1. Reliable Multimedia Transmission over Cognitive Radio Networks Using Fountain Codes HarikeshwarKushwaha, Student Member IEEE, Yiping Xing, Student Member IEEE, RajarathnamChandramouli, Senior Member IEEE, and Harry Heffes, Fellow IEEE Proceedings of the IEEE | Vol. 96, No. 1, January 2008

2. Outline • Introduction • System Model • Spectrum Pooling Concept • Digital Fountain Codes • Primary User Arrival Model • Coding Scheme for Scalable Multimedia Applications • Problem description • Subchannel Selection • An Analytical Expression for : Analysis I &II • Simulation Result • Conclusion

3. Introduction • Wireless multimedia applications require • significant bandwidth and • satisfying relatively tight delay constraints. • Radio spectrum is a scarce resource. • spectrum has already been allocated. • Secondary spectrum access(FCC) • dynamic access to the unused parts of the spectrum owned by the primary license holder • facilitated by Cognitive radios

4. Introduction

5. Spectrum Pooling Concept

6. Spectrum Pooling Concept • A link is composed of multiple different SCs at different frequencies. • achieving distributed streaming • Reliable • But, the coordination required between the SCs.

7. Digital Fountain Codes • No need of coordination required between the SCs. • Robust against the packet loss caused by the PU interference and other channel conditions.

8. Primary User Arrival Model

9. Primary User Arrival Model ：capacity of ：loss probability of ：arrival rate of ：inter-arrival time ：maximum tolerable delay

10. Coding Scheme for Scalable Multimedia Applications

11. Problem description • PU interference is high so that decoder does not receive N packet within resulting a decoding error. • Increase the number of • Redundancy X is used to compensate for the loss due to PU interference. • Spectral efficiency How many?

12. Problem description • Model the arrival of the primary traffic as a Poisson process. • Markov chain Poisson Process • Assume that the available channel set and parameters and are known to the secondary devices. 1.The primary traffic is more dynamic and varying fast. 2. are less correlation and dissimilar.

13. Subchannel Selection • A channel is good if • PU arrives after • Packet lossless • Define a metric to measure the quality of the • If we require S of the from the pool of , just select first S from the pool. • S depends on the required

14. An Analytical Expression for : Analysis I

15. An Analytical Expression for : Analysis I

16. An Analytical Expression for : Analysis I

17. An Analytical Expression for : Analysis II • More ideal • The channel is free of error. • packet per GOP. • Total time needed to receive packets is

18. An Analytical Expression for : Analysis II ：a configuration such that exactly r of the out of S are less than . ：a random variable denoting the total time in .

19. An Analytical Expression for : Analysis II Now by combining (9), (10), (12), and (19), can be computed.

20. Simulation Result • Assumptions： • Packets are lost only due to PU arrival, losses due to other channel conditions are not considered. • There is no secondary user arrival. • Once an SC is lost due to PU arrival, it is considered unusable during that GOP duration, i.e. , even if the PU leaves the channel after sometime, no further transmission is done until the next GOP. • PU mean arrival time is comparable to the maximum tolerable delay().

21. Simulation Result - Parameterization • 1) Estimation of Number of Required Subchannels: • R = 1000 packets= 1 • = 15 • = [0.3 0.2 0.1 0.25 0.36 0.4 0.6 0.24 0.32 0.15 • 0.25 0.36 0.4 0.6 0.24] • = [0.03 0.04 0.01 0.02 0.05 .025 0.06 0.01 0.03 0.015 0.04 0.01 0.02 0.05 .025]. For example： = 6500 fountain coded packets to recover an original K = 6000 packets with high probability(i.e., ) then at least S = 10 subchannels are needed, as shown in Fig.10.

22. Simulation Result - Parameterization

23. Simulation Result - Parameterization • 2) LT Codes(ideal case)：robust soliton distribution

24. Simulation Result - Parameterization • 3) Delay and Other Requirements: = 10 MbpsW = 100 kHz = 200ms= 10ms = 1000 bits per packet

25. Simulation Result - Parameterization = 10 MbpsW = 100 kHz = 200ms= 10ms = 1000 bits per packet

26. Simulation Result - Parameterization = 10 MbpsW = 100 kHz = 200ms= 10ms = 1000 bits per packet

27. Simulation Result – Performance Analysis • Observation in Figs. 12-14： • Dependence on XFor each value of S, attains a maximum value at a specific value of X . • Dependence on S ηattains another maximum at a specific value of S. • Dependence on λ η decreases as λincreases

28. Conclusion • Propose a scheme for the transmission of distributed multimedia applications over cognitive radio networks with the help of digital fountain codes. • For a givenλand number of original packets K, we can find out the value of the optimum number of SCs and the overhead X that give maximum spectral efficiency.