Analytical and Learning-Based Spectrum Sensing Time Optimization in Cognitive Radio Systems

TL;DR

This paper presents both analytical and neural network-based methods to optimize spectrum sensing time in cognitive radio systems, aiming to maximize secondary user throughput while minimizing energy consumption, without prior environment knowledge.

Contribution

It introduces a novel learning-based approach for sensing time optimization that adapts without prior knowledge, complementing traditional analytical methods.

Findings

Neural network approach effectively finds optimal sensing time.

Optimized sensing improves secondary throughput and reduces energy use.

Simulation results validate both analytical and learning-based methods.

Abstract

Powerful spectrum sensing schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. In this paper, maximizing the average throughput of a secondary user by optimizing its spectrum sensing time is formulated assuming that a prior knowledge of the presence and absence probabilities of the primary users is available. The energy consumed for finding a transmission opportunity is evaluated and a discussion on the impact of the number of the primary users on the secondary user throughput and consumed energy is presented. In order to avoid the challenges associated with the analytical method, as a second solution, a systematic neural network-based sensing time optimization approach is also proposed in this paper. The proposed adaptive scheme is able to find the optimum value of the channel sensing time without any prior knowledge or assumption about the wireless environment. The structure, performance, and cooperation of the artificial neural networks used in the proposed method are disclosed in detail and a set of illustrative simulation results is presented to validate the analytical results as well as the performance of the proposed learning-based optimization scheme.