Design a Cross-Layer Cognitive Engine using Cross-Layer Optimization - - PowerPoint PPT Presentation

▶
design a cross layer cognitive engine using cross layer
SMART_READER_LITE
LIVE PREVIEW

Design a Cross-Layer Cognitive Engine using Cross-Layer Optimization - - PowerPoint PPT Presentation

Dec 20, 2023 264 likes •677 views

Design a Cross-Layer Cognitive Engine using Cross-Layer Optimization with Case- Based Reasoning and Reinforcement Learning 4 th Workshop of COST Action IC0902 Rome, Italy 09 11, October, 2013 Ali Haider Mahdi, Andreas Mitschele-Thiel

slide-1
SLIDE 1

Design a Cross-Layer Cognitive Engine Page 1 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Design a Cross-Layer Cognitive Engine using Cross-Layer Optimization with Case- Based Reasoning and Reinforcement Learning

4th Workshop of COST Action IC0902 Rome, Italy

Ali Haider Mahdi, Andreas Mitschele-Thiel Ilmenau, Germany

09 – 11, October, 2013

slide-2
SLIDE 2

Design a Cross-Layer Cognitive Engine Page 2 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Motivation

  • Efficiently usage of spectrum
  • Avoiding interfering with PUs
  • Fast CR’s link adaptation according to channel behavior
  • Ensure QoS at CR
  • Autonomous reconfiguration
  • Speed up action process

– Fast convergence – Repeat previous actions – Re-evaluate actions

slide-3
SLIDE 3

Design a Cross-Layer Cognitive Engine Page 3 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Outline

  • State of the Art
  • Proposed approach:

– ADPSO – CBR – Q-Learning

  • Simulation scenario
  • Evaluation
  • Conclusion
slide-4
SLIDE 4

Design a Cross-Layer Cognitive Engine Page 4 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

State of the Art

Previous Work Adv. Disadv. Cognitive system monitor [1]

  • Using GA for optimum output
  • Using Learning from previous

action

  • Limited Cross-Layer capability
  • Physical layer objectives
  • Limited learning process

Learning and inference system [2]

  • Inference and learning based on

BN

  • No Cross-Layer capability
  • Single Objective (Bit Error Rate)
  • Single input parameter (SNR)

Access network [3]

  • Cross layer capability
  • L2 – L7 application
  • No learning from previous actions
  • FLC has difficult knowledge

acquisition Link adaptation [4]

  • Fast decision making
  • No Cross-Layer capability
  • No learning process
  • Physical layer objectives
slide-5
SLIDE 5

Design a Cross-Layer Cognitive Engine Page 5 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach

Environment 𝑂𝑂𝑂𝑂𝑂, 𝑀𝑂𝑂𝑂, Channels 𝑈𝑈𝑈𝑈𝑂𝑈𝑂𝑈 𝑞𝑂𝑞𝑂𝑈, Modulation scheme, Packet length Channel

Cognitive Radio

ADPSO CBR Q-L

  • Combination: ADPSO, CBR, Q-L

– ADPSO: proposes link configuration – CBR: selects previous link configuration – Q-L: Update previous link configuration’s score

ADPSO: Adaptive Discrete Particle Swarm Optimization CBR: Case-Based Reasoning Q-L: Q-Learning

slide-6
SLIDE 6

Design a Cross-Layer Cognitive Engine Page 6 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach: ADPSO

Environment 𝑂𝑂𝑂𝑂𝑂, 𝑀𝑂𝑂𝑂, Channels 𝑈𝑈𝑈𝑈𝑂𝑈𝑂𝑈 𝑞𝑂𝑞𝑂𝑈, Modulation scheme, Packet length Channel

Cognitive Radio

ADPSO

  • A. Mahdi, J. Mohanen, M. Kalil, A. Mitschele-Thiel, ”Adaptive Discrete Particle Swarm Optimization for Cognitive Radios”, IEEE ICC ’12, Ottawa,

Canada, June 2012.

  • A. Mahdi, M. Kalil, A. Mitschele-Thiel, ”Cross-Layer Optimization for Efficient Spectrum Utilization in Cognitive radios”, ICNC 2013, San Diego, USA,

January 2013.

slide-7
SLIDE 7

Design a Cross-Layer Cognitive Engine Page 7 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach: ADPSO

  • Adaptive Discrete Particle Swarm Optimization

(ADPSO):

– Divide fitness space into four regions – Modify the velocity coefficients (c1 , c2 , w) according to fitness value – Implements Elitist Learning Strategy (ELS)

Regions Fitness C1 C2 Jump-out

0 – 0.2

  • 0.1

+ 0.1

Exploration

0.2 - 0.4 + 0.1

  • 0.1

Exploitation

0.4 – 0.6 +0.05

  • 0.05

Convergence

0.6 – 1.0

  • 0.05

+ 0.05

𝑋(𝑔𝑂𝑈𝑈𝑂𝑂𝑂)=

1 (1+1.5×𝑓−2.6𝑔𝑔𝑔𝑔𝑔𝑔𝑔)

slide-8
SLIDE 8

Design a Cross-Layer Cognitive Engine Page 8 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach: CBR

  • Case-Based Reasoning (CBR):

– Implement past experience – Speed up convergence – Reduce computation efforts

Environment 𝑂𝑂𝑂𝑂𝑂, 𝑀𝑂𝑂𝑂, Channels 𝑈𝑈𝑈𝑈𝑂𝑈𝑂𝑈 𝑞𝑂𝑞𝑂𝑈, Modulation scheme, Packet length Channel

Cognitive Radio

ADPSO CBR

  • A. Mahdi, M. Kalil, A. Mitschele-Thiel, ”Dynamic Packet Length Control for Cognitive Radio Networks”, VTC2013-Fall, Las Vegas, USA, September 2013.
slide-9
SLIDE 9

Design a Cross-Layer Cognitive Engine Page 9 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach: Q-Learning

  • Q-Learning(Q-L):

– Study the history of channels – Learn appropriate action

Environment 𝑂𝑂𝑂𝑂𝑂, 𝑀𝑂𝑂𝑂, Channels 𝑈𝑈𝑈𝑈𝑂𝑈𝑂𝑈 𝑞𝑂𝑞𝑂𝑈, Modulation scheme, Packet length Channel

Cognitive Radio

ADPSO CBR Q-L

slide-10
SLIDE 10

Design a Cross-Layer Cognitive Engine Page 10 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach: Q-Learning

  • Q-L:

– Select similar previous (state-decision) pairs

Ch Noise Loss Pt M L fitness 4

  • 100

90 25 QPSK 600 0.75 2

  • 110

85 20 8PSK 700 0.78 1

  • 95

88 30 8PSK 850 0.82

slide-11
SLIDE 11

Design a Cross-Layer Cognitive Engine Page 11 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach: Q-Learning

  • Q-L:

– Select similar previous (state-decision) pairs – Evaluate current fitness at Tx and Rx

Ch Noise Loss Pt M L fitness 4

  • 100

90 25 QPSK 600 0.75 2

  • 110

85 20 8PSK 700 0.78 1

  • 95

88 30 8PSK 850 0.82

slide-12
SLIDE 12

Design a Cross-Layer Cognitive Engine Page 12 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach: Q-Learning

  • Q-L:

– Select similar previous (state-decision) pairs – Evaluate current fitness at Tx and Rx – Update total fitness + Rewards

Ch Noise Loss Pt M L fitness 4

  • 100

90 25 QPSK 600 0.75 2

  • 110

85 20 8PSK 700 0.78 1

  • 95

88 30 8PSK 850 0.82 0.85 0.82 0.7

slide-13
SLIDE 13

Design a Cross-Layer Cognitive Engine Page 13 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Proposed approach: Q-Learning

  • Q-L:

– Select similar previous (state-decision) pairs – Evaluate current fitness at Tx and Rx – Update total fitness – Select best decision (higher fitness)

Ch Noise Loss Pt M L fitness 2

  • 110

85 20 8PSK 700 0.78 0.85

slide-14
SLIDE 14

Design a Cross-Layer Cognitive Engine Page 14 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Simulation Scenario

Simulation parameters:

Parameter Value

  • No. of channels

5 Channel bandwidth 100 kHz CCC 1 PU arrival 0.1 -1.5 ms Noise (dBm)

  • 85 to -100

Path loss (dB) 80 to 90

  • Min. Data Rate (kbps)

100

  • Max. Bit Error Rate

10-4 Transmit power (dBm) 0 - 25 Modulation scheme PSK Modulation index 1, 2, 3, 4 Packet length (Byte) 100 - 1000 Environmental Inputs QoS Req. Outputs

slide-15
SLIDE 15

Design a Cross-Layer Cognitive Engine Page 15 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Simulation scenario (cont.)

Metrics:

  • Total fitness

𝑔

1 - maximum achievable throughput

𝑔

2 - minimum achievable delay

𝑔

3 - channel availability

𝑔

4 - packet loss probability

𝑞1 = 0.7, 𝑞2 = 0.1, 𝑞3 = 0.1 , 𝑞4 = 0.1

𝑔

𝑢𝑢𝑢𝑢𝑢 = 𝑞1𝑔 1 + 𝑞2𝑔 2 + 𝑞3𝑔 3 + 𝑞4𝑔 4

[0,1]

  • Signaling overhead
  • Throughput
  • Channel usage
slide-16
SLIDE 16

Design a Cross-Layer Cognitive Engine Page 16 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Evaluation: Throughput

slide-17
SLIDE 17

Design a Cross-Layer Cognitive Engine Page 17 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Evaluation: Signaling overhead

slide-18
SLIDE 18

Design a Cross-Layer Cognitive Engine Page 18 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Evaluation: Channel Usage

slide-19
SLIDE 19

Design a Cross-Layer Cognitive Engine Page 19 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Conclusion

  • Efficient algorithm for dynamic environment
  • Fast autonomous link adaptation
  • Low signaling overhead
  • Higher throughput
  • Best channel selection
slide-20
SLIDE 20

Design a Cross-Layer Cognitive Engine Page 20 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

References

[1] C. Reiser, “Biologically inspired Cognitive radio Engine model Utilizing Distributed Genetic Algorithm for Secure and Robust Wireless Communications and Networking”, Ph.D thesis, Virginia University, 2004. [2] Y. Huang, J. Wang, H. Jiang, “Modeling of Learning Inference and Decision Making Engine in Cognitive Radio”, Second International Conference on Network Security, Wireless Communications, and Trusted Computing, 2010. [3] N. Baldo, M. Zorzi, “Cognitive Network Access using Fuzzy Decision Making”, IEEE Transactions on Wireless Communications, Vol. 8, No. 7, July 2009. [4] Z. Zhao, S. Xu, S. Zheng, and J. Shang, “Cognitive radio adaptation using particle swarm optimization,” Wireless Communications & Mobile Computing, vol. Volume 9,

  • no. 7, pp. 875–881, July 2009.
slide-21
SLIDE 21

Design a Cross-Layer Cognitive Engine Page 21 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Thank you for your attention Questions?? Comments!!!

  • MSc. Ali Haider Mahdi

Integrated Communication Systems Group International Graduate School on Mobile Communications Technische Universität Ilmenau

Tel: +49 (0)3677 69 4133 E-mail: ali.mahdi@tu-ilmenau.de Website: www.tu-ilmenau.de/ics www.gs-mobicom.de

slide-22
SLIDE 22

Design a Cross-Layer Cognitive Engine Page 22 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Optimization (cont.)

  • Evaluation:

Minimum BER Maximum Rb

  • A. Mahdi, J. Mohanen, M. Kalil, A. Mitschele-Thiel, ”Adaptive Discrete Particle Swarm Optimization for Cognitive Radios”, IEEE ICC ’12, Ottawa,

Canada, June 2012.

slide-23
SLIDE 23

Design a Cross-Layer Cognitive Engine Page 23 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Optimization (cont.)

  • Evaluation:

Signaling overhead Spectrum utilization

  • A. Mahdi, M. Kalil, A. Mitschele-Thiel, ”Cross-Layer Optimization for Efficient Spectrum Utilization in Cognitive radios”, ICNC 2013, San Diego, USA,

January 2013.

slide-24
SLIDE 24

Design a Cross-Layer Cognitive Engine Page 24 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Reasoning (cont.)

  • Evaluation:
  • A. Mahdi, M. Kalil, A. Mitschele-Thiel, ”Dynamic Packet Length Control for Cognitive Radio Networks”, VTC2013-Fall, Las Vegas, USA, September 2013.
slide-25
SLIDE 25

Design a Cross-Layer Cognitive Engine Page 25 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Evaluation: Fitness value

slide-26
SLIDE 26

Design a Cross-Layer Cognitive Engine Page 26 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Evaluation: Data Rate

slide-27
SLIDE 27

Design a Cross-Layer Cognitive Engine Page 27 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

State of the Art

  • Genetic Algorithm (GA) for link adaptation [1]

– Only for static environment – No PU activities model

  • Energy-efficient packet size optimization[2]

– Fixed packet size – redundant retransmissions – Only for static environment

  • Adaptive Discrete Particle Swarm Optimization (ADPSO)

– Algorithm run for every environmental change – New configuration for every packet

 Need to new approach

[1] T. R. Newman, B. A. Barker, A. M. Wyglinski, A. Agah, J. B. Evans, and G. J. Minden, “Cognitive engine implementation for wireless multicarrier transceivers,” Wireless Communications and Mobile Computing, vol. 7, no. 9, pp. 1129–1142, November 2007. [2] M. Oto and O. Akan, “Energy-efficient packet size optimization for cognitive radio sensor networks,” IEEE Transactions on Wireless Communications, vol. 11,

  • no. 4, pp. 1544–553, April 2012.
slide-28
SLIDE 28

Design a Cross-Layer Cognitive Engine Page 28 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 28

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

28

Evaluation1: Link config.

slide-29
SLIDE 29

Design a Cross-Layer Cognitive Engine Page 29 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 29

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

29

Evaluation1: Link configuration

slide-30
SLIDE 30

Design a Cross-Layer Cognitive Engine Page 31 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Time plan

Practical implementation Writing dissertation

October 2013 – May 2014

Q-L in CE Simulate CE in OMNeT++ Practical implementation (in progress) Publications: 1 accepted 2 planned to submit

April 2013 – September 2013

Poster at Sophia Antipolis

slide-31
SLIDE 31

Design a Cross-Layer Cognitive Engine Page 33 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 33

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

33

  • Objectives [1,2]
  • High Throughput 𝑔𝑈𝑈𝑈

𝑛𝑢𝑛 =

𝑀 𝑀+𝑃(1−𝐶𝐶𝐶)𝑀+𝑃𝐶𝑐 𝑀𝑛𝑛𝑛 𝑀𝑛𝑛𝑛+𝑃 𝐶𝑐𝑛𝑛𝑛

  • Low Transmission delay 𝑔𝑒𝑂𝑒𝑈𝑒𝑛𝑛𝑛 =

1 𝑆𝑐 𝑛𝑛𝑛 𝑀𝑛𝑔𝑔 1 𝑆𝑐 𝑀

Scenarios

slide-32
SLIDE 32

Design a Cross-Layer Cognitive Engine Page 34 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

Similarity

  • Input:

– Noise  Normalization  Norm(N) – Loss  Normalization  Norm(L)

  • Previous states:

– Noise-t  Normalization  Norm(N-t) – Loss-t  Normalization  Norm(L-t)

  • Euclidean Distance (ED)

= (𝑂𝑂𝑈𝑈 𝑂 − 𝑂𝑂𝑈𝑈 𝑂−𝑢 )2+(𝑂𝑂𝑈𝑈 𝑀 − 𝑂𝑂𝑈𝑈(𝑀−𝑢)2

  • Similarity

= 1 − 𝐹𝐹

slide-33
SLIDE 33

Design a Cross-Layer Cognitive Engine Page 35 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

PU activities model

  • Generate exponential random variables[2]

– IDLE 𝑂𝑛 – BUSY 𝑂𝑐

  • Mean of the exponential random variables

– 𝑈𝑛 = 𝑈𝑂𝑈𝑈(𝑂𝑛) – 𝑈𝑐 = 𝑈𝑂𝑈𝑈(𝑂𝑐)

  • Probability of PU state:

– Probability of IDLE

  • 𝑄𝑈

𝑛𝑗𝑢𝑓 = 𝑛𝑔 𝑛𝑔+𝑛𝑐

– Probability of BUSY

  • 𝑄𝑈

𝑐𝑐𝑐𝑐 = 𝑛𝑐 𝑛𝑔+𝑛𝑐

[2] M. Oto and O. Akan, “Energy-efficient packet size optimization for cognitive radio sensor networks,” IEEE Transactions on Wireless Communications, vol. 11, no. 4, pp. 1544–553, April 2012.

slide-34
SLIDE 34

Design a Cross-Layer Cognitive Engine Page 36 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 36

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

36

  • Rx sends to Tx over CCC

– ACK/ NACK according to PER – Rx sends current environmental factors (Noise, Loss) – Current free channels

Proposed approach (cont.)

Frame control Duration ID RA ACK/N ACK Noise Loss Free channels FCS

CCC:Common Control Channel

slide-35
SLIDE 35

Design a Cross-Layer Cognitive Engine Page 37 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 37

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

37

ACK , Noise, Loss, Ch ACK , Noise, Loss, Ch Ch, Pt, M, L Packet Ch, Pt , M , L Run ADPSO Data channel

Proposed approach

ADPSO

Common Control Channel ACK , Noise, Loss, Ch Packet ACK

Cognitive Radio (Tx)

Achieve the requirements Ch, Pt , M , L

CBR

Cognitive Engine

Configure CR

Cognitive Radio (Rx)

Cognitive Engine

slide-36
SLIDE 36

Design a Cross-Layer Cognitive Engine Page 38 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 38

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

38

Evaluation 2: Signaling overhead

slide-37
SLIDE 37

Design a Cross-Layer Cognitive Engine Page 39 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 39

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

39

Conclusion

  • Efficient approach in time and signaling overhead for dynamic environment
  • Works under different scenarios
  • Achievements:
  • Dynamic link configuration
  • High convergence values (Fitness values)
  • Low time
  • Low signaling overhead
slide-38
SLIDE 38

Design a Cross-Layer Cognitive Engine Page 42 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 42

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

42

𝑔

𝑢𝑢𝑢𝑢𝑢 = 𝑞1𝑔 𝑈𝑈𝑈 + 𝑞2𝑔 𝑞𝑢𝑞𝑓𝑈 + 𝑞3𝑔 𝐶𝐶𝐶 + 𝑞4𝑔 𝑗𝑓𝑢𝑢𝑐

Simulation

Weights M1 M2 M3 w1 0.1 0.1 0.8 w2 0.1 0.1 0.1 w3 0.1 0.1 0.1 w4 0.7 0.7 Scenario Rb(kbps) BER Voice 64 10-3 Video 500 10-4 Data 300 10-6

Parameter Value Transmit power (dBm) 0 - 25 Modulation scheme PSK Modulation index 0, 1, 2, 3, 4 Packet length (Byte) 100 - 1000 Code rate 1/2 - 7/8 Channel type AWGN

  • No. of Free channels

16 Bandwidth of channel 50 kHz Noise (dBm)

  • 85

Path loss (dB) 90

slide-39
SLIDE 39

Design a Cross-Layer Cognitive Engine Page 43 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 43

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

43

Weights and QoS requirements

Link’s objectives:

  • High throughput (𝑔

1)

  • Low transmission delay (𝑔

2)

  • Low BER (𝑔

3)

  • Low power consumption (𝑔

4)

Total fitness:

  • 𝑔

𝑢𝑢𝑢𝑢𝑢 = ∑

𝑞𝑛𝑔

𝑛 4 𝑛=1

Weights Voice Video Data w1 0.1 0.1 0.8 w2 0.1 0.1 0.1 w3 0.1 0.1 0.1 w4 0.7 0.7 Scenario Rb(kbps) BER Voice 64 10-3 Video 500 10-4 Data 300 10-6

slide-40
SLIDE 40

Design a Cross-Layer Cognitive Engine Page 44 Ali Haider Mahdi Integrated Communication Systems Group www.tu-ilmenau.de/ics

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated HW/SW Systems Group Self-Organization 10 October 2013 44

  • Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group www.tu-ilmenau.de/ics Dynamic Link Configuration for Cognitive Radio Networks

  • Ali Haider Mahdi-

44

Results: Convergence in voice scenario

Weights W1 W2 W3 W4 Voice 0.1 0.7 0.1 0.1

Weights and GA model are in [2]