Cooperative Prediction-and-Sensing Based Spectrum Sharing in Cognitive Radio Networks

TL;DR

This paper introduces a novel spectrum sharing model for cognitive radio networks that combines prediction and sensing, optimizing transmit beamforming and sensing time to enhance secondary user rates while managing interference.

Contribution

It develops a joint design framework for spectrum prediction, sensing, and beamforming, with a new algorithm guaranteeing convergence and improved performance over existing methods.

Findings

Algorithm converges rapidly within few iterations.

Significant performance gains over existing approaches.

Effective joint optimization of sensing and beamforming.

Abstract

This paper proposes prediction-and-sensing based spectrum sharing, a new spectrum-sharing model for cognitive radio networks, with a time structure for each resource block divided into a spectrum prediction-and-sensing phase and a data transmission phase. Cooperative spectrum prediction is incorporated as a sub-phase of spectrum sensing in the first phase. We investigate a joint design of transmit beamforming at the secondary base station (BS) and sensing time. The primary design goal is to maximize the sum rate of all secondary users (SUs) subject to the minimum rate requirement for all SUs, the transmit power constraint at the secondary BS, and the interference power constraints at all primary users. The original problem is difficult to solve since it is highly nonconvex. We first convert the problem into a more tractable form, then arrive at a convex program based on an inner approximation framework, and finally propose a new algorithm to successively solve this convex program. We prove that the proposed algorithm iteratively improves the objective while guaranteeing convergence at least to local optima. Simulation results demonstrate that the proposed algorithm reaches a stationary point after only a few iterations with a substantial performance improvement over existing approaches.