Opportunistic Channel Selection by Cognitive Wireless Nodes Under Imperfect Observations and Limited Memory: A Repeated Game Model


We tend to study the matter of how autonomous cognitive nodes (CNs) can arrive at an economical and truthful opportunistic channel access policy in scenarios where channels may be non-homogeneous in terms of primary user (PU) occupancy. In our model, a CN that's in a position to adapt to the surroundings is limited in two ways that. First, CNs have imperfect observations (like due to sensing and channel errors) of their setting. Second, CNs have imperfect memory thanks to limitations in computational capabilities. For economical opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a 2-state machine (automaton). Using the framework of repeated games (with imperfect observations and restricted memory), we tend to show that the proposed strategy enables the CNs (while not any explicit coordination) to achieve an outcome that: one) maximizes the overall network payoff and additionally ensures fairness among the CNs; a pair of) reduces the probability of collisions among CNs; and 3) needs a little variety of sensing steps (tries) to find a channel free of PU activity. We have a tendency to compare the performance of the proposed autonomous strategy with a centralized strategy and conjointly test it with real spectrum knowledge collected at RWTH Aachen.

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