This study employs an array-of-subarrays (AoSA) hybrid beamforming (HBF) architecture in ultra-massive multiple-input multiple-output (UM-MIMO) systems to enhance the total achievable rate. Our primary objective is to mitigate the strong multi-user interference (MUI) through the design of null-space projection (NSP)-based HBF scheme, which involves two stages: (i) RF beamforming based on introducing beam perturbations to steer beams in the null space of interfering users, and (ii) baseband MU precoding stage based on the instantaneous effective channel to mitigate the residual MUI by a regularized zero-forcing (RZF) technique. To solve this challenging non-convex optimization problem, we propose a swarm intelligence-based sequential optimization solution that finds the optimal beam perturbations while adhering to the directivity degradation constraints for the beams in each user direction. The illustrative results depict the high achievable rate by using the proposed NSP scheme over maximum-directivity beamforming (MBF) irrespective of the users' angular locations, which can be a promising beamforming solution in future sub-Terahertz (sub- THz) UM-MIMO systems.

This study explores a multiuser massive multiple-input-multiple-output (MU-mMIMO) system that incorporates an autonomous aerial vehicle (AAV) as a decode-and-forward (DF) relay between the base station (BS) and multiple Internet of Things (IoT) devices. The primary goal is to maximize the overall achievable rate (AR) by introducing a novel framework that integrates joint hybrid beamforming (HBF) with AAV deployment in dynamic MU-mMIMO IoT systems. Specifically, considering the geometry-based millimeter-wave (mmWave) channel model for both links, the radio frequency (RF) stages are configured to minimize the number of RF chains by utilizing slow time-varying angular information, while the baseband (BB) stages are developed using reduced-dimension effective channel matrices. Subsequently, deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm with continuous action space, is developed to train the AAV for its deployment. By employing a customized reward function, the RL agent learns an optimal AAV deployment policy capable of adapting to both static and dynamic environments. Then, a novel low-complexity DDPG codebook-based AAV deployment (DDPG-C-AD) is proposed, consisting of an offline agent training phase, and an online AAV deployment prediction to achieve maximum AR. The illustrative results show that the proposed DDPG-C-AD can attain a performance close to the DDPG-based solution in both static and dynamic environments while reducing the runtime by 99%. This makes the DDPG codebook-based solution a promising implementation for real-time online applications in AAV-assisted MU-mMIMO IoT systems.

This study employs a uniform rectangular array (URA) sub-connected hybrid beamforming (SC-HBF) architecture to provide a novel self-interference (SI) suppression scheme in a full-duplex (FD) massive multiple-input multiple-output (mMIMO) system. Our primary objective is to mitigate the strong SI through the design of RF beamforming stages for uplink and downlink transmissions that utilize the spatial degrees of freedom provided due to the use of large array structures. We propose a non-constant modulus RF beamforming (NCM-BF-SIS) scheme that incorporates the gain controllers for both transmit (Tx) and receive (Rx) RF beamforming stages and optimizes the uplink and downlink beam directions jointly with gain controller coefficients. To solve this challenging non-convex optimization problem, we propose a swarm intelligence-based algorithmic solution that finds the optimal beam perturbations while also adjusting the Tx/Rx gain controllers to alleviate SI subject to the directivity degradation constraints for the beams. The data-driven analysis based on the measured SI channel in an anechoic chamber shows that the proposed NCM-BF-SIS scheme can suppress SI by around 80 dB in FD mMIMO systems.

This study considers an unmanned aerial vehicle (UAV)-assisted multiuser massive multiple-input multiple-output (MU-mMIMO) systems, where a decode-and-forward (DF) relay in the form of an UAV facilitates the transmission of multiple data streams from a base station (BS) to multiple Internet of Things (IoT) users. A joint optimization problem of hybrid beamforming (HBF), UAV relay positioning, and power allocation (PA) to multiple IoT users to maximize the total achievable rate (AR) is investigated. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and proposes three different swarm intelligence (SI)-based algorithmic solutions to optimize: 1) UAV location with equal PA; 2) PA with fixed UAV location; and 3) joint PA with UAV deployment. The radio frequency (RF) stages are designed to reduce the number of RF chains based on the slow time-varying angular information, while the baseband (BB) stages are designed using the reduced-dimension effective channel matrices. Then, a novel deep learning (DL)-based low-complexity joint HBF, UAV location, and PA optimization scheme (J-HBF-DLLPA) is proposed via fully connected deep neural network (DNN), consisting of an offline training phase, and an online prediction of UAV location and optimal power values for maximizing the AR. The illustrative results show that the proposed algorithmic solutions can attain higher capacity and reduce average delay for delay-constrained transmissions in a UAV-assisted MU-mMIMO IoT systems. Additionally, the proposed J-HBF-DLLPA can closely approach the optimal capacity while significantly reducing the runtime by 99%, which makes the DL-based solution a promising implementation for real-time online applications in UAV-assisted MU-mMIMO IoT systems.

This study considers a dual-hop massive multiple-input multiple-output (mMIMO) system, where a decode-and-forward (DF) relay in the form of an unmanned aerial vehicle (UAV) facilitates the transmission of multiple data streams from a base station (BS) to a gateway serving multiple Internet-of-Things (IoT) devices. To maximize the end-to-end throughput in a three-node wireless sensor network (WSN), we investigate a novel joint optimization problem of hybrid beamforming (HBF) and UAV relay positioning in a given deployment span. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and utilizes particle swarm optimization (PSO) to optimize the UAV location. The radio frequency (RF) stage is designed to minimize the number of RF chains through the utilization of slow time-varying angular information, while the baseband (BB) stage is designed through singular value decomposition (SVD) of the reduced-dimension effective channel matrix. The illustrative results show that the proposed joint HBF approach enhances energy efficiency compared to full-digital beamforming, and the UAV DF relay, placed via the PSO-based deployment scheme, attains a higher capacity compared to fixed UAV deployment locations.

This study considers a novel full-duplex (FD) massive multiple-input multiple-output (mMIMO) system using hybrid beamforming (HBF) architecture, which allows for simultaneous uplink (UL) and downlink (DL) transmission over the same frequency band. Particularly, our objective is to mitigate the strong self-interference (SI) solely on the design of UL and DL RF beamforming stages jointly with sub-array selection (SAS) for transmit (Tx) and receive (Rx) sub-arrays at base station (BS). Based on the measured SI channel in an anechoic chamber, we propose a min-SI beamforming scheme with SAS, which applies perturbations to the beam directivity to enhance SI suppression in UL and DL beam directions. To solve this challenging nonconvex optimization problem, we propose a swarm intelligence-based algorithmic solution to find the optimal perturbations as well as the Tx and Rx sub-arrays to minimize SI subject to the directivity degradation constraints for the UL and DL beams. The results show that the proposed min-SI BF scheme can achieve SI suppression as high as 78 dB in FD mMIMO systems.

This work considers a spherical array (SA)-based dual-hop massive multiple-input multiple-output (mMIMO) system using an unmanned aerial vehicle (UAV) as an amplify-and-forward (AF) relay between the base station (BS) and Internet of Things (IoT) gateway. We propose a particle swarm optimization (PSO)-based UAV deployment technique to maximize the total achievable rate by considering joint optimization of UAV location, hybrid beamforming (HBF) at two terminal nodes, and analog beamforming/combining at the UAV relay. Additionally, we employ singular value decomposition (SVD) of the channel matrices to form the transmit and receive radio frequency (RF) stages of the UAV relay, and an orthogonal matching pursuit (OMP)-based algorithmic approach to the HBF for the BS and the gateway. The illustrative results show that our proposed joint beamforming scheme for two distinct SA configurations significantly improves spectral and energy efficiencies, and outperforms uniform rectangular arrays (URAs).

This work studies the joint design of hybrid pre-coding (HP) and optimal positioning of unmanned aerial vehicle (UAV) relay in a millimeter-wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) systems to maximize the spectral and energy efficiencies. The UAV operates as a flying wireless relay, expanding a base station’s coverage and delivering capacity boost to a group of users/devices that are obscured by obstructions. We explore the geometry-based mmWave channel model for the UAV-User link and propose joint HP and UAV positioning scheme (JHPP). In particular, the RF beamformer is designed using singular value decomposition (SVD) of channel matrix by incorporating users’ angle-of-departure (AoD) information to reduce the number of radio frequency (RF) chains, and the baseband (BB) precoder is designed using regularized zero-forcing (RZF) technique to mitigate MU interference. Then, using a particle swarm optimization-based location algorithm (PSO-L), a constrained optimization problem with the goal of maximizing the achievable sum-rate (ASR) is constructed for the optimal UAV placement in the given search space. Illustrative results show that the integration of a UAV relay considerably enhances the performance of mmWave MU-mMIMO systems when the BS is remote. Moreover, compared to UAV random placement in the given flying span, PSO-L based UAV positioning has higher spectral/energy efficiency. Finally, the use of a hemispherical array (HSA) configuration at UAV relay can further increase the performance when compared to uniform rectangular array (URA).

This paper investigates the performance of different antenna array structures for hybrid massive-MIMO precoding schemes using few-bit DACs. Particularly, the proposed hybrid scheme includes two precoding stages: the RF-beamforming stage is designed via the slowly time-varying channel second-order correlation matrix, while the baseband multi-user (MU) precoding stage is constructed via the regularized zero-forcing (RZF) technique to mitigating the MU-interference. For the same system cost and complexity, we examine the achieved sum-rate and energy efficiency of various 2D antenna array structures, namely, uniform linear array (ULA), uniform rectangular array (URA), uniform circular array (UCA), and concentric circular array (CCA), in serving multiple users at different angular locations. The Monte Carlo simulation results indicate the higher achievable rate and energy efficiency of CCA by using low-resolution DACs as compared to various 2D array structures. We also show that only (3-5)-bit DACs are sufficient to provide comparable spectral and energy efficiencies.

This paper investigates the performance behaviours of various antenna array structures for hybrid massive-MIMO precoding schemes. In particular, the proposed hybrid scheme includes two cascaded stages: the RF-beamforming stage is designed via the eigen-decomposition of the massive-MIMO channel second-order correlation matrix while the baseband multi-user (MU) precoding stage is constructed via the regularized zero-forcing (RZF) technique to mitigating the MU-interference in the reduced-dimension effective MU-channel. A transfer block is introduced between the RF-beamforming and baseband precoding stages to significantly reduce the number of required RF chains. For the same number of antenna elements with half-wavelength spacing, we examine the achieved sum-rate performance of different 2D antenna array structures, namely, uniform linear array (ULA), uniform rectangular array (URA), uniform circular array (UCA), and concentric circular array (CCA), in serving multiple users in various angle-of-departure (AoD) settings. Simulation results indicate that, among the array structures, URA and CCA can offer both smaller array sizes and higher achieved sum-rate. Furthermore, for various user angular locations, the sum-rate of URA can vary about 2 bits/s/Hz while CCA can give an invariant sum-rate performance.