In underlay device-to-device (D2D) communications, a D2D pair reuses the cellular spectrum causing interference to regular cellular users. Maximizing the performance of underlay D2D communications requires joint consideration for the achieved D2D rate and the interference to cellular users. In this paper, we consider the D2D power allocation optimization over multiple resource blocks (RBs), aimed at maximizing either the ergodic D2D rate or the ergodic sum rate of D2D and cellular users, under the long-term sum-power constraint of the D2D users and per-RB probabilistic signal-to-interference-and-noise (SINR) requirements for all cellular users. We formulate stochastic optimization problems for D2D power allocation over time. The proposed optimization framework is applicable to both uplink and downlink cellular spectrum sharing. To solve the proposed stochastic optimization problems, we first convexify the problems by introducing a family of convex constraints as a replacement for the non-convex probabilistic SINR constraints. We then present two dynamic power allocation algorithms: a Lagrange dual-based algorithm that is optimal but with a high computational complexity and a low-complexity heuristic algorithm based on dynamic time averaging. Through simulation, we show that the performance gap between the optimal and heuristic algorithms is small, and the effective long-term stochastic D2D power optimization over the shared RBs can lead to substantial gains in the ergodic D2D rate and the ergodic sum rate.

For device-to-device (D2D) underlaid cellular networks, the perfect channel state information (CSI) may not be available at the base station (BS). In this work, under an assumption of partial CSI, we study the problem of maximizing the expected sum rate for a cellular user (CU) and a D2D pair, with receive beamforming at the BS, subject to minimum SINR requirements for both the CU and D2D pair, per-node maximum power, and inter-cell interference constraints in multiple neighboring cells. We solve this non-convex joint optimization problem in two steps. We first consider the D2D admissibility problem to determine whether the D2D pair can reuse the channel resource of the CU. We then propose a robust power control algorithm using a ratio-of-expectation approximation to maximize the expected sum rate. For benchmarking, we further provide an upper bound on the maximum expected sum rate. Simulation results show that our proposed solution gives performance close to the upper bound.

Massive multiple‐input multiple‐output (MIMO) systems bring manifold improvements in the system spectral efficiency but result in high hardware and processing complexity at the base station. Employing hybrid precoding at the base station can reduce such complexity. In this study, unlike most existing work on hybrid precoding design, the authors consider a sub‐connected analogue combining structure to reduce the complexity. Starting from an important observation on the effect of sequentially designed analogue phased arrays on users' sum rate, the authors develop three low‐complexity hybrid precoding schemes for a multi‐user massive MIMO system. The proposed schemes apply equal gain transmission (EGT) based analogue beamforming to reap the diversity benefit of an analogue phased array and employ zero‐forcing (ZF) beamforming for nullifying inter‐user interference. The authors carry out an extensive computational complexity analysis and simulation study on the proposed schemes. The proposed singular‐value decomposition based EGT scheme outperforms all others but incurs the highest computational burden. On the other hand, the sequential‐EGT scheme is the least computationally intensive scheme but shows the worse performance amongst them.

For device-to-device (D2D) communication under laid in a cellular network with uplink resource sharing, both cellular and D2D pairs may cause significant inter-cell interference (ICI) at a neighboring base station (BS). In this paper, under optimal BS receive beamforming, we jointly optimize the power of a cellular user (CU) and a D2D pair for their sum rate maximization, while satisfying minimum SINR requirements and worst-case ICI limit in multiple neighboring cells. We solve this non-convex joint optimization problem in two steps. First, the necessary and sufficient condition for the D2D admissibility under given constraints is obtained. Finally, we consider joint power control of the CU and D2D transmitters. We propose a power control algorithm to maximize the sum rate. Depending on the severity of ICI that D2D and CU may cause, we categorize the feasible solution region into five cases, each of which may further include several scenarios based on minimum SINR requirements. The proposed algorithm is optimal when ICI to a single neighboring cell is considered. For multiple neighboring cells, we provide an upper bound on the performance loss by the proposed algorithm and conditions for its optimality. We further extend our consideration to the scenario of multiple CUs and D2D pairs, and formulate the joint power control and CU-D2D matching problem. We show how our proposed solution for one CU and one D2D pair can be utilized to solve this general joint optimization problem. Simulation demonstrates the effectiveness of our power control algorithm and the nearly optimal performance of the proposed approach in the setting of multiple CUs and D2D pairs.

In underlay device-to-device (D2D) communication, a D2D pair reuses the cellular spectrum and creates interference to regular cellular users. Optimal operation requires joint consideration for the achieved D2D rate and the added interference to cellular users. Most existing work on D2D rate maximization concerns only the simplified scenario where the D2D pair has access to a single channel or resource block. In this work, we present an optimization solution to allocate the D2D transmission power over multiple channels, to maximize the sum rate between D2D and cellular users, under a sum-power constraint on the D2D transmitter and minimum SINR guarantees at each RB for all cellular users. The proposed optimization is applicable to both uplink and downlink cellular spectrum sharing. Our simulation studies further shed light into how the maximum sum rate is impacted by the available D2D power and the SINR guarantees.

In this paper, the utilization of mobile devices (MDs) as decode-and-forward relays in a device-to-device assisted virtual MIMO (VMIMO) system is studied. Single antenna MDs are randomly distributed on a 2D plane according to a Poisson point process, and only a subset of them are sources leaving other idle MDs available to assist them (relays). Our goal is to develop an efficient algorithm to cluster each source with a subset of available relays to form a VMIMO system under a limited feedback assumption. We first show that the NP-hard optimization problem of precoding in our scenario can be approximately solved by semidefinite relaxation. We investigate a special case with a single source and analytically derive an upper bound on the average spectral efficiency of the VMIMO system. Then, we propose an optimal greedy algorithm that achieves this bound. We further exploit these results to obtain a polynomial time clustering algorithm for the general case with multiple sources. Finally, numerical simulations are performed to compare the performance of our algorithm with that of an exhaustive clustering algorithm, and it shown that these numerical results corroborate the efficiency of our algorithm.