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A Cooperative Strategy for Trustworthy Relay Selection in CR Network: A Game-Theoretic Solution

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Abstract

The non-symmetric type of game is Stackelberg, where one player or a specific set of players has the advantage of making moves ahead of the other players. These games are being used to aid administrative decisions in telecommunications and computational systems. Stackelberg games have increasingly been helpful in systems where security is a key decision consideration. The authors have suggested a cooperative and novel framework for selection of relays within Cognitive Radio (CR) System utilizing Stackelberg game-theoretic approach through this correspondence. The cooperation is based on the trustworthiness of the Secondary Users (SUs) while getting selected as a relay. Additionally, the strategy of the Secondary Transmitter (ST) is always to increase its own utility function by selecting the best available SU (as a relay) satisfying the rules of the Stackelberg game. The Nash equilibrium strategies for Secondary Relays (SR) have been analyzed in this paper at the suggested approach, as well as the Stackelberg competitive game with successive moves has been justified in the process. To our experience, no work on the trustworthy selection process of the relay to aid transmission of secondary users in the CR network using the game-theoretic method by Stackelberg has yet been published. Results obtained from simulation confirm that ST, as well as SR, gain more utilities if compared with the non-cooperative transmission scheme. Further, the relay which gets selected in this process even reduces the secondary outage loss in comparison to few non-game-theoretic approaches.

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Abbreviations

PT, ST:

Primary transmitter, secondary transmitter

\(P_{PT}\), \(P_{ST}\) :

Transmission power of PT and ST

\(r_{PT}\), \(r_{ST}\) :

Data rate of PT and ST

\(SR_{i}\) :

‘i’ th Secondary Relay

K:

Total number of relay nodes

\(\Re\) :

Set of secondary relays \(\Re = \left\{ {\left. {SR_{i} } \right| \, i = 1, \, 2,{ 3 }\ldots \, K} \right\}\)

\(c_{u, \, v}\) :

Channel impulse response of the link between u and v nodes

\(\alpha_{u, \, v}\) :

Instantaneous channel gain between nodes u and v

\(\Phi_{D}\) :

Decoding set \(\Phi_{D} \, = \, \left\{ {\varphi \, \left| { \, \varphi \, \in \, \phi \, \cup \varphi_{{\text{K }}} {\text{ , k}} = { 1, 2 }\ldots{, 2}^{{\text{K}}} - 1} \right.} \right\}\)

\(\gamma\) :

Path loss coefficient

\(d_{u, \, v}\) :

Path length connecting elements u and v

\({\text{g}}_{{u,{\text{ v}}}}\) :

Fading coefficient with zero-mean

\(U_{ST} ,{\text{U}}_{{{\text{SR}}}}\) :

Utility of ST and SR

\({\text{I}}()\) :

Status of packet delivery

\({\text{p}}_{{{\text{SR}}_{{\text{i}}} }}\) :

Packet forwarding probability

\(\omega_{{SR_{i} }}\) :

Willingness of \(SR_{i}\) node to cooperate in a transmission

\({\text{f}}_{{{\text{SD}}}}^{{\text{i}}}\) :

Feedback from SD for ‘i’ th relay’s packet delivery

\(\mu\), \(\nu\) :

Observed number of well and misbehavior over the time T

\(\Gamma_{{{\text{SR}}_{{\text{i}}} }}\) :

Trust factor of \(SR_{i}\)

\({\text{ C}}_{{\text{V}}}\) :

Equivalent cost of achieving +ve trust factor

\(\Im_{{{\text{SR}}_{{\text{i}}} }}\) :

Trust utility

\(\eta_{{{\text{thr}}}}\) :

Predefined outage threshold

References

  1. FCC, (2003). ET Docket No 03–322 Notice of Proposed Rule Making and Order, Dec 2003.

  2. Akyildiz, I. F., Wang, X., & Wang, W. (2005). Wireless mesh networks: A survey. Computer Networks, 47(4), 445–487.

    Article  Google Scholar 

  3. Akyildiz, I. F., Lee, W. Y., & Chowdhury, K. R. (2009). CRAHNs: Cognitive radio ad hoc networks. AD hoc Networks, 7(5), 810–836.

    Article  Google Scholar 

  4. Banerjee, J. S., Chakraborty, A., & Chattopadhyay, A. (2021). A decision model for selecting best reliable relay queue for cooperative relaying in cooperative cognitive radio networks: The extent analysis based fuzzy AHP solution. Wireless Networks, 27(4), 2909–2930.

  5. Banerjee, J. S., Chakraborty, A., & Chattopadhyay, A. (2021). A hybrid double layered technique for the best reliable and optimal relay selection in cooperative CR systems based on M-AHP and grey relational analysis. Journal of The Institution of Engineers (India): Series B. Springer.

  6. Banerjee, J. S., Chakraborty, A., & Chattopadhyay, A. (2017). Fuzzy based relay selection for secondary transmission in cooperative cognitive radio networks. In Proceedings of advances in optical science and engineering (pp. 279–287).

  7. Zhang, N., Lu, N., Lu, R., Mark, J. W., & Shen, X. (2012). Energy-efficient and trust-aware cooperation in cognitive radio networks. In Proceedings of the 2012 IEEE international conference on communications (ICC) (pp. 1763–1767). IEEE.

  8. Bletsas, A., Khisti, A., Reed, D. P., & Lippman, A. (2006). A simple cooperative diversity method based on network path selection. IEEE Journal on Selected Areas in Communications, 24(3), 659–672.

    Article  Google Scholar 

  9. Beres, E., & Adve, R. (2008). Selection cooperation in multi-source cooperative networks. IEEE Transactions on Wireless Communications, 7(1), 118–127.

    Article  Google Scholar 

  10. Ikki, S. S., & Ahmed, M. H. (2010). Performance analysis of adaptive decode-and-forward cooperative diversity networks with best-relay selection. IEEE Transactions on Communications, 58(1), 68–72.

    Article  Google Scholar 

  11. Simeone, O., Bar-Ness, Y., & Spagnolini, U. (2007). Stable throughput of cognitive radios with and without relaying capability. IEEE Transactions on Communications, 55(12), 2351–2360.

    Article  Google Scholar 

  12. Banerjee, J. S., & Karmakar, K. (2012). A comparative study on cognitive radio implementation issues. International Journal of Computer Applications, 45(15), 44–51.

    Article  Google Scholar 

  13. Chakraborty, A., & Banerjee, J. S. (2013). An advance Q learning (AQL) approach for path planning and obstacle avoidance of a mobile robot. International Journal of Intelligent Mechatronics and Robotics (IJIMR), 3(1), 53–73.

    Article  Google Scholar 

  14. Banerjee, J. S., Chakraborty, A., & Karmakar, K. (2013). Architecture of cognitive radio networks. In Cognitive radio technology applications for wireless and mobile ad hoc networks (pp. 125–152).

  15. Banerjee, J. S., & Chakraborty, A. (2014). Modeling of software defined radio architecture and cognitive radio: The next generation dynamic and smart spectrum access technology. In Cognitive radio sensor networks: Applications, architectures, and challenges (pp. 127–158).

  16. Banerjee, J. S., & Chakraborty, A. (2015). Fundamentals of software defined radio and cooperative spectrum sensing: a step ahead of cognitive radio networks. In Handbook of research on software-defined and cognitive radio technologies for dynamic spectrum management (pp. 499–543).

  17. Lu, R., Li, X., Liang, X., Shen, X., & Lin, X. (2011). GRS: The green, reliability, and security of emerging machine to machine communications. IEEE Communications Magazine, 49(4), 28–35.

    Article  Google Scholar 

  18. Chakraborty, A., Banerjee, J. S., & Chattopadhyay, A. (2020). Malicious node restricted quantized data fusion scheme for trustworthy spectrum sensing in cognitive radio networks. Journal of Mechanics of Continua and Mathematical Sciences, 15(1), 39–56.

    Google Scholar 

  19. Marti, S., Giuli, T. J., Lai, K., & Baker, M. (2000). Mitigating routing misbehavior in mobile ad hoc networks. In Proceedings of the 6th annual international conference on Mobile computing and networking (pp. 255–265).

  20. He, Q., Wu, D., & Khosla, P. (2004). SORI: A secure and objective reputation-based incentive scheme for ad-hoc networks. In Proceedings of the 2004 IEEE wireless communications and networking conference (IEEE Cat. No. 04TH8733) (Vol. 2, pp. 825–830). IEEE.

  21. Urpi, A., Bonuccelli, M., & Giordano, S. (2003). Modelling cooperation in mobile ad hoc networks: a formal description of selfishness. In Proceedings of the WiOpt'03: Modeling and optimization in mobile, ad hoc and wireless networks (pp. 10).

  22. Srinivasan, V., Nuggehalli, P., Chiasserini, C. F. and Rao, R. R. (2003). Cooperation in wireless ad hoc networks. In Proceedings of the IEEE INFOCOM 2003, Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No. 03CH37428) (Vol. 2, pp. 808–817). IEEE.

  23. Kelly, F. P., Maulloo, A. K., & Tan, D. K. (1998). Rate control for communication networks: Shadow prices, proportional fairness and stability. Journal of the Operational Research society, 49(3), 237–252.

    Article  Google Scholar 

  24. Basar, T., & Srikant, R. (2002). Revenue-maximizing pricing and capacity expansion in a many-users regime. In Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (Vol. 1, pp. 294–301). IEEE.

  25. Das, K., Banerjee, J.S., (2021), Cognitive Radio-Enabled Internet of Things (CR-IoT): An Integrated Approach towards Smarter World, CRC Press, (Accepted).

  26. Saha, P., Guhathakurata, S., Saha, S., Chakraborty, A., & Banerjee, J. S. (2021). Application of machine learning in app-based cab booking system: a survey on Indian scenario. In Applications of Artificial Intelligence in Engineering (pp. 483–497). Singapore: Springer.

  27. Das, D., Pandey, I., Chakraborty, A., & Banerjee, J. S. (2017). Analysis of implementation factors of 3D printer: the key enabling technology for making prototypes of the engineering design and manufacturing. International Journal of Computer Applications, 8–14.

  28. Das, D., Pandey, I., & Banerjee, J. S. (2016). An in-depth study of implementation issues of 3D printer. In Proc. MICRO 2016 Conference on Microelectronics, Circuits and Systems (pp. 45–49).

  29. Biswas, S., Sharma, L. K., Ranjan, R., & Banerjee, J. S. (2020). Go-COVID: an interactive cross-platform based dashboard for real-time tracking of COVID-19 using data analytics. J Mechanics Continua Math Sci, 15, 1–15.

  30. Chattopadhyay, J., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2018, November). Facial expression recognition for human computer interaction. In International Conference On Computational Vision and Bio Inspired Computing (pp. 1181–1192). Cham: Springer.

  31. Das, K., & Banerjee, J. S. (2021). Green IoT for Intelligent Cyber-Physical Systems in Industry 4.0: A Review of Enabling Technologies, and Solutions. CRC Press.

  32. Banerjee, J., Maiti, S., Chakraborty, S., Dutta, S., Chakraborty, A., & Banerjee, J. S. (2019). Impact of machine learning in various network security applications. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) (pp. 276–281). IEEE.

  33. Karmakar, K., & Banerjee, J. S. (2011). Different network micro-mobility protocols and their performance analysis. International Journal of Computer Science and Information Technologies, 2(5), 2165–2175.

  34. Geng, K., Gao, Q., Fei, L., & Xiong, H. (2017). Relay selection in cooperative communication systems over continuous time-varying fading channel. Chinese Journal of Aeronautics, 30(1), 391–398.

    Article  Google Scholar 

  35. Ho-Van, K. (2016). Exact outage probability analysis of proactive relay selection in cognitive radio networks with MRC receivers. Journal of Communications and Networks, 18(3), 288–298.

    Article  Google Scholar 

  36. Zhang, X., An, K., Zhang, B., Chen, Z., Yan, Y., & Guo, D. (2020). Vickrey auction-based secondary relay selection in cognitive hybrid satellite-terrestrial overlay networks with non-orthogonal multiple access. IEEE Wireless Communications Letters, 9(5), 628–632.

    Article  Google Scholar 

  37. Silva, S., Ardakani, M., & Tellambura, C. (2020). Interference Suppression and Energy Efficiency Improvement with Massive MIMO and Relay Selection in Cognitive Two-Way Relay Networks. IEEE Transactions on Green Communications and Networking, 4(2), 326–339.

    Article  Google Scholar 

  38. Simon, M. K., & Alouini, M. S. (2005). Digital communication over fading channels (Vol. 95). Wiley.

  39. Kandukuri, S., & Boyd, S. (2002). Optimal power control in interference-limited fading wireless channels with outage-probability specifications. IEEE Transactions on Wireless Communications, 1(1), 46–55.

    Article  Google Scholar 

  40. Zhang, Q., Jia, J., & Zhang, J. (2009). Cooperative relay to improve diversity in cognitive radio networks. IEEE Communications Magazine, 47(2), 111–117.

    Article  MathSciNet  Google Scholar 

  41. Zhao, G., Yang, C., Li, G. Y., Li, D., & Soong, A. C. (2010). Power and channel allocation for cooperative relay in cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing, 5(1), 151–159.

    Article  Google Scholar 

  42. Yu, H., Tang, W., & Li, S. (2012). Joint optimal sensing and power allocation for cooperative relay in cognitive radio networks. In Proceedings of the 2012 IEEE International Conference on Communications (ICC) (pp. 1635–1640). IEEE.

  43. Jia, J., Zhang, J., & Zhang, Q. (2009). Cooperative relay for cognitive radio networks. In Proceedings of the IEEE INFOCOM 2009 (pp. 2304–2312). IEEE.

  44. Jaafar, W., Ajib, W., & Haccoun, D. (2011). A novel relay-aided transmission scheme in cognitive radio networks. In Proceedings of the 2011 IEEE Global Telecommunications Conference-GLOBECOM 2011 (pp. 1–6). IEEE.

  45. Jaafar, W., Ajib, W., & Haccoun, D. (2012). Opportunistic adaptive relaying in cognitive radio networks. In Proceedings of the 2012 IEEE International Conference on Communications (ICC) (pp. 1811–1815). IEEE.

  46. Jaafar, W., Ajib, W., & Haccoun, D. (2012). Incremental relaying transmissions with relay selection in cognitive radio networks. In Proceedings of the 2012 IEEE Global Communications Conference (GLOBECOM) (pp. 1230–1235). IEEE.

  47. Do, T., & Mark, B. L. (2010). Joint spatial–temporal spectrum sensing for cognitive radio networks. IEEE Transactions on Vehicular Technology, 59(7), 3480–3490.

    Article  Google Scholar 

  48. Luo, H., Zhang, Z., & Yu, G. (2008). Cognitive cooperative relaying. In Proceedings of the 2008 11th IEEE Singapore International Conference on Communication Systems (pp. 1499–1503). IEEE.

  49. Jing, T., Zhu, S., Li, H., Xing, X., Cheng, X., Huo, Y., & Znati, T. (2014). Cooperative relay selection in cognitive radio networks. IEEE Transactions on Vehicular Technology, 64(5), 1872–1881.

    Article  Google Scholar 

  50. Krikidis, I., Charalambous, T., & Thompson, J. S. (2012). Buffer-aided relay selection for cooperative diversity systems without delay constraints. IEEE Transactions on Wireless Communications, 11(5), 1957–1967.

    Article  Google Scholar 

  51. Alsharoa, A., Bader, F., & Alouini, M. S. (2013). Relay selection and resource allocation for two-way DF-AF cognitive radio networks. IEEE Wireless Communications Letters, 2(4), 427–430.

    Article  Google Scholar 

  52. Zhang, S., & Lau, V. K. (2011). Multi-relay selection design and analysis for multi-stream cooperative communications. IEEE Transactions on Wireless Communications, 10(4), 1082–1089.

    Article  Google Scholar 

  53. Song, L. (2011). Relay selection for two-way relaying with amplify-and-forward protocols. IEEE Transactions on Vehicular Technology, 60(4), 1954–1959.

    Article  Google Scholar 

  54. Zou, Y., Yao, Y. D., & Zheng, B. (2012). Diversity-multiplexing tradeoff in selective cooperation for cognitive radio. IEEE Transactions on communications, 60(9), 2467–2481.

    Article  Google Scholar 

  55. Zou, Y., Zhu, J., Zheng, B., & Yao, Y. D. (2010). An adaptive cooperation diversity scheme with best-relay selection in cognitive radio networks. IEEE Transactions on Signal Processing, 58(10), 5438–5445.

    Article  MathSciNet  Google Scholar 

  56. Zou, Y., Zhu, J., Zheng, B., Tang, S., & Yao, Y. D. (2010). A cognitive transmission scheme with the best relay selection in cognitive radio networks. In Proceedings of the 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 (pp. 1–5). IEEE.

  57. Ma, Y., Kibria, M. R., & Jamalipour, A. (2008). Optimized routing framework for intermittently connected mobile ad hoc networks. In Proceedings of the 2008 IEEE International Conference on Communications (pp. 3171–3175). IEEE.

  58. Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2021). A new approach to predict COVID-19 using artificial neural networks. In Cyber-Physical Systems: AI and COVID-19. Elsevier.

  59. Guhathakurata, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2021). A novel approach to predict COVID-19 using support vector machine. In Data Science for COVID-19 (pp. 351–364). Academic Press, Elsevier.

  60. Kim, B., Cho, J., Jeon, S., & Lee, B. (2016). An AHP-Based flexible relay node selection scheme for WBANs. Wireless Personal Communications, 89(2), 501–520.

    Article  Google Scholar 

  61. Saha, O., Chakraborty, A., & Banerjee, J. S. (2017). A decision framework of IT-based stream selection using analytical hierarchy process (AHP) for admission in technical institutions. In Proceedings of 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix), (pp. 1–6).

  62. Saha, O., Chakraborty, A., & Banerjee, J. S. (2019). A fuzzy AHP approach to IT-based stream selection for admission in technical institutions in India. In Proceedings of Emerging Technologies in Data Mining and Information Security, (pp. 847–858).

  63. Paul, S., Chakraborty, A., & Banerjee, J. S. (2017). A fuzzy AHP-based relay node selection protocol for wireless body area networks (WBAN). In Proceedings of 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix), (pp. 1–6).

  64. Paul, S., Chakraborty, A., & Banerjee, J. S. (2019). The extent analysis based fuzzy AHP approach for relay selection in WBAN. In Proceedings of Cognitive Informatics and Soft Computing, (pp. 331–341).

  65. Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2021). South Asian Countries are less fatal concerning COVID-19: A fact-finding procedure integrating machine learning & multiple criteria decision-making (MCDM) technique. Journal of The Institution of Engineers (India): Series B, 1–15.

  66. Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2021). South Asian countries are less fatal concerning COVID-19: A hybrid approach using machine learning and M-AHP. In Computational Intelligence Techniques for combating COVID-19 (pp. 1–26). Cham: Springer.

  67. Kim, J., & Lee, J. (2011). Opportunistic wireless network coding with relay node selection. EURASIP Journal on Wireless Communications and Networking, 2011(1), 196.

    Article  Google Scholar 

  68. Elrabiei, S. M., & Habaebi, M. H. (2010). Energy efficient cooperative communication in single frequency networks. In Proceedings of the 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1719–1724). IEEE.

  69. Razeghi, B., Hatamian, M., Naghizadeh, A., Sabeti, S., & Hodtani, G. A. (2015). A novel relay selection scheme for multi-user cooperation communications using fuzzy logic. In Proceedings of the 2015 IEEE 12th International Conference on Networking, Sensing and Control (pp. 241–246). IEEE.

  70. Tuah, N., & Ismail, M. (2013). Extending lifetime of heterogenous wireless sensor network using relay node selection. In Proceedings of the 2013 International Conference of Information and Communication Technology (ICoICT) (pp. 17–21). IEEE.

  71. Biswas, S., Sharma, L. K., Ranjan, R., Saha, S., Chakraborty, A., & Banerjee, J. S. (2021). Smart farming and water saving-based intelligent irrigation system implementation using the internet of things. In Recent Trends in Computational Intelligence Enabled Research (pp. 339–354). Academic Press, Elsevier.

  72. Roy, R., Dutta, S., Biswas, S., & Banerjee, J. S. (2020). Android Things: A Comprehensive Solution from Things to Smart Display and Speaker. In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India (pp. 339–352).

  73. Banerjee, J. S., Goswami, D., & Nandi, S. (2014). OPNET: a new paradigm for simulation of advanced communication systems. In Proceedings of International Conference on Contemporary Challenges in Management, Technology & Social Sciences, SEMS (pp. 319–328).

  74. de Graaf, M. (2013). Energy efficient networking via dynamic relay node selection in wireless networks. Ad hoc Networks, 11(3), 1193–1201.

    Article  Google Scholar 

  75. Rajpoot, P., & Dwivedi, P. (2019). Multiple parameter based energy balanced and optimized clustering for WSN to enhance the Lifetime using MADM Approaches. Wireless Personal Communications, 106(2), 829–877.

    Article  Google Scholar 

  76. Pandey, I., Dutta, H. S., & Banerjee, J. S. (2019, March). WBAN: A smart approach to next generation e-healthcare system. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) (pp. 344–349). IEEE.

  77. Ehyaie, A., Hashemi, M., & Khadivi, P. (2009). Using relay network to increase life time in wireless body area sensor networks. In Proceedings of the 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops (pp. 1–6). IEEE.

  78. Elias, J., & Mehaoua, A. (2012). Energy-aware topology design for wireless body area networks. In Proceedings of the 2012 IEEE international conference on communications (ICC) (pp. 3409–3410). IEEE.

  79. Lin, C. S., & Chuang, P. J. (2013). Energy-efficient two-hop extension protocol for wireless body area networks. IET Wireless Sensor Systems, 3(1), 37–56.

    Article  Google Scholar 

  80. Banerjee, J. S., Chakraborty, A., & Chattopadhyay, A. (2018). Relay node selection using analytical hierarchy process (AHP) for secondary transmission in multi-user cooperative cognitive radio systems. In Advances in Electronics, Communication and Computing (pp. 745–754). Springer.

  81. Banerjee, J. S., Chakraborty, A., & Chattopadhyay, A. (2018). Reliable best-relay selection for secondary transmission in co-operation based cognitive radio systems: A multi-criteria approach. Journal of Mechanics of Continua and Mathematical Sciences, 13(2), 24–42.

    Article  Google Scholar 

  82. Banerjee, J. S., Chakraborty, A., & Chattopadhyay, A. (2018). A novel best relay selection protocol for cooperative cognitive radio systems using fuzzy AHP. Journal of Mechanics of Continua and Mathematical Sciences, 13(2), 72–87.

    Article  Google Scholar 

  83. Chakraborty, A., Banerjee, J. S., & Chattopadhyay, A. (2019). Non-uniform quantized data fusion rule for data rate saving and reducing control channel overhead for cooperative spectrum sensing in cognitive radio networks. Wireless Personal Communications, 104(2), 837–851.

    Article  Google Scholar 

  84. Chakraborty, A., Banerjee, J. S., & Chattopadhyay, A. (2017). Non-uniform quantized data fusion rule alleviating control channel overhead for cooperative spectrum sensing in cognitive radio networks. In Proceedings of 2017 IEEE 7th International Advance Computing Conference (IACC) (pp. 210–215).

  85. Han, Y., Pandharipande, A., & Ting, S. H. (2009). Cooperative decode-and-forward relaying for secondary spectrum access. IEEE Transactions on Wireless Communications, 8(10), 4945–4950.

    Article  Google Scholar 

  86. Hao, X., Cheung, M. H., Wong, V. W., & Leung, V. C. (2011). A Stackelberg game for cooperative transmission and random access in cognitive radio networks. In Proceedings of the 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 411–416). IEEE, (2011).

  87. Kwon, H., Lee, H., & Cioffi, J. M. (2009). Cooperative strategy by Stackelberg games under energy constraint in multi-hop relay networks. In Proceedings of the GLOBECOM 2009–2009 IEEE Global Telecommunications Conference (pp. 1–6). IEEE.

  88. Debreu, G. (1952). A social equilibrium existence theorem. Proceedings of the National Academy of Sciences, 38(10), 886–893.

    Article  MathSciNet  Google Scholar 

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Banerjee, J.S., Chakraborty, A. & Chattopadhyay, A. A Cooperative Strategy for Trustworthy Relay Selection in CR Network: A Game-Theoretic Solution. Wireless Pers Commun 122, 41–67 (2022). https://doi.org/10.1007/s11277-021-08888-0

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