A Learning-based Multiuser Opportunistic Spectrum Access Approach in Unslotted Primary Networks

Opportunistic spectrum access presents a new approach to wireless spectrum utilization and management. In this paper, we propose a non-cooperative based OSA approach: learning-based approach to allow multiple secondary users to achieve maximal throughput in an unslotted opportunistic spectrum access (OSA) network. In this approach, collisions among secondary users are taken into consideration while making channel sensing decisions. Spectrum maps for secondary users are estimated based on occurrence of collisions. Our approach allows secondary users to achieve maximal throughput by seeking independent spectrum opportunities without exchanging any control information among secondary users. Numerical results show that the learning-based approach obtains near-optimal performance in most of the scenarios.