Cooperative Cognitive Networks: Optimal, Distributed and Low-Complexity Algorithms

TL;DR

This paper develops optimal, distributed, and low-complexity algorithms for cooperative cognitive networks where cognitive base stations relay primary signals to minimize power while satisfying rate requirements.

Contribution

It introduces a novel framework for cooperative relaying with unit-rank matrices and proposes two efficient algorithms for optimal beamforming in cognitive networks.

Findings

Distributed algorithm with linear convergence performs well in practice.

Superlinear centralized algorithm achieves faster convergence.

Cooperation improves outage performance significantly.

Abstract

This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs). We show that the relaying matrices have unit rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, which provide much simpler solutions. Simulation results demonstrate the improvement in terms of outage performance due to the cooperation between the primary and cognitive systems.