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

Abstract

We consider small-cell networks with multipleantenna 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-andbound 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.Index Terms-Small-cell networks, energy efficiency, MIMO, joint design, ADMM, branch-and-bound.

Resource Allocation for Energy Efficiency in 5G Wireless Networks

“…In our work [38], we use a novel group sparsity of weighted 1 -norm corresponding to switching-off SBSs and associated users for joint linear precoder design in EE maximization problem. The Dinkelbachtype algorithms [13] of fractional programming is used as the main approach for obtaining computational solutions of EE maximization [17,52,55]. From the considered HetNet scenario in [39], we propose a novel computational algorithm for maximizing EE via a convex quadratic program at each iteration under threeobjective (EE, QoS and BS service loading) optimization.…”

“…With the increasing demand for green networking, recently research has been focused on approaches that reduce energy consumption [16,51,55]. It is critical to meet the required quality of service (QoS) for all users in the network [53] while managing the amount of utilized energy.…”

“…Nevertheless, the EE maximization problem usually results in a nonconvex program [5]. Specifically, the network energy-efficiency merit is usually defined as the ratio of the total information throughput and the total energy consumption, and thus, the EE maximization problem is introduced under the class of fractional programs [32,49,55] in the field of optimization. To enhance the performance of communication systems, the combination of interference alignment [59], spectrum allocation and energy-efficient management should be necessarily considered and implemented simultaneously.…”

Joint Fronthaul Link Selection and Transmit Precoding for Energy Efficiency Maximization of Multiuser MIMO-Aided Distributed Antenna Systems

“…To check the feasibility of the problem, a heuristic method based on the interference alignment technique was proposed in [29] under the assumption of having infinite transmit power, which is not realistic. References [30] and [31] adopted the interference alignment technique and the zero-forcing method to eliminate the multiuser interference, respectively. Then, the original beam-vector optimization problems were transformed into a more tractable scalar power allocation optimization problem.…”

“…Then, the original beam-vector optimization problems were transformed into a more tractable scalar power allocation optimization problem. Since the beam directions in [30] and [31] are heuristically chosen and not jointly optimized with the power allocation, the algorithms developed in [30] and [31] will suffer from a substantial EE loss. Furthermore, neither the feasibility nor the link selection issues were addressed in [30] and [31].…”