the RngStream.
Definition: random-variable-stream.cc:99
ns3::Simulator::Now
static Time Now(void)
Return the current simulation virtual time.
Definition: simulator.cc:195
ns3::StringValue
Hold variables of type string.
Definition: string.h:41
ns3::ThreeGppChannelModel::CalcAttenuationOfBlockage
DoubleVector CalcAttenuationOfBlockage(const Ptr< ThreeGppChannelModel::ThreeGppChannelParams > channelParams, const DoubleVector &clusterAOA, const DoubleVector &clusterZOA) const
Applies the blockage model A described in 3GPP TR 38.901.
Definition: three-gpp-channel-model.cc:2037
ns3::ThreeGppChannelModel::AssignStreams
int64_t AssignStreams(int64_t stream)
Assign a fixed random variable stream number to the random variables used by this model.
Definition: three-gpp-channel-model.cc:2265
ns3::ThreeGppChannelModel::m_portraitMode
bool m_portraitMode
true if potrait mode, false if landscape
Definition: three-gpp-channel-model.h:333
ns3::ThreeGppChannelModel::ChannelParamsNeedsUpdate
bool ChannelParamsNeedsUpdate(Ptr< const ThreeGppChannelParams > channelParams, Ptr< const ChannelCondition > channelCondition) const
Check if the channel params has to be updated.
Definition: three-gpp-channel-model.cc:1042
ns3::ThreeGppChannelModel::GetThreeGppTable
virtual Ptr< const ParamsTable > GetThreeGppTable(Ptr< const ChannelCondition > channelCondition, double hBS, double hUT, double distance2D) const
Get the parameters needed to apply the channel generation procedure.
Definition: three-gpp-channel-model.cc:386
ns3::ThreeGppChannelModel::m_normalRv
Ptr< NormalRandomVariable > m_normalRv
normal random variable
Definition: three-gpp-channel-model.h:322
ns3::ThreeGppChannelModel::Y_INDEX
static const uint8_t Y_INDEX
index of the Y value in the m_nonSelfBlocking array
Definition: three-gpp-channel-model.h:339
ns3::ThreeGppChannelModel::m_blockage
bool m_blockage
enables the blockage model A
Definition: three-gpp-channel-model.h:331
ns3::ThreeGppChannelModel::GetParams
Ptr< const ChannelParams > GetParams(Ptr< const MobilityModel > aMob, Ptr< const MobilityModel > bMob) const override
Looks for the channel params associated to the aMob and bMob pair in m_channelParamsMap.
Definition: three-gpp-channel-model.cc:1175
ns3::ThreeGppChannelModel::ChannelMatrixNeedsUpdate
bool ChannelMatrixNeedsUpdate(Ptr< const ThreeGppChannelParams > channelParams, Ptr< const ChannelMatrix > channelMatrix)
Check if the channel matrix has to be updated (it needs update when the channel params generation tim...
Definition: three-gpp-channel-model.cc:1068
ns3::ThreeGppChannelModel::THETA_INDEX
static const uint8_t THETA_INDEX
index of the THETA value in the m_nonSelfBlocking array
Definition: three-gpp-channel-model.h:338
ns3::ThreeGppChannelModel::m_channelParamsMap
std::unordered_map< uint64_t, Ptr< ThreeGppChannelParams > > m_channelParamsMap
map containing the common channel parameters per pair of nodes, the key of this map is reciprocal and...
Definition: three-gpp-channel-model.h:316
ns3::ThreeGppChannelModel::WrapAngles
static std::pair< double, double > WrapAngles(double azimuthRad, double inclinationRad)
Wrap an (azimuth, inclination) angle pair in a valid range.
Definition: three-gpp-channel-model.cc:2016
ns3::ThreeGppChannelModel::m_blockerSpeed
double m_blockerSpeed
the blocker speed
Definition: three-gpp-channel-model.h:334
ns3::ThreeGppChannelModel::GetChannel
Ptr< const ChannelMatrix > GetChannel(Ptr< const MobilityModel > aMob, Ptr< const MobilityModel > bMob, Ptr< const PhasedArrayModel > aAntenna, Ptr< const PhasedArrayModel > bAntenna) override
Looks for the channel matrix associated to the aMob and bMob pair in m_channelMatrixMap.
Definition: three-gpp-channel-model.cc:1082
ns3::ThreeGppChannelModel::SetFrequency
void SetFrequency(double f)
Sets the center frequency of the model.
Definition: three-gpp-channel-model.cc:352
ns3::ThreeGppChannelModel::m_channelMatrixMap
std::unordered_map< uint64_t, Ptr< ChannelMatrix > > m_channelMatrixMap
map containing the channel realizations per pair of PhasedAntennaArray instances, the key of this map...
Definition: three-gpp-channel-model.h:315
ns3::ThreeGppChannelModel::m_uniformRv
Ptr< UniformRandomVariable > m_uniformRv
uniform random variable
Definition: three-gpp-channel-model.h:321
ns3::ThreeGppChannelModel::DoDispose
void DoDispose() override
Destructor implementation.
Definition: three-gpp-channel-model.cc:262
ns3::ThreeGppChannelModel::SetScenario
void SetScenario(const std::string &scenario)
Sets the propagation scenario.
Definition: three-gpp-channel-model.cc:367
ns3::ThreeGppChannelModel::SetChannelConditionModel
void SetChannelConditionModel(Ptr< ChannelConditionModel > model)
Set the channel condition model.
Definition: three-gpp-channel-model.cc:338
ns3::ThreeGppChannelModel::m_uniformRvDoppler
Ptr< UniformRandomVariable > m_uniformRvDoppler
uniform random variable, used to compute the additional Doppler contribution
Definition: three-gpp-channel-model.h:328
ns3::ThreeGppChannelModel::m_numNonSelfBlocking
uint16_t m_numNonSelfBlocking
number of non-self-blocking regions
Definition: three-gpp-channel-model.h:332
ns3::ThreeGppChannelModel::GetNewChannel
virtual Ptr< ChannelMatrix > GetNewChannel(Ptr< const ThreeGppChannelParams > channelParams, Ptr< const ParamsTable > table3gpp, const Ptr< const MobilityModel > sMob, const Ptr< const MobilityModel > uMob, Ptr< const PhasedArrayModel > sAntenna, Ptr< const PhasedArrayModel > uAntenna) const
Compute the channel matrix between two nodes a and b, and their antenna arrays aAntenna and bAntenna ...
Definition: three-gpp-channel-model.cc:1768
ns3::ThreeGppChannelModel::PHI_INDEX
static const uint8_t PHI_INDEX
index of the PHI value in the m_nonSelfBlocking array
Definition: three-gpp-channel-model.h:336
ns3::ThreeGppChannelModel::m_frequency
double m_frequency
the operating frequency
Definition: three-gpp-channel-model.h:318
ns3::ThreeGppChannelModel::m_vScatt
double m_vScatt
value used to compute the additional Doppler contribution for the delayed paths
Definition: three-gpp-channel-model.h:327
ns3::ThreeGppChannelModel::GetChannelConditionModel
Ptr< ChannelConditionModel > GetChannelConditionModel() const
Get the associated channel condition model.
Definition: three-gpp-channel-model.cc:345
ns3::ThreeGppChannelModel::m_channelConditionModel
Ptr< ChannelConditionModel > m_channelConditionModel
the channel condition model
Definition: three-gpp-channel-model.h:320
ns3::ThreeGppChannelModel::m_scenario
std::string m_scenario
the 3GPP scenario
Definition: three-gpp-channel-model.h:319
ns3::ThreeGppChannelModel::GetScenario
std::string GetScenario(void) const
Returns the propagation scenario.
Definition: three-gpp-channel-model.cc:379
ns3::ThreeGppChannelModel::R_INDEX
static const uint8_t R_INDEX
index of the R value in the m_nonSelfBlocking array
Definition: three-gpp-channel-model.h:340
ns3::ThreeGppChannelModel::GetFrequency
double GetFrequency(void) const
Returns the center frequency.
Definition: three-gpp-channel-model.cc:360
ns3::ThreeGppChannelModel::GetTypeId
static TypeId GetTypeId()
Get the type ID.
Definition: three-gpp-channel-model.cc:275
ns3::ThreeGppChannelModel::Shuffle
void Shuffle(double *first, double *last) const
Shuffle the elements of a simple sequence container of type double.
Definition: three-gpp-channel-model.cc:2256
ns3::ThreeGppChannelModel::GenerateChannelParameters
Ptr< ThreeGppChannelParams > GenerateChannelParameters(const Ptr< const ChannelCondition > channelCondition, const Ptr< const ParamsTable > table3gpp, const Ptr< const MobilityModel > aMob, const Ptr< const MobilityModel > bMob) const
Prepare 3gpp channel parameters among the nodes a and b.
Definition: three-gpp-channel-model.cc:1196
ns3::ThreeGppChannelModel::m_updatePeriod
Time m_updatePeriod
the channel update period
Definition: three-gpp-channel-model.h:317
ns3::ThreeGppChannelModel::X_INDEX
static const uint8_t X_INDEX
index of the X value in the m_nonSelfBlocking array
Definition: three-gpp-channel-model.h:337
ns3::ThreeGppChannelModel::m_uniformRvShuffle
Ptr< UniformRandomVariable > m_uniformRvShuffle
uniform random variable used to shuffle array in GetNewChannel
Definition: three-gpp-channel-model.h:323
ns3::Time::NS
@ NS
nanosecond
Definition: nstime.h:117
ns3::Time::IsZero
bool IsZero(void) const
Exactly equivalent to t == 0.
Definition: nstime.h:300
ns3::TimeValue
AttributeValue implementation for Time.
Definition: nstime.h:1308
ns3::TypeId
a unique identifier for an interface.
Definition: type-id.h:59
ns3::TypeId::SetGroupName
TypeId SetGroupName(std::string groupName)
Set the group name.
Definition: type-id.cc:929
ns3::TypeId::SetParent
TypeId SetParent(TypeId tid)
Set the parent TypeId.
Definition: type-id.cc:922
ns3::UniformRandomVariable::GetValue
double GetValue(double min, double max)
Get the next random value, as a double in the specified range .
Definition: random-variable-stream.cc:181
ns3::UniformRandomVariable::GetInteger
uint32_t GetInteger(uint32_t min, uint32_t max)
Get the next random value, as an unsigned integer in the specified range .
Definition: random-variable-stream.cc:192
NS_ASSERT
#define NS_ASSERT(condition)
At runtime, in debugging builds, if this condition is not true, the program prints the source file,...
Definition: assert.h:67
NS_ASSERT_MSG
#define NS_ASSERT_MSG(condition, message)
At runtime, in debugging builds, if this condition is not true, the program prints the message to out...
Definition: assert.h:88
ns3::MakeBooleanChecker
Ptr< const AttributeChecker > MakeBooleanChecker(void)
Definition: boolean.cc:121
ns3::MakeBooleanAccessor
Ptr< const AttributeAccessor > MakeBooleanAccessor(T1 a1)
Definition: boolean.h:85
ns3::MakeDoubleAccessor
Ptr< const AttributeAccessor > MakeDoubleAccessor(T1 a1)
Definition: double.h:42
ns3::MakeIntegerAccessor
Ptr< const AttributeAccessor > MakeIntegerAccessor(T1 a1)
Definition: integer.h:45
ns3::MakePointerAccessor
Ptr< const AttributeAccessor > MakePointerAccessor(T1 a1)
Definition: pointer.h:227
ns3::MakeStringAccessor
Ptr< const AttributeAccessor > MakeStringAccessor(T1 a1)
Definition: string.h:42
ns3::MakeStringChecker
Ptr< const AttributeChecker > MakeStringChecker(void)
Definition: string.cc:30
ns3::MakeTimeAccessor
Ptr< const AttributeAccessor > MakeTimeAccessor(T1 a1)
Definition: nstime.h:1309
NS_FATAL_ERROR
#define NS_FATAL_ERROR(msg)
Report a fatal error with a message and terminate.
Definition: fatal-error.h:165
NS_LOG_COMPONENT_DEFINE
#define NS_LOG_COMPONENT_DEFINE(name)
Define a Log component with a specific name.
Definition: log.h:205
NS_LOG_DEBUG
#define NS_LOG_DEBUG(msg)
Use NS_LOG to output a message of level LOG_DEBUG.
Definition: log.h:273
NS_LOG_FUNCTION
#define NS_LOG_FUNCTION(parameters)
If log level LOG_FUNCTION is enabled, this macro will output all input parameters separated by ",...
Definition: log-macros-enabled.h:244
NS_LOG_WARN
#define NS_LOG_WARN(msg)
Use NS_LOG to output a message of level LOG_WARN.
Definition: log.h:265
NS_LOG_INFO
#define NS_LOG_INFO(msg)
Use NS_LOG to output a message of level LOG_INFO.
Definition: log.h:281
NS_OBJECT_ENSURE_REGISTERED
#define NS_OBJECT_ENSURE_REGISTERED(type)
Register an Object subclass with the TypeId system.
Definition: object-base.h:45
ns3::Now
Time Now(void)
create an ns3::Time instance which contains the current simulation time.
Definition: simulator.cc:287
ns3::MilliSeconds
Time MilliSeconds(uint64_t value)
Construct a Time in the indicated unit.
Definition: nstime.h:1252
first
Definition: first.py:1
ns3
Every class exported by the ns3 library is enclosed in the ns3 namespace.
ns3::offSetAlpha
static const double offSetAlpha[20]
The ray offset angles within a cluster, given for rms angle spread normalized to 1....
Definition: three-gpp-channel-model.cc:44
ns3::sqrtC_RMa_O2I
static const double sqrtC_RMa_O2I[6][6]
The square root matrix for RMa O2I, which is generated using the Cholesky decomposition according to ...
Definition: three-gpp-channel-model.cc:92
ns3::sqrtC_UMi_LOS
static const double sqrtC_UMi_LOS[7][7]
The square root matrix for UMi LOS, which is generated using the Cholesky decomposition according to ...
Definition: three-gpp-channel-model.cc:163
ns3::sqrtC_office_LOS
static const double sqrtC_office_LOS[7][7]
The square root matrix for Indoor-Office LOS, which is generated using the Cholesky decomposition acc...
Definition: three-gpp-channel-model.cc:216
ns3::sqrtC_UMa_O2I
static const double sqrtC_UMa_O2I[6][6]
The square root matrix for UMa O2I, which is generated using the Cholesky decomposition according to ...
Definition: three-gpp-channel-model.cc:145
ns3::sqrtC_RMa_NLOS
static const double sqrtC_RMa_NLOS[6][6]
The square root matrix for RMa NLOS, which is generated using the Cholesky decomposition according to...
Definition: three-gpp-channel-model.cc:75
ns3::sqrtC_UMa_LOS
static const double sqrtC_UMa_LOS[7][7]
The square root matrix for UMa LOS, which is generated using the Cholesky decomposition according to ...
Definition: three-gpp-channel-model.cc:109
ns3::sqrtC_UMi_NLOS
static const double sqrtC_UMi_NLOS[6][6]
The square root matrix for UMi NLOS, which is generated using the Cholesky decomposition according to...
Definition: three-gpp-channel-model.cc:182
ns3::MakeTimeChecker
Ptr< const AttributeChecker > MakeTimeChecker(const Time min, const Time max)
Helper to make a Time checker with bounded range.
Definition: time.cc:536
ns3::sqrtC_RMa_LOS
static const double sqrtC_RMa_LOS[7][7]
The square root matrix for RMa LOS, which is generated using the Cholesky decomposition according to ...
Definition: three-gpp-channel-model.cc:56
ns3::DegreesToRadians
double DegreesToRadians(double degrees)
converts degrees to radians
Definition: angles.cc:40
ns3::sqrtC_UMi_O2I
static const double sqrtC_UMi_O2I[6][6]
The square root matrix for UMi O2I, which is generated using the Cholesky decomposition according to ...
Definition: three-gpp-channel-model.cc:199
ns3::sqrtC_office_NLOS
static const double sqrtC_office_NLOS[6][6]
The square root matrix for Indoor-Office NLOS, which is generated using the Cholesky decomposition ac...
Definition: three-gpp-channel-model.cc:235
ns3::sqrtC_UMa_NLOS
static const double sqrtC_UMa_NLOS[6][6]
The square root matrix for UMa NLOS, which is generated using the Cholesky decomposition according to...
Definition: three-gpp-channel-model.cc:128
ns3::WrapTo2Pi
double WrapTo2Pi(double a)
Wrap angle in [0, 2*M_PI)
Definition: angles.cc:110
ns3::RadiansToDegrees
double RadiansToDegrees(double radians)
converts radians to degrees
Definition: angles.cc:47
sample-rng-plot.alpha
float alpha
Plot alpha value (transparency)
Definition: sample-rng-plot.py:46
sample-rng-plot.x
list x
Random number samples.
Definition: sample-rng-plot.py:37
ns3::MatrixBasedChannelModel::ChannelMatrix::m_channel
Complex3DVector m_channel
channel matrix H[u][s][n].
Definition: matrix-based-channel-model.h:62
ns3::MatrixBasedChannelModel::ChannelMatrix::m_antennaPair
std::pair< uint32_t, uint32_t > m_antennaPair
the first element is the ID of the antenna of the s-node (the antenna of the transmitter when the cha...
Definition: matrix-based-channel-model.h:64
ns3::MatrixBasedChannelModel::ChannelMatrix::m_nodeIds
std::pair< uint32_t, uint32_t > m_nodeIds
the first element is the s-node ID (the transmitter when the channel was generated),...
Definition: matrix-based-channel-model.h:65