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Two-stage decision making policy for opportunistic spectrum access and validation on USRP testbed

Abstract

“Recently, various paradigms, for instance, device-to-device communications, LTE-unlicensed and cognitive radio based on an opportunistic spectrum access (OSA) are being envisioned to improve the average spectrum utilization. In OSA, secondary (unlicensed) users (SUs) need decision making policies (DMPs) to identify and transmit over optimum frequency bands without any interference to the primary (licensed) users as well as minimize the number of collisions among SUs. In this paper, we have proposed a two-stage DMP consisting of Bayesian Multi-armed Bandit algorithm to accurately characterize the frequency band statistics independently at each SU and frequency band selection scheme for orthogonalization of SUs. The analytical and simulation results show that the proposed DMP leads to 45% improvement in the average spectrum utilization compared to 36–39% in the existing DMPs. Furthermore, the number of collisions are 58.5% lower in the proposed DMP making SU terminals energy-efficient. The performance of the proposed DMP has been verified on the proposed USRP testbed in real radio environment and the experimental results closely match the simulated results .”

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Notes

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    Indicator function:\(\mathbf {1}_{\{logical expression\}}\) = 1 if logical expression = true; else 0.

References

  1. 1.

    Asadi, A., Wang, Q., & Mancuso, V. (2014). A survey on device-to-device communication in cellular networks. IEEE Communications Surveys and Tutorials, 16(4), 1801–1819.

  2. 2.

    Palicot, J., Zhang, H., & Moy, C. (2013). On the road towards green radio. URSI Radio Science Bulletin, 347, 40–56.

  3. 3.

    Ananadkumar, A., Michael, N., Tang, K., & Swami, A. (2011). Distributed algorithms for learning and cognitive medium access with logarithmic regret. IEEE Journal on Selected Areas in Communications, 29(4), 731–745.

  4. 4.

    Darak, S. J., Zhang, H., Palicot, H., & Moy, C. (2015). An efficient policy for D2D communications and energy harvesting in cognitive radios: Go Bayesian. In 23rd European Signal Processing Conference (EUSIPCO). France: Nice.

  5. 5.

    Kolodzy, P., et al. (2001). Next generation communications. Kickoff meeting, DARPA.

  6. 6.

    Chen, L., Iellamo, S., Coupechoux, M., & Godlewski, P. (2011). Spectrum auction with interference constraint for cognitive radio networks with multiple primary and secondary users. Wireless Networks, 17(5), 1355–1371.

  7. 7.

    Zhao, N., Yu, F. R., Sun, H., Yin, H., Nallanathan, A., & Wang, G. (2015). Interference alignment with delayed channel state information and dynamic AR-model channel prediction in wireless networks. Wireless Networks, 21(4), 1227–1242.

  8. 8.

    Zhao, N., Yu, F. R., Sun, H., & Li, M. (2016). Adaptive power allocation schemes for spectrum sharing in interference alignment (IA)-based cognitive radio networks. IEEE Transactions on Vehicular Technology, 65, 3700–3714.

  9. 9.

    Su, H., & Zhang, X. (2008). Cross-layer based opportunistic MAC protocols for QoS provisionings over cognitive radio wireless networks. IEEE Journal on Selected Areas in Communications, 26(1), 118–129.

  10. 10.

    Tumuluru, V. K., Wang, P., & Niyato, D. (2011). A novel spectrumscheduling scheme for multichannel cognitive radio network and performance analysis. IEEE Transactions on Vehicular Technology, 60(4), 1849–1858.

  11. 11.

    Rashid, M., Hossain, M., Hossain, E., & Bhargava, V. K. (2009). Opportunistic spectrum scheduling for multiuser cognitive radio: A queueing analysis. IEEE Transactions on Wireless Communications, 8(10), 5259–5269.

  12. 12.

    Zhao, Q., Tong, L., Swami, A., & Chen, Y. (2007). Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework. IEEE Journal on Selected Areas in Communications, 25(3), 589–600.

  13. 13.

    Zhao, Q., Krishnamachari, B., & Liu, K. (2008). On myopic sensing for multi-channel opportunistic access: Structure, optimality, and performance. IEEE Transactions on Wireless Communications, 7(12), 5431–5440.

  14. 14.

    Ahmad, S., Liu, M., Javidi, T., Zhao, Q., & Krishnamachari, B. (2008). Optimality of myopic sensing for multi-channel opportunistic access. IEEE Transactions on Information Theory, 55(9), 4040–4050.

  15. 15.

    Liu, K., Zhao, Q., & Krishnamachari, B. (2010). Dynamic multichannel access with imperect channel state detection. IEEE Transactions on Signal Processing, 58(5), 2795–2808.

  16. 16.

    Liu, K., & Zhao, Q. (2010). Distributed learning in cognitive radio networks: Multi-armed bandit with distributed multiple players. In Proceedings of the IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP) (pp. 3010–3013).

  17. 17.

    Liu, K., & Zhao, Q. (2010). Distributed learning in multi-armed with multiple player. IEEE Transactions on Signal Processing, 58(11), 5665–5681.

  18. 18.

    Zandi, M., Dong, M., & Grami, A. (2013). Decentralized spectrum learning and access adapting to primary channel availability distribution. In Proceedings on IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Darmstadt, Germany.

  19. 19.

    Gai, Y., & Krishnamachari, B. (2011). Decentralized online learning algorithms for opportunistic spectrum access. In Proceedings on IEEE Global Communication Conference (GLOBECOM) (pp. 1–6).

  20. 20.

    Gai, Y., & Krishnamachari, B. (2014). Distributed stochastic online learning policies for opportunistic spectrum access. IEEE Transactions on Signal Processing, 62(23), 6184–6193.

  21. 21.

    Darak, S. J., Zhang, H., Palicot, J., & Moy, C. (2017). Decision making policy for RF energy harvesting enabled cognitive radios in decentralized wireless networks. Digital Signal Processing, 60, 33–45.

  22. 22.

    Jouini, W., Ernst, D., Moy, C., & Palicot, J. (2011). Upper confidence bound algorithm for opportunistic spectrum access with sensing errors. In Proceedings of International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications, Osaka, Japan.

  23. 23.

    Auer, P., Cesa-Bianchi, N., & Fisher, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2), 236–256.

  24. 24.

    Kaufmann, E., Cappé, O., Garivier, A. (2011). On the efficiency of Bayesian bandit algorithms from a frequentist point of view. In Neural Information Processing Systems (NIPS).

  25. 25.

    Agrawal, S., Goyal, N (2013). Further optimal regret bounds for Thompson sampling. In 16th International Conference on Artificial Intelligence and Statistics (AISTATS), Scottsdale, USA.

  26. 26.

    Garivier, A., Cappé, O. (2011). The KL-ucb algorithm for bounded stochastic bandits and beyond. In Conference On Learning Theory (COLT) (pp. 359-376), Budapest, Hungary.

  27. 27.

    Lai, T., & Robbins, H. (1985). Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics, 6(1), 4–22.

  28. 28.

    Agrawal, R. (1995). Sample mean based index policies with O(log n) regret for the multi-armed bandit problem. Advances in Applied Probability, 27(4), 1054–1078.

  29. 29.

    Darak, S. J., Dhabhu, S., Moy, C., Zhang, H., Palicot, J., & Vinod, A. P. (2015). Low complexity and efficient dynamic spectrum learning and tunable bandwidth access for heterogeneous decentralized cognitive radio networks. Digital Signal Processing, 37, 13–23.

  30. 30.

    Lai, J., Dutkiewicz, E., Liu, R. P., & Vesilo, R. (2015). Opportunistic spectrum access with two channel sensing in cognitive radio networks. IEEE Transactions on Mobile Computing, 14(1), 126–138.

  31. 31.

    Darak, S. J., Nafkha, A., Moy, C., & Palicot, J. (2016). Is Bayesian multi-armed bandit algorithm superior?: Proof-of-concept for opportunistic spectrum access in decentralized networks. In Proceedings of 11th International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM) (pp. 104–115), Grenoble, France.

  32. 32.

    Bahamou, S., & Nafkha, A. (2013). Noise uncertainty analysis of energy detector: Bounded and unbounded approximation relationship. In 21th European Signal Processing Conference (EUSIPCO) (pp. 1–4), Marrakech, Morocco.

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Acknowledgements

The authors thank the Department of Science and Technology (DST), Government of India for the INSPIRE fellowship in support of this work.

Author information

Correspondence to Rohit Kumar.

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Kumar, R., Darak, S.J., Sharma, A.K. et al. Two-stage decision making policy for opportunistic spectrum access and validation on USRP testbed. Wireless Netw 24, 1509–1523 (2018). https://doi.org/10.1007/s11276-016-1420-y

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Keywords

  • Decentralized network
  • Dynamic spectrum learning and access
  • Opportunistic spectrum access
  • Multi-armed bandit
  • Decision making policy