In a cognitive radio (CR) network, the channel sensing scheme used to detect the existence of a primary user (PU) directly affects the performances of both CR and PU. However, in practical systems, the CR is prone to sensing errors due to the inefficiency of the sensing scheme. This may yield primary user interference and low system performance. In this paper, we present a learning-based scheme for channel sensing in CR networks. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). The optimal policy is derived by solving the problem. Simulation results show the effectiveness and efficiency of our proposed scheme.

In this letter, we propose a Partially Observable Markov Decision Process (POMDP) based Distributed Adaptive Opportunistic Spectrum Access (DA-OSA) Strategy for Cognitive Ad Hoc Networks (CAHNs). In each slot, the source and destination choose a set of channels to sense and then decide the transmission channels based on the sensing results. In order to maximize the throughput for each link, we use the theories of sequential decision and optimal stopping to determine the optimal sensing channel set. Moreover, we also establish the myopic policy and exploit the monotonicity of the reward function that we use, which can be used to reduce the complexity of the sequential decision.