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Learning-based multi-relay selection for cooperative networks based on compressed sensing

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Abstract

Cooperative relaying is an effective technology to improve the secrecy capacity of physical-layer (PHY) security. Multiple relays can help further exploit the spatial diversity of cooperative networks. In power-limited networks, relay selection scheme is crucial important for it determines whether the optimal relay combination can be selected. This paper studies the problem of multi-relay selection in amplify-and-forward compressed sensing (AF-CS) networks, in which relays help all sources amplify and forward the signal, and the transmission matrix is used as the measurement matrix to encrypt the information. A self-organizing algorithm based on stochastic learning automata (SLA) is proposed for the AF-CS network to look for the best relay combination in a self-learning and self-optimizing way, and named “learning-based multi-relay selection algorithm” (L-MRS). In L-MRS, the destination node is self-optimizing to select the best state autonomously, and relays are self-organizing to decide whether to join the cooperation or not according to the environment feedback. Simulation studies verify the L-MRS’s is able to select the optimal relay-combination in a very stable way, and can get higher secrecy capacity compared with the coalition formation game method.

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References

  1. Khodakarami, H., & Lahouti, F. (2012). Link adaptation for physical layer security over wireless fading channels. IET Communications,6(3), 353–362.

    Article  MathSciNet  MATH  Google Scholar 

  2. Saad, W., Han, Z., Basar, T., Debbah, M., & Hjorungnes, A. (2009). Physical layer security: Coalitional games for distributed cooperation. In WiOPT 2009. 7th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks, (pp. 1–8). IEEE.

  3. Rachlin, Y., & Baron, D. (2008). The secrecy of compressed sensing measurements. In 2008 46th annual allerton conference on communication, control, and computing, (pp. 813–817). IEEE.

  4. Barcelo-Llado, J. E., Morell, A., & Seco-Granados, G. (2014). Amplify-and-forward compressed sensing as a physical-layer secrecy solution in wireless sensor networks. IEEE Transactions on Information Forensics and Security,9(5), 839–850.

    Article  Google Scholar 

  5. Fu, X., & Cui, Y. (2015). Multi-source and multi-relay cooperative system based on compressed sensing. Electronics Letters,51(22), 1828–1830.

    Article  Google Scholar 

  6. Krikidis, I. (2010). Opportunistic relay selection for cooperative networks with secrecy constraints. IET Communications,4(15), 1787–1791.

    Article  Google Scholar 

  7. Zhao, Y., Adve R., & Lim, T. J. (2006). Improving amplify-and-forward relay networks: optimal power allocation versus selection. In 2006 IEEE international symposium on information theory, IEEE.

  8. Michalopoulos, D. S. (2006). Wlc41-1: An optimized user selection method for cooperative diversity systems. In Global telecommunications conference, GLOBECOM’06IEEE, IEEE.

  9. Jing, Y., & Jafarkhani, H. A. (2008). Single and multiple relay selection schemes and their diversity orders. In ICC workshops’ 08. IEEE international conference oncommunications workshops, IEEE.

  10. Zhang, Z., Song, L., Han, Z., & Saad, W. (2014). Coalitional games with overlapping coalitions for interference management in small cell networks. IEEE Transactions on Wireless Communications,13(5), 2659–2669.

    Article  Google Scholar 

  11. Saad, W., Han, Z., Başar, T., Debbah, M., & Hjørungnes, A. (2011). Distributed coalition formation games for secure wireless transmission. Mobile Networks and Applications,16(2), 231–245.

    Article  Google Scholar 

  12. Saad, W., Han, Z., Debbah, M., & Hjorungnes, A. (2009). A distributed coalition formation framework for fair user cooperation in wireless networks. IEEE Transactions on Wireless Communications, 8(9).

  13. Bahbahani, M. S., Baidas, M. W., & Alsusa, E. (2015). A distributed political coalition formation framework for multi-relay selection in cooperative wireless networks. IEEE Transactions on Wireless Communications,14(12), 6869–6882.

    Article  Google Scholar 

  14. Thathachar, M. A. L. (1990). Stochastic automata and learning systems. Sadhana,15(4–5), 263–281.

    Article  MathSciNet  MATH  Google Scholar 

  15. Chen, Z., Lin, T., & Chunlin, Wu. (2016). Decentralized learning-based relay assignment for cooperative communications. IEEE Transactions on Vehicular Technology,65(2), 813–826.

    Article  Google Scholar 

  16. Barto, A., & Anandan, P. (1985). Pattern-recognizing stochastic learning automata. IEEE Transactions on Systems, Man, and Cybernetics,15(3), 360–375.

    Article  MathSciNet  MATH  Google Scholar 

  17. de Lope, J., Maravall, D., & Quinonez, Y. (2013). Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems. Robotics and Autonomous Systems,61(7), 714–720. (Collective and Social Autonomous Robots).

    Article  Google Scholar 

  18. Sharma, S., Shi, Y., Hou, Y., & Kompella, S. (2011). An optimal algorithm for relay node assignment in cooperative ad hoc networks. IEEE/ACM Transactions on Networking,19(3), 879–892.

    Article  Google Scholar 

  19. Yang, D., Fang, X., & Xue, G. (2011). OPRA: Optimal relay assignment for capacity maximization in cooperative networks. In Proceedings of the IEEE ICC, (pp. 1–6).

  20. Ding, C., et al. (2017). A game theoretic learning solution for distributed relay selection on throughput optimization. Wireless Networks,23(6), 1757–1766.

    Article  Google Scholar 

  21. Baraniuk, R. G. (2007). Compressive sensing [lecture notes]. IEEE Signal Processing Magazine,24(4), 118–121.

    Article  Google Scholar 

  22. Mayiami, M. R., Seyfe, B., & Bafghi, H. G. (2010). Perfect secrecy using compressed sensing. Mathematics.

  23. Guo, J., et al. (2015). Information capacity and sampling ratios for compressed sensing-based SAR imaging. IEEE Geoscience and Remote Sensing Letters,12(4), 900–904.

    Article  Google Scholar 

  24. Miller, Rob, & Trappe, Wade. (2012). On the vulnerabilities of CSI in MIMO wireless communication systems. IEEE Transactions on Mobile Computing,11(8), 1386–1398.

    Article  Google Scholar 

  25. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: MIT Press.

    MATH  Google Scholar 

  26. Papadimitriou, G. I. (1994). A new approach to the design of reinforcement schemes for learning automata: Stochastic estimator learning algorithms. IEEE Transactions on Knowledge and Data Engineering,6(4), 649–654.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Marine Science and Technology, Tianjin University, Tianjin, China

    Xiaomei Fu & Shuai Chang

  2. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Jialun Li

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  1. Xiaomei Fu
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  2. Jialun Li
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  3. Shuai Chang
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Correspondence to Shuai Chang.

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Fu, X., Li, J. & Chang, S. Learning-based multi-relay selection for cooperative networks based on compressed sensing. Wireless Netw 26, 3069–3081 (2020). https://doi.org/10.1007/s11276-019-02034-2

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