Qingjiang Shi

Distributed Wireless Sensor Network Localization Via Sequential Greedy Optimization Algorithm

Node localization is essential to most applications of wireless sensor networks (WSNs). In this paper, we consider both range-based node localization and range-free node localization with uncertainties in range measurements, radio range, and anchor positions. First, a greedy optimization algorithm, named sequential greedy optimization (SGO) algorithm, is presented, which is more suitable for distributed optimization in networks than the classical nonlinear Gauss-Seidel algorithm. Then a unified optimization framework is proposed for both range-based localization and range-free localization, and two convex localization formulations are obtained based on semidefinite programming (SDP) relaxation techniques. By applying the SGO algorithm to the edge-based SDP relaxation formulation, we propose a second-order cone programming (SOCP)-based distributed node localization algorithm. Two distributed refinement algorithms are also proposed by using the SGO algorithm to nonconvex localization formulations. The proposed localization algorithms all can be implemented partially asynchronously in networks. Finally, extensive simulations are conducted to demonstrate the efficiency and accuracy of the proposed distributed localization algorithms.

Joint Beamforming and Power Splitting for MISO Interference Channel With SWIPT: An SOCP Relaxation and Decentralized Algorithm

This paper considers a power splitting-based MISO interference channel for simultaneous wireless information and power transfer (SWIPT), where each single antenna receiver splits the received signal into two streams of different power for decoding information and harvesting energy separately. We aim to minimize the total transmission power by joint beamforming and power splitting (JBPS) under both the signal-to-interference-plus-noise ratio (SINR) constraints and energy harvesting (EH) constraints. The JBPS problem is nonconvex and has not yet been well addressed in the literature. Moreover, decentralized algorithm design for JBPS based on local channel state information (CSI) and limited information exchange remains open. In this paper, we first propose a novel relaxation method named second-order cone programming (SOCP) relaxation to address the JBPS problem. We formulate the relaxed problem as an SOCP and present two sufficient conditions under which the SOCP relaxation is tight. For the case when the SOCP solution is not necessarily optimal to the JBPS problem, a closed-form feasible-solution-recovery method is provided. Then, we develop a distributed algorithm for the JBPS problem based on primal-decomposition (PD) method. The PD-based distributed algorithm consists of a master problem and a set of subproblems. The former is solved by using subgradient method while the latter are solved using coordinate descent method. Finally, numerical results validates the efficiency of the proposed algorithms.

Robust SINR-constrained MISO downlink beamforming: when is semidefinite programming relaxation tight?

We consider the multiuser beamforming problem for a multi-input single-output downlink channel that takes into account the errors in the channel state information at the transmitter side (CSIT). By modeling the CSIT errors as elliptically bounded uncertainty regions, this problem can be formulated as minimizing the transmission power subject to the worst-case signal-to-interference-plus-noise ratio constraints. Several methods have been proposed to solve this nonconvex optimization problem, but none can guarantee a global optimal solution. In this article, we consider a semidefinite relaxation (SDR) for this multiuser beamforming problem, and prove that the SDR method actually solves the robust beamforming problem to global optimality as long as the channel uncertainty bound is sufficiently small or when the transmitter is equipped with at most two antennas. Numerical examples show that the proposed SDR approach significantly outperforms the existing methods in terms of the average required power consumption at the transmitter.

Energy Efficiency Optimization for MISO SWIPT Systems With Zero-Forcing Beamforming

This paper considers a power splitting based multiuser multiple-input-single-output (MISO) downlink system with simultaneous wireless information and power transfer, where each single antenna receiver splits the received signal into two streams of different power for decoding information and harvesting energy separately. Assuming that the most common zero-forcing (ZF) beamforming scheme is employed by the base station, we aim to maximize the system energy efficiency in bits per Joule by joint beamforming and power splitting under both the signal-to-interference-plus-noise ratio constraints and energy harvesting constraints. The energy efficiency optimization (EEO) problem is nonconvex and very hard to solve. In this paper, by exploiting the problem structure, we first simplify the EEO problem to a joint transmit power allocation and receive power splitting problem. Then, with a tactful reformulation, we propose a Lagrangian relaxation (LR) method coupled with Dinkelbach method to address the simplified EEO problem, whilst devising a nearly closed-form solution for the subproblems involved in the Dinkelbach method. It is proven that the proposed LR method is optimum under some condition and can guarantee at least a feasible solution, which is a notable advantage over the existing methods. Besides, we develop a low complexity EEO algorithm by proportionally distributing the total power to users. Finally, numerical results validate the excellent efficiency of the proposed algorithms.

Robust SINR-constrained MISO downlink beamforming: When is semidefinite programming relaxation tight?

We consider the robust beamforming problem under imperfect channel state information (CSI) subject to SINR constraints in a downlink multiuser MISO system. One popular approach to solve this nonconvex optimization problem is via semidefinite relaxation (SDR). In this paper, we prove that the SDR method is tight when the channel uncertainty bound is small or when the base station is equipped with two antennas.

An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel

Consider the MIMO interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to the users in other cells. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, we extend the algorithm to a general class of utility functions and establish its convergence. The resulting algorithm can be implemented in a distributed asynchronous manner. The effectiveness of the proposed algorithm is validated by numerical experiments.

Nonnegative matrix factorization using ADMM: Algorithm and convergence analysis

The nonnegative matrix factorization (NMF) has been a popular model for a wide range of signal processing and machine learning problems. It is usually formulated as a nonconvex cost minimization problem. This work settles the convergence issue of a popular algorithm based on the alternating direction method of multipliers proposed in Boyd et al 2011. We show that the algorithm converges globally to the set of KKT solutions whenever certain penalty parameter ρ satisfies ρ > 1. We further extend the algorithm and its analysis to the problem where the observation matrix contains missing values. Numerical experiments on real and synthetic data sets demonstrate the effectiveness of the algorithms under investigation.

Robust Transceiver Design for MISO Interference Channel With Energy Harvesting

In this paper, we consider the multiuser multiple-input single-output (MISO) interference channel where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. The transmit beamforming vectors and receive power splitting (PS) ratios are jointly designed in order to minimize the total transmission power subject to both signal-to-interference-plus-noise ratio (SINR) and EH constraints. Most joint beamforming and power splitting (JBPS) designs assume that perfect channel state information (CSI) is available; however CSI errors are inevitable in practice. To overcome this limitation, we study the robust JBPS design problem assuming a norm-bounded error (NBE) model for the CSI. Three different solution approaches are proposed for the robust JBPS problem, each one leading to a different computational algorithm. Firstly, an efficient semidefinite relaxation (SDR)-based approach is presented to solve the highly non-convex JBPS problem, where the latter can be formulated as a semidefinite programming (SDP) problem. A rank-one recovery method is provided to recover a robust feasible solution to the original problem. Secondly, based on second order cone programming (SOCP) relaxation, we propose a low complexity approach with the aid of a closed-form robust solution recovery method. Thirdly, a new iterative method is also provided which can achieve near-optimal performance when the SDR-based algorithm results in a higher-rank solution. We prove that this iterative algorithm monotonically converges to a Karush-Kuhn-Tucker (KKT) solution of the robust JBPS problem. Finally, simulation results are presented to validate the robustness and efficiency of the proposed algorithms.

Joint Source-Relay Design for Full-Duplex MIMO AF Relay Systems

The performance of full-duplex (FD) relay systems can be greatly impacted by the self-interference (SI) at relays. By exploiting multiple antennas, the spectral efficiency of FD relay systems can be enhanced through spatial SI mitigation. This paper studies joint source transmit beamforming and relay processing to achieve rate maximization for FD multiple-input-multiple-output (MIMO) amplify-and-forward (AF) relay systems with consideration of relay processing delay. The problem is difficult to solve mainly due to the SI constraint induced by the relay processing delay. In this paper, we first present a sufficient condition under which the relay amplification matrix has rank-one structure. Then, for the case of rank-one amplification matrix, the rate maximization problem is equivalently simplified into an unconstrained problem that can be locally solved using the gradient ascent method. Next, we propose a penalty-based algorithmic framework, named P-BSUM, for a class of constrained optimization problems that have difficult equality constraints in addition to some convex constraints. By rewriting the rate maximization problem with a set of auxiliary variables, we apply the P-BSUM algorithm to the rate maximization problem in the general case. Finally, numerical results validate the efficiency of the proposed algorithms and show that the joint source-relay design approach under the rankone assumption could be strictly suboptimal as compared to the P-BSUM-based joint source-relay design approach.

Joint Transceiver Designs for Full-Duplex

In this paper, we propose joint transceiver design algorithms for the full-duplex