Energy-Efficient Beam Coordination Strategies With Rate-Dependent Processing Power

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.

Energy-efficient transmission strategies for multiantenna downlink

“…For simplicity we assume that P dyn and P sta are fixed. However, more detailed power consumption could be also applied, e.g., the power consumption for coding/decoding and backhaul can increase with the transmission rate [10].…”

“…Depending on network features and design objective, the EE maximization problem is investigated under three following major criteria Network Energy Efficiency: NEE metric is to consider the EE gain of entire network [4], [10] which follows directly the general definition of EE as (4), i.e.,…”

“…In the light of the SCA principle, we can replace (9a) and (9d) by (11) and (12), respectively to arrive at the approximate convex program of (10), thereby obtaining a local optimality of (10). In the following, we discuss the application of the above technique to specific EEmax problems, i.e., NEE, SWEE and maxminEE maximization.…”

“…Details of the transformation, alternative transformations, and its convergence analysis can be found from [7], [10].…”

“…In the same manner, the SCA based algorithm solving (16) can be followed the one solving (10). More details of the proposed method solving (16) can be found in [10] and an alternative formulation in [14].…”