Near-Optimal Throughput Performance in Cognitive Radio Driven Vehicular Networks

Öz

A cognitive radio driven vehicular network with a multichannel secondary user (SU) and many primary users is considered. A channel is allocated to each primary user for data transmission. SU is data backlogged and selects channels for transmission. SU knows neither channel states nor statistics of channel evolution processes. SU senses each of its selected communications channels to decide whether to use it for data transmission or not. It is assumed that sensing the channel takes 1 time slot. If SU senses that a good-state selected channel is in good state, it sends data over that channel at that time. Otherwise, SU do not use that channel and make another selection next time. Considering average throughput criteria, it is shown that the proposed policy is near-optimal for general channel evolution processes.

Anahtar Kelimeler

• Resource allocation
• opportunistic spectrum access
• wireless network
• cognitive radio

Bilişsel Radyo ile Etkin Taşıtsal Aglarda En İyiye Yakın Veri Hacmi Edimi

Öz

Bir çokkanallı ikincil kullanıcı (İK) ve çok sayıda birincil kullanıcılardan oluşan bir bilişsel radyo etkinleşmiş araçsal ağ incelenmektedir. Veriyi gönderim amacıyla her birincil kullanıcı için tahsis edilmiş bir kanal vardır. İK’nın verisi birikmiş ve iletisim amacıyla kanallar seçmektedir. Kanalların durumları ve evrimlerinin istatistikleriyle ilgili hiçbir bilgiye sahip degildir. İK seçtiği her haberleşme kanalını onunla verisini iletip iletmeyecegine karar verme amacıyla algılamaktadır. Bu kanal durumunu algılama için 1 zaman dilimi harcandığı kabul edilmiştir. Eğer İK o zaman diliminde iyi durumda bir kanalı seçerse, o kanal üzerinden ikincil alıcıya paket iletir. Eger o zaman diliminde kötü kanal seçerse o kanal üzerinden veri paketi göndermez ve başka bir kanalı seçmek için sonraki zaman dilimini bekler. Ortalama verihacmi kriterleri gözetilerek, önerilen politikanın genel kanal evrim süreçleri için eniyiye yakın oldugu gösterilmiştir.

Anahtar Kelimeler

• Kaynak tahsisi
• fırsatçı spektrum erişimi
• kablosuz ağlar
• bilişsel radyo

Kaynakça

1. Alsheikh, M. A., Hoang, D. T., Niyato, D., Tan, H., Lin, S. (2015). Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey. IEEE Communications Surveys and Tutorials vol 17, no. 3, 1239-1267.
2. Arapostathis, A., Borkar, V. S., Fernandez-gaucherand, E., Ghosh, M. K., and Marcus, S. I. (1993). Discrete-time controlled Markov processes with average cost criterion: A survey," SIAM J. Control Optim., vol. 31, no. 2, 282-344.
3. Bellman, R. E. (1957). Dynamic Programming. Princeton, N.J.: Princeton Univ. Press.
4. Biglieri, E., Caire, G., Taricco, G. (2000). Coding for the fading channel: a survey", Elsevier Signal Processing, vol. 80, 1135-1148.
5. Gul, O.M. (2014). A low-complexity, near-optimal scheduling policy for solving a restless multi-armed bandit problem occurring in a single-hop wireless network, MSc Thesis.
6. Gul, O.M. and Uysal-Biyikoglu, E. (2014). A Randomized Scheduling Algorithm for Energy Harvesting Wireless Sensor Networks Achieving Nearly 100% Throughput", IEEE Wireless Communication& Networking Conference (IEEE WCNC 2014), 6-9 April 2014, Istanbul, Turkey.
7. Gul, O. M., Demirekler. M. (2017). Average Throughput Performance of Myopic Policy in Energy Harvesting Wireless Sensor Network. Sensors, vol. 17, no. 10:2206.
8. Gul, O. M., Erkmen, M. A. (2016). Achieving asymptotically optimal throughput in centralized mobile robot networks without dispatching feedback. 28th European Conference on Operational Research (EURO 2016), July 3-6, 2016, Poznan, Poland.

9. Gul, O. M. (2017). Asymptotically Optimal Scheduling for Energy Harvesting Wireless Sensor Networks. 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2017), Montreal, QC, Canada, 1-7.
10. Gul, O. M., Demirekler, M. (2018). Asymptotically Throughput Optimal Scheduling Policy for Energy Harvesting Wireless Sensor Networks. IEEE Access, vol. 6, pp. 45004-45020.
11. Gul O. M. (Temmuz 2019). Achieving Near-Optimal Fairness in Energy Harvesting Wireless Sensor Networks", 24th Annual IEEE International Symposium on Computers and Communications (IEEE ISCC 2019), Barcelona, Spain, 1-6.
12. Gul, O. M. (Mart 2019). Average Throughput of Myopic Policy for Opportunistic Access over Block Fading Channels. IEEE Networking Letters, vol.1, no. 1, 38-41.
13. Gul, O. M. (Aralık 2020). Near-Optimal Data Communication Between Unmanned Aerial and Ground Vehicles. International Conference on Intelligent Systems Design and Applications (ISDA) 2020, 1-4.
14. Gul, O. M. (Haziran 2021, 1.). Fair Data Collection in Wireless Networks. IEEE Sinyal İşleme ve Uygulamaları (SIU) 2021, 1-4.
15. Gul, O. M. (Haziran 2021, 2.). Average Throughput Performance of Greedy Policy in Cognitive Radio Enabled Vehicular Networks. IEEE Sinyal İşleme ve Uygulamaları (SIU) 2021, 1-4.
16. Gul, O. M. (Ekim 2021). Near-Optimal Opportunistic Spectrum Access in Cognitive Radio Networks in IoT era. IEEE Conference on Local Computer Networks (LCN) 2021, 1-4.
17. Gul, O. M. (Mayıs 2022). Fair Data Collection in Wireless Communications Networks. IEEE Sinyal İşleme ve Uygulamaları (SIU) 2022, 1-4.
18. Gul, O. M., Kantarci. B. (Ekim 2022). Near optimal scheduling for opportunistic spectrum access over block fading channels in cognitive radio assisted vehicular network. Vehicular Communications, vol. 37, Oct. 2022, 100500.
19. Haykin, S. (2005). Cognitive radio: brain-empowered wireless communications. IEEE JSAC, vol. 23, no. 2, pp. 201-220.
20. Hero, A., Castanon, D., Cochran, D., Kastella, K. (2007). Foundations and Applications of Sensor Management, Chapter 6, Springer, US.
21. Johnston, M., Modiano, E., Keslassy, I. (2013). Channel Probing in Communication Systems: Myopic Policies Are not Always Optimal", IEEE International Symposium on Information Theory (ISIT), Istanbul, Turkey, 1934-1938.
22. Johnston, M., Modiano, E., Keslassy, I. (2017). Channel Probing in Opportunistic Communication Systems. IEEE Transactions on Information Theory, vol. 63, no. 11, 7535-7552.
23. Mansourifard, P., Javidi, T. and Krishnamachari, B. (2012). Optimality of myopic policy for a class of monotone affine restless multi-armed bandits", IEEE CDC.
24. Mitola, J. and Maguire, G. Q. (1999). Cognitive radio: making software radios more personal. IEEE Pers. Comm., vol. 6, no. 6, 13-18.
25. Papadimitriou, C. H. and Tsitsiklis, J. N. (1999). The complexity of optimal queueing network control. Math. Oper. Res., vol 24, 293-305.
26. Quyang, Y., Teneketzis, D. (2014). On the Optimality of Myopic Sensing in Multi-State Channels. IEEE Trans. on Inform. Theory, vol. 60, no. 1, 681-696.
27. Rappaport, T. S. (2002). Wireless Communications: Principles and Practice, 2nd Edition.
28. Reed, J. H. and Bostian, C. W. (2005). Understanding the issues in software defined cognitive radio, Tutorial at IEEE DySPAN 05.
29. Villar, S. S. (2016). Indexability and Optimal Index Policies for A Class of Reinitialising Restless Bandits. Prob. in Eng.&Inform. Sci., vol. 30, Iss. 01, 1-23.
30. Wang, K., Liu, Q., Li, F., Chen, L., Ma, X. (2015). Myopic policy for opportunistic access in cognitive radio networks by exploiting primary user feedbacks, IET Commun., Vol. 9, Iss. 7, 1017-1025.
31. Wang, K., Chen, L. Yu, J., and Zhang, D. (2016). Optimality of Myopic Policy for Multistate Channel Access. IEEE Communications Letters, vol. 20, no. 2, 300-303.
32. Wang, K., Chen, L. and Yu, J. (2016). On Optimality of Myopic Policy in Multi-channel Opportunistic Access. IEEE ICC 2016, 1-6.
33. Wang, K., Chen, L., Yu, J., Fan, Q., Zhang, Y., Chen, W., Zhou, P., Zhong, Y. (2018). On Optimality of Second-Highest Policy for Opportunistic Multichannel Access. IEEE Trans. Vehicular Technology, vol. 67, no. 12, 12013-12024.
34. Watkins, C. J., (1989). Learning from delayed rewards. Ph.D. dissertation, University of Cambridge, Psychology Dep.
35. Whittle, P. (1988). Restless bandits: Activity allocation in a changing world. J.Appl. Prob. 25A, 287-298.