A Low-Overhead Energy Detection Based Cooperative Sensing Protocol for Cognitive Radio Systems

TL;DR

This paper introduces a low-overhead, energy detection-based cooperative sensing protocol for cognitive radio systems that enhances spectrum sensing efficiency by reducing reporting overhead and dynamically allocating power based on channel conditions.

Contribution

It proposes a novel, low-complexity cooperative sensing framework that minimizes sensing overhead and includes a power scheduling algorithm based on channel statistics.

Findings

False alarm rate decreases with more sensor nodes.

Mis-detection rate decreases with more sensor nodes.

Asymptotic analysis shows improved sensing performance with increased nodes.

Abstract

Cognitive radio and dynamic spectrum access represent a new paradigm shift in more effective use of limited radio spectrum. One core component behind dynamic spectrum access is the sensing of primary user activity in the shared spectrum. Conventional distributed sensing and centralized decision framework involving multiple sensor nodes is proposed to enhance the sensing performance. However, it is difficult to apply the conventional schemes in reality since the overhead in sensing measurement and sensing reporting as well as in sensing report combining limit the number of sensor nodes that can participate in distributive sensing. In this paper, we shall propose a novel, low overhead and low complexity energy detection based cooperative sensing framework for the cognitive radio systems which addresses the above two issues. The energy detection based cooperative sensing scheme greatly reduces the quiet period overhead (for sensing measurement) as well as sensing reporting overhead of the secondary systems and the power scheduling algorithm dynamically allocate the transmission power of the cooperative sensor nodes based on the channel statistics of the links to the BS as well as the quality of the sensing measurement. In order to obtain design insights, we also derive the asymptotic sensing performance of the proposed cooperative sensing framework based on the mobility model. We show that the false alarm and mis-detection performance of the proposed cooperative sensing framework improve as we increase the number of cooperative sensor nodes.