Sensing Matrix Setting Schemes for Cognitive Networks and Their Performance Analysis

TL;DR

This paper investigates spectrum sensing matrix schemes for cognitive radios, proposing novel sub-optimal solutions that maximize throughput with lower complexity, validated through simulations.

Contribution

It introduces three new sub-optimal sensing matrix setting schemes for cognitive radios, balancing performance and computational complexity.

Findings

Proposed solutions achieve near-optimal throughput performance.

Lower computational complexity and energy consumption compared to optimal solutions.

Simulation results validate the effectiveness of the proposed schemes.

Abstract

Powerful spectrum decision schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. One of the key effecting factor on the CR network throughput is the spectrum sensing sequence used by each secondary user. In this paper, secondary users' throughput maximization through finding an appropriate sensing matrix (SM) is investigated. To this end, first the average throughput of the CR network is evaluated for a given SM. Then, an optimization problem based on the maximization of the network throughput is formulated in order to find the optimal SM. As the optimum solution is very complicated, to avoid its major challenges, three novel sub optimum solutions for finding an appropriate SM are proposed for various cases including perfect and non-perfect sensing. Despite of having less computational complexities as well as lower consumed energies, the proposed solutions perform quite well compared to the optimum solution (the optimum SM). The structure and performance of the proposed SM setting schemes are discussed in detail and a set of illustrative simulation results is presented to validate their efficiencies.