Design of Spectrum Sensing Policy for Multi-user Multi-band Cognitive Radio Network

TL;DR

This paper introduces a reinforcement learning-based method for spectrum sensing in multi-user multi-band cognitive radio networks, effectively balancing detection accuracy and data rate without complete system knowledge.

Contribution

It develops a non-parametric reinforcement learning approach with a suboptimal sensing policy search algorithm using the randomized Chair-Varshney rule.

Findings

Achieves near-optimal sum profit in simulations

Maintains desired probability of detection

Reduces false alarms effectively

Abstract

Finding an optimal sensing policy for a particular access policy and sensing scheme is a laborious combinatorial problem that requires the system model parameters to be known. In practise the parameters or the model itself may not be completely known making reinforcement learning methods appealing. In this paper a non-parametric reinforcement learning-based method is developed for sensing and accessing multi-band radio spectrum in multi-user cognitive radio networks. A suboptimal sensing policy search algorithm is proposed for a particular multi-user multi-band access policy and the randomized Chair-Varshney rule. The randomized Chair-Varshney rule is used to reduce the probability of false alarms under a constraint on the probability of detection that protects the primary user. The simulation results show that the proposed method achieves a sum profit (e.g. data rate) close to the optimal sensing policy while achieving the desired probability of detection.