Energy-Efficient Beam Coordination Strategies with Rate Dependent Processing Power

TL;DR

This paper introduces energy-efficient beamforming strategies for multi-cell systems considering rate-dependent power consumption, proposing algorithms that improve energy efficiency while accounting for practical power models.

Contribution

It develops decentralized algorithms for energy-efficient beamforming that incorporate rate-dependent power consumption, a novel aspect in the field.

Findings

Rate-dependent power consumption significantly impacts energy efficiency.

Proposed algorithms outperform conventional beamforming methods.

Decentralized implementation is feasible with closed-form solutions.

Abstract

This paper proposes energy-efficient coordinated beamforming strategies for multi-cell multi-user multiple-input single-output system. We consider a practical power consumption model, where part of the consumed power depends on the base station or user specific data rates due to coding, decoding and backhaul. This is different from the existing approaches where the base station power consumption has been assumed to be a convex or linear function of the transmit powers. Two optimization criteria are considered, namely network energy efficiency maximization and weighted sum energy efficiency maximization. We develop successive convex approximation based algorithms to tackle these difficult nonconvex problems. We further propose decentralized implementations for the considered problems, in which base stations perform parallel and distributed computation based on local channel state information and limited backhaul information exchange. The decentralized approaches admit closed-form solutions and can be implemented without invoking a generic external convex solver. We also show an example of the pilot contamination effect on the energy efficiency using a heuristic pilot allocation strategy. The numerical results are provided to demonstrate that the rate dependent power consumption has a large impact on the system energy efficiency, and, thus, has to be taken into account when devising energy-efficient transmission strategies. The significant gains of the proposed algorithms over the conventional low-complexity beamforming algorithms are also illustrated.