Reinforcement learning-based multiband sensing policy for cognitive radios

Abstract

Cognitive radios (CR) and dynamic spectrum access (DSA) have been proposed as a way to exploit the underutilized radio spectrum by allowing secondary users to access the licensed frequencies in an opportunistic manner. The constraint set to the secondary use is that it should not interfere the primary users, i.e., the license holder. Hence, the secondary users need to sense the spectrum in order to classify a licensed frequency band as vacant or occupied. However, spectrum sensing can be a demanding task for a single user due to the random nature of the wireless channel, and to mitigate the effects of channel fading cooperative detection algorithms have been proposed. In this paper a multiband spectrum sensing policy for coordinating the cooperative sensing is proposed. It is based on dynamically allocating frequency hopping codes to the secondary users. The proposed policy employs the ∈-greedy reinforcement learning action selection to prioritize the sensing of different subbands and to select the best secondary users to sense them. The results show the proposed policy is able to significantly increase the obtained throughput in the secondary network and to reduce the number of missed detections of the primary signal.