Energy-Efficient Zero-Forcing Precoding Design for Small-Cell Networks

We consider small-cell networks with multiple-antenna transceivers and base stations (BSs) cooperating to jointly design linear precoders to maximize the network energy efficiency, subject to a sum power and per-antenna power constraints at individual BSs, as well as user-specific quality of service...

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Bibliographic Details
Published in:IEEE transactions on communications 2016-02, Vol.64 (2), p.790-804
Main Authors: Vu, Quang-Doanh, Tran, Le-Nam, Farrell, Ronan, Hong, Een-Kee
Format: Article
Language:English
Subjects:
ADMM
Algorithm design and analysis
Algorithms
Antennas
branch-and-bound
Computing time
energy efficiency
Energy management
joint design
Mathematical model
Mathematical models
MIMO
Networks
Optimization
Power demand
Quality of service
Quality of service architectures
Small-cell networks
Transformations
Wireless communication
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Summary:We consider small-cell networks with multiple-antenna transceivers and base stations (BSs) cooperating to jointly design linear precoders to maximize the network energy efficiency, subject to a sum power and per-antenna power constraints at individual BSs, as well as user-specific quality of service (QoS) requirements. Assuming zero-forcing precoding, we formulate the problem of interest as a concave-convex fractional program to which we proposed a centralized optimal solution based on the prevailing Dinkelbach algorithm. To facilitate distributed implementations, we transform the design problem into an equivalent convex program using Charnes-Cooper's transformation. Then, based on the framework of alternative direction method of multipliers (ADMM), we develop a decentralized algorithm, which is numerically shown to achieve fast convergence. Since BSs are generally power-hungry, it may be more energy-efficient if some BSs can be shut down, while still satisfying the QoS constraints. Toward this end, we investigate the problem of joint precoder design and BS selection, which is a mixed Boolean nonlinear program, and then provide an optimal solution by customizing the branch-and-bound method. For real-time applications, we propose a greedy algorithm which achieves near-optimal performance in polynomial time. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2015.2502941