Adaptive Spectrum Sensing in Cognitive Radio Networks Using Reinforcement Learning

Abstract

Cognitive Radio Networks (CRNs) are potential solutions to the problem of spectrum scarcity through opportunistic access to unutilized frequency bands. But the traditional methods of spectrum sensing like energy detection, matched filtering, and cyclostationary detection usually exhibit constraints in low signal-noise ratio (SNR), noise uncertainty, and multipath fading conditions. In this paper, a proposal is made of an adaptive spectrum sensing framework, which utilizes the reinforcement learning (RL) and deep reinforcement learning (DRL) models to make an intelligent spectrum decision. This framework combines the spectral feature-extraction techniques of convolutional neural networks (CNNs), the channel-optimization techniques of Deep Q-Networks (DQN), and the multi-agent RL approach that utilizes cooperative sensing between distributed cognitive radios. Additionally, there is also a transfer learning mechanism to allow fast adaptation in the heterogeneous environment. Evaluations through simulations-based datasets and benchmark of the proposed approach have shown that it is capable of significantly enhancing the detection probability, the efficiency of spectrum utilization, and the resilience in the presence of uncertainty and outperforms classical baselines. The findings make the presented framework a scalable and effective solution to large-scale CRNs, which provides a better reliability and efficiency in the ever-changing wireless communication conditions.