An Efficient MAC Protocol with Selective Grouping and Cooperative Sensing in Cognitive Radio Networks

TL;DR

This paper introduces GC-MAC, a group-based cooperative MAC protocol for cognitive radio networks that balances sensing accuracy and efficiency, reducing overhead and boosting throughput through selective cooperation and analytical optimization.

Contribution

The paper proposes a novel GC-MAC protocol with a selective SU algorithm and analytical modeling to optimize sensing and throughput tradeoffs in cognitive radio networks.

Findings

Significantly reduces sensing overhead.

Increases network throughput.

Maintains sensing accuracy under various channel conditions.

Abstract

In cognitive radio networks, spectrum sensing is a crucial technique to discover spectrum opportunities for the Secondary Users (SUs). The quality of spectrum sensing is evaluated by both sensing accuracy and sensing efficiency. Here, sensing accuracy is represented by the false alarm probability and the detection probability while sensing efficiency is represented by the sensing overhead and network throughput. In this paper, we propose a group-based cooperative Medium Access Control (MAC) protocol called GC-MAC, which addresses the tradeoff between sensing accuracy and efficiency. In GC-MAC, the cooperative SUs are grouped into several teams. During a sensing period, each team senses a different channel while SUs in the same team perform the joint detection on the targeted channel. The sensing process will not stop unless an available channel is discovered. To reduce the sensing overhead, an SU-selecting algorithm is presented to selectively choose the cooperative SUs based on the channel dynamics and usage patterns. Then, an analytical model is built to study the sensing accuracy-efficiency tradeoff under two types of channel conditions: time-invariant channel and time-varying channel. An optimization problem that maximizes achievable throughput is formulated to optimize the important design parameters. Both saturation and non-saturation situations are investigated with respect to throughput and sensing overhead. Simulation results indicate that the proposed protocol is able to significantly decrease sensing overhead and increase network throughput with guaranteed sensing accuracy.