modeling voip in cognitive radio network
DESCRIPTION
Modeling VoIP in Cognitive Radio Network. Students: Taly Sessler038741401 Ben Rubovitch065631475 Instructor: Boris Oklander Semester: Winter 2010. Index. Introduction 1.1 VoIP 1.2 Cognitive Radio 2.Project’s Goals 3. Model Description 4. Simulation design 5. Results - PowerPoint PPT PresentationTRANSCRIPT
Modeling VoIP in Cognitive Radio Network
Students: Taly Sessler 038741401 Ben Rubovitch 065631475
Instructor: Boris OklanderSemester: Winter 2010
Index1. Introduction
1.1 VoIP 1.2 Cognitive Radio2. Project’s Goals3. Model Description4. Simulation design5. Results6. Conclusions7. Summery
Introduction to VoIP Voice over Internet Protocol- VoIP
is a general term for a family of transmission technologies for delivery of voice communications over IP networks such as the Internet or other packet-switched networks.
Flexibility - VoIP can facilitate tasks and provide services that may be more difficult to implement using the PSTN.Many telephone calls over a single channel.Secure calls using standardized protocols. Location independence. Integration with other Internet services.
Introduction to VoIPRelevant challenges
Quality of Service – the network cannot ensure that the data packets are delivered in sequential order, or provide Quality of Service (QoS) guarantees, VoIP implementations may face problems mitigating latency and jitter.
Delay Jitter - in the context of computer networks, the term jitter is often used as a measure of the variability over time of the packet latency across a network.
Jitter Buffer - Some systems use sophisticated delay-optimal de-jitter buffers that are capable of adapting the buffering delay to changing network jitter characteristics.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180
1
2
3
4
5
6
7
8
9
10
11se
nded
/rece
ived
pac
kets
time (in packet units)
XX
sended packetsreceived packetsplayout time (a)playout time (b)
• clustering / dispersion → overflow / time-outs • Trade-offs management: Delay ↔ Loss
Introduction to VoIP
Cognitive Radio (CR) is a new wireless communication paradigm.
CR characteristics:Based on Software Defined Radio (SDR).CR is aware of its environment and use case.Spectrum SensingSpectrum AnalysisSpectrum Decision
Introduction to Cognitive Radio
Project Goals Studying VoIP technology with emphasis on
Oos aspects Implementation of VoIP CRN Model using
MATLAB@
Executing and Performance studying
E-model MOS
Kλ
Delay Loss
TDelay
Network conditions •Delay•Loss
Codec characteristics•Equipment impairment•Loss robustness
R-Factor
jitter bufferjitter
buffercodec codeccodec codec
jitter buffer controller
Voice Quality
Jitter Buffer
Network & Codec
System’s Model
E-model MOSR-Factor
jitter bufferjitter
buffercodec codeccodec codec
jitter buffer controller
Voice Quality
Jitter Buffer
Network & Codec
CRN System’s Model
CRN
E-model MOS
Kλ
Delay Loss
TDelay
Network conditions •Delay•Loss
Codec characteristics•Equipment impairment•Loss robustness
R-Factor
jitter bufferjitter
buffercodec codeccodec codec
jitter buffer controller
Voice Quality
Jitter Buffer
Network & CodecCRN System’s Model
C(t)
Spectrum opportunities
Network Simulator
Performance Studying
Scenarios generator
MOS
Channel State
Simulator
Adaptive Jitter
Buffer
Network simulator
Channel State Simulator DesignInputs:M – number of channelsCi(t) – state of ith channel i=1,2,…,MPU parameters α,βSimulation timeOutputs:Channels(t) – state of channels
Channel State Simulator DesignChange 1:for i = 1:Network.channel_set.M nst = find(Network.channels(i).times > slot(n),1)-1; network_state = network_state + Network.channels(i).state(nst); if isempty(network_state) error('1'); endEndChange 2:
if network_state == 0 Stream.TOA(id) = -1; else Stream.TOA(id) = Stream.TOC(id)+Session.T_packet*50/network_state; if Stream.TOA(id) < Stream.TOA(id-1) Stream.TOA(id) = Stream.TOA(id-1)+ Session.T_packet/10000; endend
Design Description using UML tools
Class Diagrams
ResultsCRN activity vs. time for =0.1=0.1
time [sec]
chan
nel n
o.
100 200 300 400 500
5
10
15
20
25
CRN activity vs. time for =0.5=0.1
time [sec]
chan
nel n
o.
100 200 300 400 500
5
10
15
20
25
1 log 1
1 log 1
on
off
rand
rand
1 log 1
1 log 1
on
off
rand
rand
1 log 1
1 log 1
on
off
rand
rand
Results and Conclusions
1. Analytic - AJB2. AR-1 – re-evaluation3. AR-N – re-evaluation4. Constant Delay
Jitter Buffer Algorithm Types
C=0.1MOS results (Network.type, participant.type, JBuffer.type)
4 3 2 1 JBuffer
4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1participan
tNetwor
k
1 0.8394
1 1 0.6791
0.9966
0.8764
0.6974
1 0.1156
1 1 0.7368
0.9966
0.9336
0.7741
1 0.9297
1 1 0.5441
1 0.8461
0.7289
1 0.1220
1 1 0.8547
0.9966
0.9217
0.8882
1 0.9397
1 1 0.6016
0.9966
0.8676
0.7378
1 0.1258
1 1 0.9262
0.9966
0.9371
0.91943
0.9685
0.9030
1 1 0.6480
0.9966
0.8395
0.6603
1 0.3177
1 0.9962
0.5041
0.9866
0.8170
0.57624
1 0.8762
1 1 0.5785
0.9966
0.8762
0.7173
0.9911
0.1224
1 0.9963
0.5793
0.9966
0.8795
0.66795
1 0.8561
1 1 0.5396
0.9966
0.8846
0.6104
1 0.1118
1 1 0.7433
1 0.900 0.72556
C=2MOS results (Network.type, participant.type, JBuffer.type)
4 3 2 1 JBuffer
4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 participant Network
0.8250 0.9665 1 1 0.6484 0.9966 0.8815 0.7180 0.9919 0.1122 1 0.9961 0.6379 0.9933 0.8212 0.6752
1
0.9921 0.8762 1 1 0.5727 0.9966 0.9285 0.6902 1 0.1190 1 1 0.5801 0.9966 0.7668 0.6455
2
1 0.9163 1 1 0.5851 0.9966 0.8932 0.6792 1 0.1224 1 1 0.5212 0.9966 0.8367 0.6121
3
0.5052 0.9163 0.9907 0.6007 0.6218 0.7023 0.8562 0.6888 0.9557 0.1088 0.9890 0.9440 0.5840 0.9765 0.8407 0.6148
4
0.7247 0.9130 1 0.9589 0.4909 0.9966 0.8344 0.6628 1 0.1394 1 0.9925 0.4107 1 0.8325 0.5148
5
0.9193 0.9464 1 0.9962 0.5944 0.9966 0.8255 0.6703 1 0.1088 1 0.9925 0.3461 0.9966 0.7824 0.4794
6
C=5MOS results (Network.type, participant.type, JBuffer.type)
4 3 2 1 JBuffer
4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 participant Network
0.7096 0.9832 1 0.7228 0.6969 0.9966 0.8516 0.7546 0.9842 0.1258 1 0.9890 0.6339 0.9933 0.8134 0.6194 1
0.8014 0.9397 1 0.9516 0.6260 0.9966 0.9203 0.6766 1 0.1190 1 0.9927 0.5619 0.9966 0.7934 0.6383 2
0.9401 0.9297 1 1 0.5833 0.9966 0.8857 0.6853 0.9922 0.1190 1 1 0.5579 0.9966 0.8711 0.6292 3
0.5740 0.4046 0.9411 0.6666 0.6771 0.1939 0.8791 0.6691 0.9015 0.1190 0.9611 0.9259 0.4818 0.9632 0.7951 0.6379 4
0.5785 0.7892 1 0.5677 0.6492 0.5819 0.8531 0.6259 0.9590 0.1224 1 0.9741 0.3851 0.9966 0.7583 0.5387 5
0.6718 0.9632 1 0.5413 0.5985 0.9966 0.8888 0.6642 0.9920 0.1190 0.9880 0.9962 0.4310 0.9966 0.7185 0.5613 6
C=10MOS results (Network.type, participant.type, JBuffer.type)
4 3 2 1 JBuffer
4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 participant
Network
0.5862 0.8795
0.9954
0.9588
0.7142
0.3779
0.9219
0.7088
0.9568
0.1156
0.9786
0.9698
0.5934
0.9966
0.7151
0.6133 1
0.6750 0.9264
0.9945
0.7063
0.5714
0.9966
0.8914
0.6958
0.9770
0.1054
0.9717
0.9924
0.5121
0.9966
0.8494
0.6067 2
0.7457 0.7892
0.9941
0.9963
0.5752
0.9966
0.8911
0.6618
0.9918
0.1360
0.9940
0.9854
0.5087
0.9966
0.7951
0.5724 3
0.5615 0.2876
0.8325
0.6240
0.6511
0.0802
0.8487
0.6742
0.7460
0.0986
0.8571
0.8333
0.5630
0.9698
0.8405
0.6298 4
0.4062 0.7625
0.9953
0.5018
0.5169
0.2508
0.8826
0.6703
0.9473
0.1190
0.9664
0.9673
0.3984
0.9799
0.7337
0.4943 5
0.4112 0.9197
0.9611
0.5114
0.5804
0.3846
0.8971
0.6370
0.9606
0.1156
0.9942
0.9742
0.3529
0.9966
0.7751
0.4644 6
C=5, K=0.01, Network type=1
MOS results (Network.type, participant.type, JBuffer.type)
4 3 2 1 JBuffer
4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 participant
Network
0.9285
0.800
0.8846
0.800
0.4230
0.7906
0.5263
0.5161
0.4250
0.4029
0.3230
0.4821
0.3252
0.3993
0.4193
0.2741 1
C=50, K=10-150MOS results (Network.type, participant.type, JBuffer.type)
4 3 2 1 JBuffer
4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 participant Network
0.3307
0.0668
0.9004 0.5055 0.5238 0 0.8511 0.5858 0.6086 0.0307 0.6666 0.7397 0.2857 0.9832 0.7806 0.4963
1
0.3421
0.301 0.9766 0.5703 0.635 0 0.8232 0.6828 0.8018 0.0612 0.9651 0.8913 0.3423 0.9966 0.8391 0.4626
2
0.2941
0.2508
0.9759 0.7472 0.5658 0 0.8372 0.6629 0.7795 0.034 0.9681 0.854 0.2252 0.9966 0.8113 0.4501
3
0.32 0.2408
0.9521 0.5505 0.735 0 0.8723 0.7333 0.8449 0.0374 0.9605 0.8523 0.3875 0.9966 0.6982 0.4833
4
0.807 0.8695
0.9955 0.989 0.5913 0 0.7248 0.5526 0.9545 0.1084 0.9782 0.9779 0.8888 0.9966 0.895 0.8228
5
0.8217
0.9698
0.9951 0.9962 0.5203 0 0.7978 0.6397 0.9923 0.1224 0.9951 0.9923 0.7723 0.9966 0.9109 0.8339
6
Conclusions
- We can see that for each situation there is an algorithm that fits it, but there is no good algorithm for all Network and Participant types.
- The use of Algorithm will be done by the state of known factors in the Network and Participant with the use of the tables above
Summery
1. In this project we integrated a network simulation that fits better with realistic Network.
2. Upgraded the Jitter Buffer’s algorithm by dumping packets that came later then their successors and simulated a more realistic Time Of Arrival.
3. Generated a table that covers a vast variety of situations which the Jitter Buffer can choose an algorithm from