Quang-Doanh Vu

Achieving Energy Efficiency Fairness in Multicell MISO Downlink

We investigate the fairness of achievable energy efficiency in a multicell multiuser multiple-input single-output (MISO) downlink system, where a beamforming scheme is designed to maximize the minimum energy efficiency among all base stations. The resulting optimization problem is a nonconvex max-min fractional program, which is generally difficult to solve optimally. We propose an iterative beamformer design based on an inner approximation algorithm which aims at locating a Karush-Kuhn-Tucker solution to the nonconvex program. By novel transformations, we arrive at a convex problem at each iteration of the proposed algorithm, which is amendable for being approximated by a second order cone program. The numerical results demonstrate that the proposed algorithm outperforms the existing schemes in terms of the convergence rate and processing time.

Energy-Efficient Bandwidth and Power Allocation for Multi-Homing Networks

This paper investigates resource allocation for multi-homing networks where users can simultaneously transmit data to multiple radio access networks (RANs) using multiple air interfaces. We aim at optimally assigning the bandwidth and power to each user-RAN connection so as to maximize energy-efficiency of the entire network subject to user specific QoS requirements as well as the available resource budgets. First, we study the problem of resource allocation for scenarios where the connections between the users and the RANs are predefined, which naturally leads to a fractional program. To obtain the optimal solution efficiently and facilitate distributed implementations, we further equivalently transform the design problem into a convex program using a parameter-free approach and develop a decentralized algorithm based on the alternating direction method of multipliers. Second, we investigate the problem of joint link selection and resource allocation for energy-efficiency maximization. The problem is cast as a mixed integer nonlinear convex program for which we particularize the branch and bound method to find an optimal solution. Then, two suboptimal low-complexity designs are proposed: the first one is based on greedy algorithm, which gradually maximizes the virtual link energy-efficiency; the other one is based on the concept of sparsity-inducing norm. Simulation results are presented to demonstrate the potential gains in terms of energy-efficiency of the proposed methods.

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 (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-Coopers 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.

An Efficiency Maximization Design for SWIPT

A joint power splitting and beamforming design for multiuser multiple-input single-output (MISO) systems where receivers have capability of decoding information and harvesting energy simultaneously from received signals is considered. The objective is to maximize the ratio of the achieved utility to the total power consumption subject to harvested power requirements and power budget at a base station (BS). The utility function of interest combines the sum rate and the total harvested power. The design problem is nonconvex, and thus, global optimality is difficult to achieve. To solve this problem locally we first convert the problem into a more tractable form, and then propose an iterative algorithm which is guaranteed to achieve a Karush-Kuhn-Tucker solution. Numerical results are provided to demonstrate the superior performance of the proposed method.

Time Reversal-Based Transmissions with Distributed Power Allocation for Two-Tier Networks

Radio pollution and power consumption problems lead to innovative development of green heterogeneous networks (HetNet). Time reversal (TR) technique which has been validated from wide-to narrow-band transmissions is evaluated as one of most prominent linear precoders with superior capability of harvesting signal energy. In this paper, we consider a new HetNet model, in which TR-employed femtocell is proposed to attain saving power benefits whereas macrocell utilizes the beam-forming algorithm based on zero-forcing principle, over frequency selective channels. In the considered HetNet, the practical case of limited signaling information exchanged via backhaul connections is also taken under advisement. We hence organize a distributed power loading strategy, in which macrocell users are treated with a superior priority compared to femtocell users. By Monte-Carlo simulation, the obtained results show that TR is preferred to zero-forcing in the perspective of beam forming technique for femtocell environments due to very high achievable gain in saving energy, and the validity of power loading strategy is verified over multipath channels.

Distributed energy efficiency fairness optimization by ADMM in multicell MISO downlink

This paper studies the fairness of achievable energy efficiency (EE) in a multicell multiuser multiple-input single-output downlink. The objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. The resulting optimization problem is a max-min fractional program, and, thus, difficult to solve in general. Our goal is to develop a decentralized algorithm for the max-min EE problem which solves the problem locally. The idea behind the proposed method is to combine the framework of successive convex approximation (SCA) and alternative direction method of multipliers (ADMM). We transform the convex program obtained at each step of the SCA procedure into a form that lends itself to the ADMM. The resulting formulation is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. In addition to further reduce the backhaul overhead, the proposed algorithm is modified to enhance the convergence speed. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms.

Distributed Solutions for Energy Efficiency Fairness in Multicell MISO Downlink

This paper aims at guaranteeing the achievable energy efficiency (EE) fairness in a multicell multiuser multiple-input single-output downlink system. The design objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. This results in a max-min fractional program and as such is difficult to solve in general. Our goal is to develop decentralized algorithms for the max-min EE problem based on combining the successive convex approximation (SCA) framework and the alternating direction method of multipliers (ADMMs). Specifically, leveraging the SCA principle, we iteratively approximate the nonconvex design problem by a sequence of convex programs for which two decentralized algorithms are then proposed. In the first approach, the convex program obtained at each step of the SCA procedure is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. The convergence of the first method is analytically guaranteed but the amount of backhaul signaling can be noticeable in some realistic settings. To reduce the backhaul overhead, the second method performs an abstract version of the ADMM where only one variables update is carried out. Numerical results are provided to demonstrate the effectiveness of the two proposed decentralized algorithms.

Optimal Energy Efficient Resource Allocation for Heterogeneous Multi-Homing Networks

This paper studies the problem of resource allocation for uplink multi-homing users in heterogeneous network where users can simultaneously transmit data to multiple radio access networks (RANs). The considered design problem is to optimally assign bandwidth and power to each user-RAN connection so as to maximize the overall energy efficiency of the network subject to QoS requirements and resource budgets. By the definition of energy efficiency which is the ratio of the aggregate throughput to the power consumption, the resulting problem is formulated as a fractional program. Then, we propose an energy efficient algorithm using the Dinkelback method that solves a series of convex problems to obtain the optimal design. Particularly, we derive closed-form expressions for the design parameters and provide useful insights into the proposed energy efficient resource allocation algorithm. Numerical results are presented to demonstrate that the proposed algorithm is superior to other resource allocation strategies in terms of energy efficiency.

Energy efficient preceding C-RAN downlink with compression at fronthaul

This paper considers a downlink transmission of cloud radio access network (C-RAN) in which precoded baseband signals at a common baseband unit are compressed before being forwarded to radio units (RUs) through limited fronthaul capacity links. We investigate the joint design of precoding, multivariate compression and RU-user selection which maximizes the energy efficiency of downlink C-RAN networks. The considered problem is inherently a rank-constrained mixed Boolean nonconvex program for which a globally optimal solution is difficult and computationally expensive to find. In order to derive practically appealing solutions, we invoke some useful relaxation and transformation techniques to arrive at a more tractable (but still nonconvex) continuous program. To solve the relaxation problem, we propose an iterative procedure based on DC algorithms which is provably convergent. Numerical results demonstrate the superior of the proposed solution in terms of achievable energy efficiency compared to existing schemes.

Globally optimal beamforming design for downlink CoMP transmission with limited backhaul capacity

This paper considers a multicell downlink channel in which multiple base stations (BSs) cooperatively serve users by jointly precoding shared data transported from a central processor over limited-capacity backhaul links. We jointly design the beamformers and BS-user link selection so as to maximize the sum rate subject to user-specific signal-to-interference-noise (SINR) requirements, per-BS backhaul capacity and per-BS power constraints. As existing solutions for the considered problem are suboptimal and their optimality remains unknown due to the lack of globally optimal solutions, we characterized this gap by proposing an globally optimal algorithm for the problem of interest. Specifically, the proposed method is customized from a generic framework of a branch and bound algorithm applied to discrete monotonic optimization. We show that the proposed algorithm converges after a finite number of iterations, and can serve as a benchmark for existing suboptimal solutions and those that will be developed for similar contexts in the future. In this regard, we numerically compare the proposed optimal solution to a current state-of-the-art, which show that this suboptimal method only attains 70% to 90% of the optimal performance.