Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach

TL;DR

This paper introduces a two-stage reinforcement learning algorithm for cognitive radio that optimizes channel selection and sensing duration, significantly reducing sensing frequency while maintaining high throughput and low interference.

Contribution

It presents a novel combined reinforcement and Bayesian learning approach for efficient spectrum access in cognitive radio networks.

Findings

Reduces sensing frequency with minimal primary interference

Increases secondary user throughput

Outperforms other learning methods in simulations

Abstract

With the advent of the 5th generation of wireless standards and an increasing demand for higher throughput, methods to improve the spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a substantial increase in throughput is possible if the secondary user can make smart decisions regarding which channel to sense and when or how often to sense. Here, we propose an algorithm to not only select a channel for data transmission but also to predict how long the channel will remain unoccupied so that the time spent on channel sensing can be minimized. Our algorithm learns in two stages - a reinforcement learning approach for channel selection and a Bayesian approach to determine the optimal duration for which sensing can be skipped. Comparisons with other learning methods are provided through extensive simulations. We show that the number of sensing is minimized with negligible increase in primary interference; this implies that lesser energy is spent by the secondary user in sensing and also higher throughput is achieved by saving on sensing.