Recall from Chapter 2 that one of the major signal processing challenges encountered in cognitive radios is that the wireless spectrum is inherently a distributed multiagent system: Almost always, there are more than one wireless device operating in any spectrum band. Indeed, perhaps, the very goal of cognitive radios is to improve such spectrum coexistence of many wireless systems and devices. This may especially be the case when new spectrum allocation policies are put in place along the lines we discussed in Section 3.6 and CRNs become prevalent. Collectively, the spectrum of interest and all its users will then form a complex distributed multiagent system.

In general, individual cognitive radios, or agents, may attempt to learn on their own in this multiagent system while interacting with the same spectrum environment at the same. This scenario is referred to as multiagent learning, and the type of learning in which independent distributed agents attempt to learn their own decision policies simultaneously is called concurrent learning. Such multiagent learning is made difficult by the fact that changes in the state of the spectrum are due to the combined effect of actions executed by all distributed radios. Worse yet, a particular cognitive radio may not know the exact actions taken by other radios.

Perhaps the most widely investigated multiagent learning paradigm is based on (noncooperative) game theory. As we mentioned in Section 14.2.2, game theory ...

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