Weighted Sum Power Minimization for Cooperative Spectrum Sharing in Cognitive Radio Networks

TL;DR

This paper proposes a new weighted sum power metric for cooperative spectrum sharing in cognitive radio networks, simplifying optimization and managing power budgets effectively.

Contribution

It introduces WSP as a novel performance metric and develops a low-complexity, near-optimal resource allocation scheme using linear programming and Newton's method.

Findings

WSP reduces optimization complexity.

Proposed scheme achieves near-optimal performance.

Accelerated convergence through optimized initial values.

Abstract

This letter introduces weighted sum power (WSP), a new performance metric for wireless resource allocation during cooperative spectrum sharing in cognitive radio networks, where the primary and secondary nodes have different priorities and quality of service (QoS) requirements. Compared to using energy efficiency (EE) and weighted sum energy efficiency (WSEE) as performance metrics and optimization objectives of wireless resource allocation towards green communication, the linear character of WSP can reduce the complexity of optimization problems. Meanwhile, the weights assigned to different nodes are beneficial for managing their power budget. Using WSP as the optimization objective, a suboptimal resource allocation scheme is proposed, leveraging linear programming and Newton's method. Simulations verify that the proposed scheme provides near-optimal performance with low computation time. Furthermore, the initial approximate value selection in Newton's method is also optimized to accelerate the proposed scheme.