Joint power allocation for MIMO-OFDM full-duplex relaying communications

© 2017, The Author(s). In this paper, we address the problem of joint power allocation in a two-hop MIMO-OFDM network, where two full-duplex users communicate with each other via an amplify-and-forward relay. We consider a general model in which the full-duplex relay can forward the received message in either one-way or two-way mode. Our aim is to maximize the instantaneous end-to-end total throughput, subject to (i) the separate sum-power constraints at individual nodes or (ii) the joint sum-power constraint of the whole network. The formulated problems are large-scale nonconvex optimization problems, for which efficient and optimal solutions are currently not available. Using the successive convex approximation approach, we develop novel iterative algorithms of extremely low complexity which are especially suitable for large-scale computation. In each iteration, a simple closed-form solution is derived for the approximated convex program. The proposed algorithms guarantee to converge to at least a local optimum of the nonconvex problems. Numerical results verify that the devised solutions converge quickly, and that our optimal power allocation schemes significantly improve the throughput of MIMO-OFDM full-duplex one-way/two-way relaying over the conventional half-duplex relaying strategy

Precoding design for Han-Kobayashi's signal splitting in MIMO interference networks

© 2017 The Institute of Electronics, Information and Communication Engineers. For a multiuser multi-input multi-output (MU-MIMO) multicell network, the Han-Kobayashi strategy aims to improve the achievable rate region by splitting the data information intended to a serviced user (UE) into a common message and a private message. The common message is decodable by this UE and another UE from an adjacent cell so that the corresponding intercell interference is cancelled off. This work aims to design optimal precoders for both common and private messages to maximize the network sum-rate, which is a highly nonlinear and nonsmooth function in the precoder matrix variables. Existing approaches are unable to address this difficult problem. In this paper, we develop a successive convex quadratic programming algorithm that generates a sequence of improved points. We prove that the proposed algorithm converges to at least a local optimum of the considered problem. Numerical results confirm the advantages of our proposed algorithm over conventional coordinated precoding approaches where the intercell interference is treated as noise

Energy-efficient precoding in multicell networks with full-duplex base stations

© 2017, The Author(s). This paper considers multi-input multi-output (MIMO) multicell networks, where the base stations (BSs) are full-duplex transceivers, while uplink and downlink users are equipped with multiple antennas and operate in a half-duplex mode. The problem of interest is to design linear precoders for BSs and users to optimize the network’s energy efficiency. Given that the energy efficiency objective is not a ratio of concave and convex functions, the commonly used Dinkelbach-type algorithms are not applicable. We develop a low-complexity path-following algorithm that only invokes one simple convex quadratic program at each iteration, which converges at least to the local optimum. Numerical results demonstrate the performance advantage of our proposed algorithm in terms of energy efficiency

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Successive Convex Quadratic Programming for Quality-of-Service Management in Full-Duplex MU-MIMO Multicell Networks

© 2016 IEEE. This paper designs jointly optimal linear precoders for both base stations (BSs) and users in a multiuser multi-input multi-output (MU-MIMO) multicell network. The BSs are full-duplexing transceivers while uplink users and downlink users (DLUs) are equipped with multiple antennas. Here, the network quality-of-service (QoS) requirement is expressed in terms of the minimum throughput at the BSs and DLUs. We consider the problems of either QoS-constrained sum throughput maximization or minimum cell throughput maximization. Due to the nonconcavity of the throughput functions, the optimal solutions of these two problems remain unknown in both half-duplexing and full-duplexing networks. The first problem has a nonconcave objective and a nonconvex feasible set, whereas the second problem has a nonconcave and nonsmooth objective. To solve such challenging optimization problems, we develop iterative low-complexity algorithms that only invoke one simple convex quadratic program at each iteration. Since the objective value is proved to iteratively increase, our path-following algorithms converge at least to the local optimum of the original nonconvex problems. Due to their guaranteed convergence, simple implementation, and low complexity, the devised algorithms lend themselves to practical precoder designs for large-scale full-duplex MU-MIMO multicell networks. Numerical results demonstrate the advantages of our successive convex quadratic programming framework over existing solutions

Power minimization in MU-MIMO cellular network under rate constraints

© 2014 IEEE. We consider a multi-user multiple input multiple output (MU-MIMO) cellular network in which each base station (BS) can control its radiation pattern and transmission power to its serving users by adjusting its preceding matrices. Under a cooperative network between BSs, we propose a two-step procedure to minimize the total power consumption of the whole network while every user is guaranteed to be served with at least target data rate. Numerical simulation shows that the proposed procedure have good performance and fast convergence

Optimized linear precoder in MIMO interference channel using D.C. programming

In this paper, we are concerned with optimized linear precoding strategies for multiple-input multiple-output interference channels (MIMO-IFC). Under practical transmission power constraints, we aim at maximizing the sum-information-rate. To date, most developments could not directly address this problem. Instead, intermediate problems of simplification have been considered by means of alternating optimization, which fails to jointly optimize all the concerned variables. In this paper, we directly solve this problem by exploring its hidden d.c. (difference of convex functions) structure. The recast problem can be iteratively solved by d.c. iterations (DCIs), which guarantees the convergence to at least a local optimum. Numerical simulation results show that the proposed solutions offer improved performance. © 2013 IEEE

Successive interference mitigation in multiuser MIMO channels

© 2015 IEEE. Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference of concave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN

User pairing and precoder design with Han-kobayashi transmission strategy in MU-MIMO multicell networks

© 2015 IEEE. This paper considers the Han-Kobayashi transmission strategy that mitigates the downlink intercell interference in a multiuser multi-input multi-output (MU-MIMO) multicell network. The base station (BS) splits the transmitted data of a user (UE) into a common message and a private message, the former of which is then decoded by a paired UE in another cell so as to reduce the respective cross-cell interference. Our aim here is to develop (i) A pairing rule that determines pairs of UEs to share common messages, and (ii) Optimal precoders at the BSs that maximize either the minimum UE throughput or the network sum-rate. To solve the combinatoric UE pairing problem, we propose a heuristic that pairs UEs with the largest corresponding cross-cell channel gains. This approach ensures that the most significant source of intercell interference is eliminated through common message decoding. We then apply the Frank-and-Wolfe procedure of concave programming to solve the highly nonconvex precoder design problems. We show that this procedure generates a sequence of improved solutions and eventually converges to at least a local optimum. Numerical results confirm the advantages of our proposed solution over the conventional strategy where intercell interference is treated as noise