Maximizing System Throughput by Cooperative Sensing in Cognitive Radio Networks

TL;DR

This paper presents algorithms to maximize system throughput in cognitive radio networks through cooperative sensing, balancing primary user protection and sensing overhead, with proven performance bounds.

Contribution

It introduces a Bayesian decision rule for optimal sensing, reformulates throughput maximization with PU constraints, and analyzes the impact of sensing overhead on system throughput.

Findings

The Bayesian decision rule achieves optimal sensing performance.

Throughput is monotonic with the number of cooperating SUs.

The greedy algorithm guarantees more than half of the optimal throughput.

Abstract

Cognitive Radio Networks allow unlicensed users to opportunistically access the licensed spectrum without causing disruptive interference to the primary users (PUs). One of the main challenges in CRNs is the ability to detect PU transmissions. Recent works have suggested the use of secondary user (SU) cooperation over individual sensing to improve sensing accuracy. In this paper, we consider a CRN consisting of a single PU and multiple SUs to study the problem of maximizing the total expected system throughput. We propose a Bayesian decision rule based algorithm to solve the problem optimally with a constant time complexity. To prioritize PU transmissions, we re-formulate the throughput maximization problem by adding a constraint on the PU throughput. The constrained optimization problem is shown to be NP-hard and solved via a greedy algorithm with pseudo-polynomial time complexity that achieves strictly greater than 1/2 of the optimal solution. We also investigate the case for which a constraint is put on the sensing time overhead, which limits the number of SUs that can participate in cooperative sensing. We reveal that the system throughput is monotonic over the number of SUs chosen for sensing. We illustrate the efficacy of the performance of our algorithms via a numerical investigation.