Capacity Optimization through Sensing Threshold Adaptation for Cognitive Radio Networks

TL;DR

This paper introduces a convex optimization approach to adapt sensing thresholds in cognitive radio networks, significantly enhancing secondary user capacity while safeguarding primary users.

Contribution

It formulates and solves a convex optimization problem for sensing threshold adaptation, demonstrating a novel method for capacity maximization in cognitive radio networks.

Findings

Optimization achieves significant capacity gains for secondary users.

Convexity of the problem is established, enabling efficient solution.

Numerical results validate the effectiveness of the proposed method.

Abstract

In this paper we propose the capacity optimization over sensing threshold for sensing-based cognitive radio networks. The objective function of the proposed optimization is to maximize the capacity at the secondary user subject to the constraints on the transmit power and the sensing threshold in order to protect the primary user. The defined optimization problem is a convex optimization over the transmit power and the sensing threshold where the concavity on sensing threshold is proved. The problem is solved by using Lagrange duality decomposition method in conjunction with a subgradient iterative algorithm and the numerical results show that the proposed optimization can lead to significant capacity maximization for the secondary user as long as the primary user can afford.