Millimeter Wave (mmWave) Multiple-input-multiple-output Research Articles

Joint Beamforming and Channel Reconfiguration for RIS-Assisted Millimeter Wave Massive MIMO-OFDM Systems

In this paper, we consider the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The objective is to maximize the spectral efficiency by jointly optimizing the RIS's reflection coefficients and the hybrid precoder/combiner at the base station (BS)/mobile station (MS). By resorting to classical hybrid precoder/combiner optimization techniques, this joint beamforming problem can be converted into an optimization concerning only the design of the reflection coefficients. Nevertheless, the objective function of the resulting passive beamforming problem does not have an explicit expression of the reflection coefficients. To address this difficulty, the inherent structure of the effective channel is exploited, based on which an approximation of the truncated singular value decomposition (SVD) of the effective channel is obtained, and a favorable propagation environment can be realized by directly manipulating the singular values of the effective channel. Simulation results show that the proposed method can configure a favorable channel with a small condition number. Moreover, the proposed method presents a clear performance advantage over state-of-the-art algorithms, while with a low computational complexity.

Beam Aggregation-Based mmWave MIMO-NOMA: An AI-Enhanced Approach

Millimeter wave (mmWave) multiple-input-multiple-output (MIMO) with hybrid beamforming enables high data rate broadband services for 5G. To further improve the spectral efficiency and connectivity, non-orthogonal multiple access (NOMA) has been considered to be integrated with mmWave MIMO. Nonetheless, the existing power-domain mmWave MIMO-NOMA with narrow analog beams suffers from beam misalignments which deteriorate the data rates of the misaligned users as well as the system fairness. Thereby, we propose a beam aggregation-based mmWave MIMO-NOMA scheme to loosen the requirement of beam alignment and improve the system fairness. The proposed scheme generates virtual beams with wider beamwidth by aggregating adjacent analog beams. NOMA transmissions are utilized within each aggregated virtual beam. Then, we propose a non-orthogonal multiuser precoding scheme to ensure the fairness within the aggregated beam by maximizing the minimum achievable rates of the grouped users. To address this issue, a max-min problem is proposed and artificial intelligence (AI) technology is exploited to solve this non-trivial problem. The problem is firstly converted to an equivalent penalized minimization problem and then an unsupervised deep neural network (DNN) is trained to map the instantaneous channel coefficients to the precoders in a data-driven fashion. Specifically, we explicitly introduce the transmit power as the DNN input to achieve better generalization in different signal-to-noise ratio regions. Performance evaluations reveal that the proposed scheme can achieve significant gain on the max-min data rate compared with the conventional scheme.

Joint 3D Trajectory and Power Optimization for UAV-Aided mmWave MIMO-NOMA Networks

This paper considers an unmanned aerial vehicle (UAV)-aided millimeter Wave (mmWave) multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV serves as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices in different clusters. We aim to maximize the downlink sum rate by jointly optimizing the three-dimensional (3D) placement of the UAV, beam pattern and transmit power. To address this problem, we first transform the non-convex problem into a total path loss minimization problem, and hence the optimal 3D placement of the UAV can be achieved via standard convex optimization techniques. Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is presented for the shaped-beam pattern synthesis of an antenna array. Finally, by transforming the original problem into an optimal power allocation problem under the fixed 3D placement of the UAV and beam pattern, we derive the closed-form expression of transmit power based on Karush-Kuhn-Tucker (KKT) conditions. In addition, inspired by fraction programming (FP), we propose a FP-based suboptimal algorithm to achieve a near-optimal performance. Numerical results demonstrate that the proposed algorithm achieves a significant performance gain in terms of sum rate for all IoT devices, as compared with orthogonal frequency division multiple access (OFDMA) scheme.

Joint Transceiver and Large Intelligent Surface Design for Massive MIMO mmWave Systems

Large intelligent surface (LIS) has recently emerged as a potential low-cost solution to reshape the wireless propagation environment for improving the spectral efficiency. In this paper, we consider a downlink millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) system, where an LIS is deployed to assist the downlink data transmission from a base station (BS) to a user equipment (UE). Both the BS and the UE are equipped with a large number of antennas, and a hybrid analog/digital precoding/combining structure is used to reduce the hardware cost and energy consumption. We aim to maximize the spectral efficiency by jointly optimizing the LIS's reflection coefficients and the hybrid precoder (combiner) at the BS (UE). To tackle this non-convex problem, we reformulate the complex optimization problem into a much more friendly optimization problem by exploiting the inherent structure of the effective (cascade) mmWave channel. A manifold optimization (MO)-based algorithm is then developed. Simulation results show that by carefully devising LIS's reflection coefficients, our proposed method can help realize a favorable propagation environment with a small channel matrix condition number. Besides, it can achieve a performance comparable to those of state-of-the-art algorithms, while at a much lower computational complexity.

On the Spectral-Efficiency of Low-Complexity and Resolution Hybrid Precoding and Combining Transceivers for mmWave MIMO Systems

Millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems will almost certainly use hybrid precoding to realize beamforming with few numbers of RF chains to reduce energy consumption, but require low complexity technique to improve spectral efficiency. While energy-efficient hybrid analog/digital precoders and combiners designs can subdue the high pathloss inherent in mmWave channels, they assume the use of infinite- (or high-) resolution phase shifters to realize the analog precoder and combiner pair which results in high hardware cost and power consumption. One promising solution is to employ the use of low-resolution phase shifters. In this paper, we first diverse the exploration of multiple candidates of array response vectors, to propose low-complexity hybrid precoder and combiner (LcHPC) design via stage-determined matching pursuit (SdMP) namely, LcHPC-SdMP for pursuing better achievable rate for mmWave MIMO systems. We initially decouple the joint optimization over hybrid precoders and combiners into two separate sparse recovery problems. Specifically, LcHPC-SdMP algorithm revises the identification step of orthogonal matching pursuit (OMP) to the selection of multiple “correct” column indices of the matrix of array response vectors, per iteration. Then adds a pruning step -after satisfying a sparsity level condition, to iteratively refine the sparse solution which aids in further accelerating the algorithm, by requiring fewer iterations. We then propose an algorithm which iteratively designs low-resolution (two-bit) hybrid analog-digital precoder and combiner (LrHPC), for pursuing efficiency while maximizing spectral efficiency. Simulation results demonstrate that the proposed LcHPC-SdMP algorithm performs very close to its full-digital precoding and achieves better spectral efficiency over state-of-the-art algorithms with a substantially reduced number of iteration than the recently proposed schemes. In addition, simulation results also reveal that the achievable rate of the proposed LrHPC algorithm is higher than those of the existing algorithms under consideration.