Maximizing System Throughput Using Cooperative Sensing in Multi-Channel Cognitive Radio Networks

TL;DR

This paper addresses the challenge of maximizing throughput in multi-channel Cognitive Radio Networks by developing an approximation algorithm that accounts for sensing limitations, providing theoretical guarantees and demonstrating improved performance over simpler methods.

Contribution

It introduces a novel approximation algorithm for throughput maximization in multi-channel CRNs with sensing constraints, with proven performance bounds.

Findings

The problem is strongly NP-hard.

The proposed algorithm guarantees at least 1/2μ of optimal throughput.

Numerical results show the algorithm outperforms random and greedy approaches.

Abstract

In Cognitive Radio Networks (CRNs), unlicensed users are allowed to access the licensed spectrum when it is not currently being used by primary users (PUs). In this paper, we study the throughput maximization problem for a multi-channel CRN where each SU can only sense a limited number of channels. We show that this problem is strongly NP-hard, and propose an approximation algorithm with a factor at least $1/2\mu$ where $\mu \in [1,2]$ is a system parameter reflecting the sensing capability of SUs across channels and their sensing budgets. This performance guarantee is achieved by exploiting a nice structural property of the objective function and constructing a particular matching. Our numerical results demonstrate the advantage of our algorithm compared with both a random and a greedy sensing assignment algorithm.