S. Hossein Seyedmehdi

Optimal power allocation in device-to-device communication with SIMO uplink beamforming

Cellular and device-to-device (D2D) communication may cause significant inter-cell interference (ICI) at a neighboring base station (BS). In this work, we aim to maximize the sum rate of a cellular user (CU) and a D2D pair, with receive beamforming at the BS equipped with multiple antennas, subject to per-node power, maximum ICI, and minimum SINR constraints. We propose an efficient algorithm to maximize the sum rate in two steps. We first consider the D2D admissibility problem to determine whether the D2D pair can share the spectrum with the CU while satisfying all the constraints and SINR requirements. We then obtain the optimal beam vector and the optimal power levels of the CU and D2D transmitters in closed form. The performance of the proposed algorithm is studied numerically. It is shown the proposed optimal solution substantially outperforms a CU-priority heuristic approach that selects the maximum CU power with minimum D2D interference.

Multi-Channel Resource Allocation Toward Ergodic Rate Maximization for Underlay Device-to-Device Communications

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.

Joint Power Optimization for Device-to-Device Communication in Cellular Networks With Interference Control

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.

Power Allocation for Underlay Device-to-Device Communication Over Multiple Channels

In underlay device-to-device (D2D) communication, a D2D pair reuses the cellular spectrum, causing interference to existing cellular users. The achieved D2D rate and the added interference to cellular users need to be jointly considered for optimal resource and power allocations. Unlike most existing work which only consider the simplified scenario of assigning each D2D pair a single channel or resource block (RB), we consider multiple RBs from different cellular users can be assigned to each D2D pair. We formulate the problem of optimal power allocation over multiple RBs at the D2D transmitter to maximize the sum-rate of D2D and cellular users, under the D2D transmitter power constraint and minimum signal-to-noise-and-interference ratio (SINR) requirement at each reused RB for all affected cellular users in all cells. To further lower the required overhead in a practical setting, we consider a second optimization problem for power allocation solution to maximize the D2D rate under the same constraints as the sum-rate maximization problem. We obtain the asymptotic power solution for the sum-rate maximization and the semiclosed-form optimal power solution for the D2D rate maximization. Our proposed optimization solutions are applicable to both uplink and downlink cellular spectrum sharing as well as to mutlicell with multiple D2D pairs scenarios. Our simulation studies demonstrates the effectiveness of the two proposed methods for both uplink and downlink resource sharing, and further shed light into how the maximum rate is impacted by the system parameters such as available D2D transmit power, number of D2D pairs, minimum SINR requirements, and the cell size.

Robust power optimization for device-to-device communication in a multi-cell network under partial CSI

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.

Long-term power allocation for multi-channel Device-to-Device communication

In underlay Device-to-Device (D2D) communication, a D2D pair reuses the cellular spectrum and creates interference to regular cellular users. Achieving potential improvements in underlay D2D communication requires joint consideration for the achieved D2D rate and the interference to cellular users. In this work, we present stochastic optimization solutions to allocate the D2D transmission power over multiple resource blocks (RBs), to maximize the D2D rate, under a long-term sum-power constraint and long-term individual power constraints over each RB at the D2D transmitter. The long-term sum-power constraint limits the battery usage of the D2D transmitter, and the per-RB constraints give probabilistic guarantees on the interference to regular cellular users. The proposed optimization is applicable to both uplink and downlink cellular spectrum sharing. We present two dynamic algorithms to solve this stochastic optimization problem: a Lagrange dual based algorithm that is optimal but has high computational complexity, and a low-complexity heuristic based on dynamic time averaging. Through simulation, we show that the performance gap between optimal and heuristic algorithms is small, and effective long-term stochastic power optimization over the D2D shared RBs can lead to substantial gains in the ergodic sum rate between D2D and cellular users.

Multi-channel power allocation for device-to-device communication underlaying cellular networks

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.

Long-term power allocation for multi-channel device-to-device communication based on limited feedback information

In underlay Device-to-Device (D2D) communication, where a D2D pair reuses the cellular spectrum and creates interference to regular cellular users, there exists a tradeoff between the achieved D2D rate and the interference to cellular users. In this work, we present stochastic optimization solutions to allocate the D2D transmission power over multiple resource blocks (RBs), to maximize the D2D rate, under a sum-power constraint and long-term individual power constraints over each RB at the D2D transmitter, which gives probabilistic guarantees on the interference to regular cellular users. This stochastic optimization problem can be solved optimally in the Lagrange dual domain with stochastic subgradient updating. However, since the vector channel state space is exponentially increasing in size, the standard subgradient updating method has prohibitive computation and storage complexity. Instead, by first showing that only the signs of subgradients are necessary to find the optimal Lagrange multipliers, we propose a distributed algorithm where each interference-victim cellular user calculates the subgradient and reports only its sign in one-bit feedback. Simulation results demonstrate the effectiveness of the proposed optimization with limited feedback.

Low Complexity Antenna Subset Selection for Massive MIMO Systems with Multi-Cell Cooperation

Cooperative massive MIMO systems have emerged as an attractive solution to meet the demands of the next generation wireless communication systems. Antenna selection at the base stations can significantly reduce the hardware and computational complexities of such systems. In this paper, we propose three sub-optimal antenna subset selection schemes based on the SNR maximization at the scheduled users with zero-forced beamforming transmission at the base stations. Simulation results show that our proposed schemes outperform other schemes with similar computational complexities and even surpass some of the higher computational complexity schemes in a massive MIMO setting.

Practical hybrid precoding for multi-user massive MIMO systems

Massive 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. This paper proposes two hybrid precoding schemes for a multi-user massive MIMO system. The proposed schemes, namely sequential-EGT and best-user EGT schemes, use equal gain transmission (EGT) based analog beamforming to reap the diversity benefit of an analog phased-array and employ zero-forcing beamforming for nullifying inter-user interference. As illustrated by the simulation results presented in this paper, the best-user EGT scheme outperforms sequential-EGT scheme in all scenarios with slightly higher computational complexity. We also investigate the optimal number of scheduled users for each scheme.