Distributed Energy Efficient Cross-layer Optimization for Multihop MIMO Cognitive Radio Networks with Primary User Rate Protection

TL;DR

This paper presents a distributed, energy-efficient cross-layer optimization framework for multihop MIMO cognitive radio networks, balancing network utility and power consumption while protecting primary user rates.

Contribution

It introduces a novel linearization-based alternative optimization method and a distributed algorithm for joint physical and network layer optimization in MIMO CR networks.

Findings

Significant power savings with near-optimal network utility.

Distributed algorithm performs close to centralized solutions.

Effective primary user rate protection achieved.

Abstract

Due to the unique physical-layer characteristics associated with MIMO and cognitive radio (CR), the network performance is tightly coupled with mechanisms at the physical, link, network, and transport layers. In this paper, we consider an energy-efficient cross-layer optimization problem in multihop MIMO CR networks. The objective is to balance the weighted network utility and weighted power consumption of SU sessions, with a minimum PU transmission rate constraint and SU power consumption constraints. However, this problem is highly challenging due to the nonconvex PU rate constraint. We propose a solution that features linearization-based alternative optimization method and a heuristic primal recovery method. We further develop a distributed algorithm to jointly optimize covariance matrix at each transmitting SU node, bandwidth allocation at each SU link, rate control at each session source and multihop/multi-path routing. Extensive simulation results demonstrate that the performance of the proposed distributed algorithm is close to that of the centralized algorithm, and the proposed framework provides an efficient way to significantly save power consumption, while achieving the network utility very close to that achieved with full power consumption.