Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution

• Published in: IEEE transactions on wireless communications Vol. 11; no. 4; pp. 1380 - 1391
• Main Authors: Yuhua Xu, Jinlong Wang, Qihui Wu, Anpalagan, A., Yu-Dong Yao
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access methods and protocols, osi model; Algorithms; Applied sciences; Availability; Channels; Cognitive radio networks; Convergence; distributed channel selection; Dynamics; exact potential game; Exact sciences and technology; Games; Heuristic algorithms; Learning; Learning automata; Operation, maintenance, reliability; opportunistic spectrum access; Radiocommunication specific techniques; Radiocommunications; Sensors; Statistics; stochastic learning automata; Stochasticity; Studies; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Teleprocessing networks. Isdn; Throughput; Transmission and modulation (techniques and equipments); Performance evaluation; distributed channel selection; Information rate; Equity; Nash equilibrium; Time variation; opportunistic spectrum access; Learning; Frequency selection; exact potential game; Stochastic automaton; Learning algorithm; Cognitive radio networks; Open service access; Information exchange; Availability; Prior information; Game theory; Information transmission; Equilibrium point; Selection problem; Simulation; stochastic learning automata; Service quality; Radio communication; Software radio
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2012.020812.110025

We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.