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

Abstract

We study the problem of how autonomous cognitive nodes (CNs) can arrive at an efficient and fair 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 is able to adapt to the environment is limited in two ways. First, CNs have imperfect observations (such as due to sensing and channel errors) of their environment. Second, CNs have imperfect memory due to limitations in computational capabilities. For efficient opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a two-state machine (automaton). Using the framework of repeated games (with imperfect observations and limited memory), we show that the proposed strategy enables the CNs (without any explicit coordination) to reach an outcome that: 1) maximizes the total network payoff and also ensures fairness among the CNs; 2) reduces the likelihood of collisions among CNs; and 3) requires a small number of sensing steps (attempts) to find a channel free of PU activity. We compare the performance of the proposed autonomous strategy with a centralized strategy and also test it with real spectrum data collected at RWTH Aachen.