Wireless Networks

, Volume 24, Issue 5, pp 1509–1523 | Cite as

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

  • Rohit Kumar
  • Sumit J. Darak
  • Ajay K. Sharma
  • Rajiv Tripathi


“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 .”


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



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


  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.CrossRefGoogle Scholar
  2. 2.
    Palicot, J., Zhang, H., & Moy, C. (2013). On the road towards green radio. URSI Radio Science Bulletin, 347, 40–56.Google Scholar
  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.CrossRefGoogle Scholar
  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.Google Scholar
  5. 5.
    Kolodzy, P., et al. (2001). Next generation communications. Kickoff meeting, DARPA.Google Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefzbMATHGoogle Scholar
  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.MathSciNetCrossRefGoogle Scholar
  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).Google Scholar
  17. 17.
    Liu, K., & Zhao, Q. (2010). Distributed learning in multi-armed with multiple player. IEEE Transactions on Signal Processing, 58(11), 5665–5681.MathSciNetGoogle Scholar
  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.Google Scholar
  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).Google Scholar
  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.MathSciNetCrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.Google Scholar
  23. 23.
    Auer, P., Cesa-Bianchi, N., & Fisher, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2), 236–256.zbMATHGoogle Scholar
  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).Google Scholar
  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.Google Scholar
  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.Google Scholar
  27. 27.
    Lai, T., & Robbins, H. (1985). Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics, 6(1), 4–22.MathSciNetCrossRefzbMATHGoogle Scholar
  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.MathSciNetCrossRefzbMATHGoogle Scholar
  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.CrossRefGoogle Scholar
  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.CrossRefGoogle Scholar
  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.Google Scholar
  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.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Rohit Kumar
    • 1
  • Sumit J. Darak
    • 2
  • Ajay K. Sharma
    • 3
  • Rajiv Tripathi
    • 1
  1. 1.Electronics and Communication Engineering DepartmentNational Institute of Technology DelhiNew DelhiIndia
  2. 2.Electronics and Communication Engineering DepartmentIndraprastha Institute of Information Technology DelhiNew DelhiIndia
  3. 3.Computer Science and Engineering DepartmentNational Institute of Technology DelhiNew DelhiIndia

Personalised recommendations