Predictive and Recommendatory Spectrum Decision for Cognitive Radio

TL;DR

This paper proposes a spectrum decision framework for cognitive radio that combines spectrum prediction and recommendation techniques, utilizing machine learning and reinforcement learning methods to improve spectrum utilization and reduce conflicts.

Contribution

It introduces a novel framework integrating spectrum prediction and recommendation, with new methods based on ELM, Q-learning, and MDP for enhanced spectrum decision-making.

Findings

Improved spectrum decision performance demonstrated

Effective prediction of spectrum states using ELM and Q-learning

Enhanced spectrum utilization with proposed methods

Abstract

Cognitive radio technology enables improving the utilization efficiency of the precious and scarce radio spectrum. How to maximize the overall spectrum efficiency while minimizing the conflicts with primary users is vital to cognitive radio. The key is to make the right decisions of accessing the spectrum. Spectrum prediction can be employed to predict the future states of a spectrum band using previous states of the spectrum band, whereas spectrum recommendation recommends secondary users a subset of available spectrum bands based on secondary user's previous experiences of accessing the available spectrum bands. In this paper, a framework for spectrum decision based on spectrum prediction and spectrum recommendation is proposed. As a benchmark, a method based on extreme learning machine (ELM) for single-user spectrum prediction and a method based on Q-learning for multiple-user spectrum prediction are proposed. At the stage of spectrum decision, two methods based on Q-learning andMarkov decision process (MDP), respectively, are also proposed to enhance the overall performance of spectrum decision. Experimental results show that the performance of the spectrum decision framework is much better.