It is known that superposition signaling in Gaussian interference networks is capable of improving the achievable rate region. However, the problem of maximizing the rate gain offered by superposition signaling is computationally prohibitive, even in the simplest case of two-user single-input single-output interference networks. This paper examines superposition signaling for the general multiple-input multiple-output broadcast Gaussian interference networks. The problem of maximizing either the sum rate or the minimal user's rate under superposition signaling and dirty paper coding is solved by a computationally efficient path-following procedure, which requires only a convex quadratic program for each iteration but ensures convergence at least to a locally optimal solution. Numerical results demonstrate the substantial performance advantage of the proposed approach.

In this paper, new strategies are devised for joint load balancing and interference management in the downlink of a heterogeneous network, where small cells are densely deployed within the coverage area of a traditional macrocell. Unlike existing work, the limited backhaul capacity at each base station (BS) is taken into account. Here, users (UEs) cannot be offloaded to any arbitrary BS, but only to ones with sufficient backhaul capacity remaining. Jointly designed with traffic offload, transmit power allocation mitigates the intercell interference to further support the quality of service of each UE. The objective here is either: 1) to maximize the network sum rate subject to minimum throughput requirements at individual UEs, or 2) to maximize the minimum UE throughput. Both formulated problems belong to the difficult class of mixed-integer nonconvex optimization problems. The inherently binary BS-UE association variables are strongly coupled with the transmit power variables, making the problems even more challenging to solve. New iterative algorithms are developed based on an exact penalty method combined with successive convex programming, where the binary BS-UE association problem and the nonconvex power allocation problem are dealt with one at a time. At each iteration of the proposed algorithms, only two simple convex problems need to be solved at the same time scale. It is proven that the algorithms improve the objective functions at each iteration and converge eventually. Numerical results demonstrate the efficiency of the proposed algorithms in both traffic offloading and interference mitigation.

This paper considers the efficient design of precoding matrices for sum throughput maximization under throughput quality of service (QoS) constraints and energy harvesting (EH) constraints for energy-constrained devices in a full-duplex (FD) multicell multi-user multiple-input-multiple-output network. Both time splitting (TS) and power splitting are considered to ensure practical EH and information decoding. These problems are quite complex due to non-concave objectives and nonconvex constraints. Especially, with TS, which is implementation-wise quite simple, the problem is even more challenging because the time splitting variable is not only coupled with the downlink throughput function but also coupled with the self-interference in the uplink throughput function. New path-following algorithms are developed for their solutions, which require only a single convex quadratic program for each iteration and ensure rapid convergence. Moreover, the FD EH maximization problem under throughput QoS constraints with TS is also considered. The performance of the proposed algorithms is compared with that of the modified problems assuming half-duplex systems. Finally, the merit of the proposed algorithms is demonstrated through extensive simulations.

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.

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.

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 ofconcave 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.