Cluster Size Optimization in Cooperative Spectrum Sensing

TL;DR

This paper analyzes and optimizes the size of cooperation clusters in spectrum sensing to maximize secondary user throughput, considering negotiation overhead and decision rules, with numerical results guiding optimal cluster size choices.

Contribution

It introduces a method to optimize cooperation cluster size in spectrum sensing by modeling throughput and considering overhead, comparing OR and AND decision rules.

Findings

Optimal cluster size is less with OR-rule than AND-rule.

Maximum throughput decreases as average SNR increases.

OR-rule consistently outperforms AND-rule when cluster size is optimized.

Abstract

In this paper, we study and optimize the cooperation cluster size in cooperative spectrum sensing to maximize the throughput of secondary users (SUs). To calculate the effective throughput, we assume each SU spends just 1 symbol to negotiate with the other SUs in its transmission range. This is the minimum overhead required for each SU to broadcast its sensing decision to the other members of the cluster. When the number of SUs is large, the throughput spent for the negotiation is noticeable and therefore increasing the cooperation cluster size does not improve the effective throughput anymore. In this paper, we calculate the effective throughput as a function of the cooperation cluster size, and then we maximize the throughput by finding the optimal cluster size. Various numerical results show that when decisions are combined by the OR-rule, the optimum cooperation cluster size is less than when the AND-rule is used. On the other hand, the optimum cluster size monotonically decreases with the increase in the average SNR of the SUs. Another interesting result is that when the cluster size is optimized the OR-rule always outperforms the AND-rule.