On the Performance of Cooperative Spectrum Sensing under Quantization

TL;DR

This paper develops analytical expressions and optimization methods for cooperative spectrum sensing performance in cognitive radio systems with multi-level quantization, enabling efficient performance evaluation and enhancement.

Contribution

It introduces closed-form formulas for false alarm and detection probabilities under quantization and proposes optimization techniques for improved sensing performance.

Findings

Closed-form expressions for false alarm and detection probabilities.

Normal approximation for performance evaluation.

Effective optimization methods for quantized sensing performance.

Abstract

In cognitive radio, the cooperative spectrum sensing (CSS) plays a key role in determining the performance of secondary networks. However, there have not been feasible approaches that can analytically calculate the performance of CSS with regard to the multi-level quantization. In this paper, we not only show the cooperative false alarm probability and cooperative detection probability impacted by quantization, but also formulate them by two closed form expressions. These two expressions enable the calculation of cooperative false alarm probability and cooperative detection probability tractable efficiently, and provide a feasible approach for optimization of sensing performance. Additionally, to facilitate this calculation, we derive Normal approximation for evaluating the sensing performance conveniently. Furthermore, two optimization methods are proposed to achieve the high sensing performance under quantization.