While neural lossy compression techniques have markedly advanced the efficiency of Channel State Information (CSI) compression and reconstruction for feedback in MIMO communications, efficient algorithms for more challenging and practical tasks—such as CSI compression for future channel prediction and reconstruction with relevant side information—remain underexplored, often resulting in suboptimal performance when existing methods are extended to these scenarios. To that end, we propose a novel framework for compression with side information, featuring an encoding process with fixed-rate compression using a trainable codebook for codeword quantization, and a decoding procedure modeled as a backward diffusion process conditioned on both the codeword and the side information. Experimental results show that our method significantly outperforms existing CSI compression algorithms, often yielding over twofold performance improvement by achieving comparable distortion at less than half the data rate of competing methods in certain scenarios. These findings underscore the potential of diffusion-based compression for practical deployment in communication 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.

In this paper, we propose a transmission mechanism for a reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) system based on cluster index modulation (CIM), named best-gain optimized cluster selection CIM (BGCS-CIM). The proposed BGCS-CIM scheme considers effective cluster power gain and spatial diversity gain obtained by the additional paths within the indexed cluster to construct an efficient codebook. We also integrate the proposed scheme into a practical system model to create a virtual path between transmitter and receiver where the direct link has been blocked. Thanks to the designed whitening filter, a closed-form expression for the upper bound on the average bit error rate (ABER) is derived and used to validate the simulation results. It has been shown that the proposed BGCS-CIM scheme outperforms the existing benchmarks thanks to its higher effective cluster gain, spatial diversity of indexed clusters, and lower inter-cluster interference.

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 paper proposes a genetic-algorithm (GA)-based subcarrier allocation in orthogonal frequency division multiplexing (OFDM)-based hybrid beamforming multi-user massive multiple-input multiple-output (MU-mMIMO) systems. Our goal is to maximize the system sum-rate capacity under the total transmit power constraint through optimally selecting Kmaxusers out of K available users to be served over each sub-carrier. Considering the energy-efficient hybrid beamforming architecture deployed at the base station (BS), the non-convex optimization problem is solved in four steps: (i) designing a radio frequency (RF) beamformer using slow time-varying angle-of-departure (AoD) information of users to generate the beams for all subcarriers, (ii) designing a baseband (BB) precoder for each subcarrier using the corresponding low-dimensional effective channel state information (CSI) seen from the BB stage based on regularized zero-forcing (RZF) technique, (iii) optimizing subcarrier allocation using GA with equal power allocation (EQ-PA) among users (iv) performing GA-based power allocation over each subcarrier to further improve the system sum-rate. Illustrative results indicate that the proposed algorithm performs significantly better than the random and greedy subcarrier allocation schemes in terms of the achieved sum-rate.

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 paper proposes a reconfigurable intelligent surface (RIS)-aided and angular-based hybrid beamforming (AB-HBF) technique for the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The proposed RIS-AB-HBF architecture consists of three stages: (i) RF beamformer, (ii) baseband (BB) precoder/combiner, and (iii) RIS phase shift design. First, in order to reduce the number of RF chains and the channel estimation overhead, RF beamformers are designed based on the 3D geometry-based mmWave channel model using slow time-varying angular parameters of the channel. Second, a BB precoder/combiner is designed by exploiting the reduced-size effective channel seen from the BB stages. Then, the phase shifts of the RIS are adjusted to maximize the achievable rate of the system via the nature-inspired particle swarm optimization (PSO) algorithm. Illustrative simulation results demonstrate that the use of RISs in the AB-HBF systems has the potential to provide more promising advantages in terms of reliability and flexibility in system design.

In this paper, a novel full-duplex non-coherent (FD-NC) transmission scheme is developed for massive multiple-input multiple-output (mMIMO) systems using analog beamforming (ABF). We propose to use a structured Grassmannian constellation for the non-coherent communications that does not require channel estimation. Then, we design the transmit and receive ABF via the slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information, respectively. The ABF design targets maximizing the intended signal power while suppressing the strong self-interference (SI) occurred in the FD transmission. Also, the proposed ABF technique only needs a single transmit and receive RF chain to support large antenna arrays, thus, it reduces hardware cost/complexity in the mMIMO systems. It is shown that the proposed FD-NC offers a great improvement in bit error rate (BER) in comparison to both half-duplex non-coherent (HD-NC) and HD coherent schemes. We also observe that the proposed FD-NC both reduces the error floor resulted from the residual SI in FD transmission, and provides lower BER compared to the FD coherent transmission.

This paper proposes a deep learning based power allocation (DL-PA) and hybrid precoding technique for multi-user massive multiple-input multiple-output (MU-mMIMO) systems. We first utilize an angular-based hybrid precoding technique for reducing the number of RF chains and channel estimation overhead. Then, we develop the DL-PA algorithm via a fully-connected deep neural network (DNN). DL-PA has two phases: (i) offline supervised learning with the optimal allocated powers obtained by particle swarm optimization based PA (PSO-PA) algorithm, (ii) online power prediction by the trained DNN. In comparison to the computationally expensive PSO-PA, it is shown that DL-PA greatly reduces the runtime by 98.6%-99.9%, while closely achieving the optimal sum-rate capacity. It makes DL-PA a promising algorithm for the real-time online applications in MU-mMIMO systems.

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 develops a new genetic algorithm based resource allocation (GA-RA) technique for energy-efficient throughout maximization in multi-user massive multiple-input multiple-output (MU-mMIMO) systems using orthogonal frequency division multiplexing (OFDM) based transmission. We employ a hybrid precoding (HP) architecture with three stages: (i) radio frequency (RF) beamformer, (ii) baseband (BB) precoder, (iii) resource allocation (RA) block. First, a single RF beamformer block is built for all subcarriers via the slow time-varying angle-of-departure (AoD) information. For enhancing the energy efficiency, the RF beamformer aims to reduce the hardware cost/complexity and total power consumption via a low number of RF chains. Afterwards, the reduced-size effective channel state information (CSI) is utilized in the design of a distinct BB precoder and RA block for each subcarrier. The BB precoder is developed via regularized zero-forcing technique. Finally, the RA block is built via the proposed GA-RA technique for throughput maximization by allocating the power and subcarrier resources. The illustrative results show that the throughput performance in the MU-mMIMO-OFDM systems is greatly enhanced via the proposed GA-RA technique compared to both equal RA (EQ-RA) and particle swarm optimization based RA (PSO-RA). Moreover, the performance gain ratio increases with the increasing number of subcarriers, particularly for low transmission powers.

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 presents a semi-deterministic groupwise channel estimation method to generate UT-group CSI of user terminal (UT) zones in the service area for the angular-based hybrid precoding (AB-HP) in multi-user massive multiple-input multiple-output (MU-mMIMO) systems based on geospatial data and the fuzzy c-Means (FCM) clustering algorithm. The slow time-varying UT-level channel state information (CSI) between the base station (BS) and all possible UTs are generated by a ray tracing algorithm and grouped into clusters by a proposed FCM clustering. The service area is then divided into a number of non-overlapping UT zones, where each is characterized by a corresponding set of clusters used as UT-group CSI for RF beamformer to eliminate the required large online CSI acquisition overhead. Simulations are performed in both outdoor and indoor scenarios to evaluate the performance of the proposed channel estimation approach. Illustrative results show that the proposed method identifies clusters robust to imprecise UT-level CSI and provides RF beamformer with the UT-group CSI for different UT zones in the service area. Meanwhile, with the UT- group CSI, the AB-HP can successfully achieve a comparable sum-rate performance as the fully-digital precoding (FDP) system for UTs in specific zones without large dimensional CSI overhead.

This work proposes a novel swarm intelligence based power allocation (PA) technique for multi-user massive multiple-input multiple-output (MU-mMIMO) systems. For the downlink transmission, we consider the geometry-based millimeter-wave (mmWave) channel model. The base station (BS) employs a three-dimensional angular-based hybrid precoding (3D-AB-HP) technique requiring low channel state information (CSI) overhead. The 3D-AB-HP architecture consists of three stages: (i) radio frequency (RF) precoder, (ii) baseband (BB) precoder, (iii) multi-user PA block. First, the RF precoder is built via the slow time-varying angle-of-departure information to reduce the CSI overhead size as well as the number of RF chains. It is designed via low cost phase-shifters, which induces the constant modulus constraint at the RF-stage design. Second, the BB precoder utilizes the regularized zero-forcing technique for mitigating the inter-user interference. Third, at the multi-user PA block, we develop a novel particle swarm optimization based PA (PSO-PA) algorithm to maximize the spectral/energy efficiency. Both the BB precoder and the multi-user PA block are constructed via the reduced-size effective channel seen from the BB-stage. Illustrative results reveal that the 3D-AB-HP with PSO-PA can remarkably improve the spectral/energy efficiency compared to the equal PA (e.g., up to 88% at the low/medium transmit power regime). Also, it is shown that the proposed 3D-AB-HP significantly decreases the number of RF chains (e.g., 94.2%) and the CSI overhead size (e.g., 87.1%), while providing higher energy efficiency than the conventional single-stage fully-digital precoding.

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.

Hybrid Precoding (HP) has been introduced to reduce the complexity/costs due to a large number of RF chains in the fully-digital massive MIMO precoding. In a fully-connected (FC) HP, each RF chain is connected to all available antenna elements to exploit the full beamforming capability of the antenna array at the expense of large connectivity. To further reduce costs/complexity associated with this large connectivity, sub-connected (SC) HP considers that each RF chain connected to a subset of selected antenna elements of the antenna array at the costs of inferior performance. This paper aims to study the complexity and performance of both FC-HP and SC-HP. For comparison, we consider a common 2-stage HP scheme with the RF-beamforming (BF) stage designed via the slow time-varying angle-of-departure (AoD) information, using both orthogonal and non-orthogonal BF approaches, while the baseband-precoding stage uses a regularized zero-forcing (RZF) technique. This common HP scheme is used by a base-station equipped with a uniform rectangular large-scale antenna-array to serve multiple single-antenna users clustered in multiple groups. Three sub-array configurations (vertical, horizontal, square) are considered for SC- HP. Illustrative simulation results are provided to compare the performance of FC-HP and SC-HP in various scenarios and indicate that for a 64-element URA and 4 RF chains, SC-HP can achieve 91.46% sum-rate performance of FC-HP with only 25% complexity.

This paper proposes a three-dimensional angular-based hybrid precoding (3D-AB-HP) for multi-cell multi-user massive multiple-input multiple-output (MU-mMIMO) systems. By employing the angle-of-departure (AoD) information, the RF precoder in the proposed 3D-AB-HP is designed to decrease the channel estimation overhead and the number of RF chains. Then, the baseband (BB) precoder in the 3D-AB-HP is constructed via regularized zero-forcing technique using the effective channel seen at the BB. To further mitigate the inter-cell interference for the cell-edge users, we propose three cooperation strategies for the multi-cell downlink transmission in the case of cloud radio access networks (C-RAN): (i) non-cooperation (NC), (ii) partial-cooperation (PC) and (iii) full-cooperation (FC). Illustrative results indicate that FC and PC improve remarkably the sum-rate performance via utilization of C-RAN in comparison to the traditional RAN. Moreover, by means of the directional beamforming, the proposed 3D-AB-HP with PC and NC can outperform the corresponding single-stage fully-digital precoding requiring higher hardware cost/complexity and larger channel estimation overhead.

Hybrid precoding/combining (HPC) architecture is a promising candidate for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. It is capable of reducing the hardware cost/complexity and power consumption compared to the full-digital precoding/combining (FDPC) while keeping the similar spectral efficiency. Most of the prior works on HPC consider the availability of full channel state information (CSI) to design both radio-frequency (RF) and baseband (BB) stages. In this work, an angular-based HPC (AB-HPC) design requiring low CSI overhead is proposed for mmWave massive MIMO systems equipped with low-resolution digital-to-analog converters (DAC) and analog-to-digital converters (ADC). Based on the 3D geometry-based mmWave channel model, the transmit and receive RF beamformers are first developed based on the slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) parameters, respectively. Then, the transmit BB precoder and receive BB combiner are designed by employing the reduced-size effective CSI seen from the BB-stages. Considering the effect of low-resolution DACs/ADCs, the receive BB combiner is obtained by the minimum mean square error (MMSE) criterion. The numerical results reveal that the proposed AB-HPC technique can closely approach the achievable rate performance of FDPC while remarkably reducing the number of power-hungry RF chains and CSI overhead size (e.g., around 94.1% – 98.5%). Moreover, the quantization error occurred due to the low-resolution DACs/ADCs causes a performance floor. For a given signal-to-noise ratio (SNR), we also ask the required number of bits for the low-resolution DACs/ADCs for converging to the same achievable rate performance in full-precision DACs/ADCs.

This paper proposes a new angular-based 3D two- stage hybrid precoding scheme for multi-user massive MIMO systems using uniform rectangular arrays (URA), where users are partitioned into different groups based on the similarity of their angle-of-departure (AoD) information. At first using the user-group AoD ranges, the RF- beamforming stage is designed to reduce the inter- group interference, the number of RF chains, and the channel state information (CSI) overhead. Then, the digital baseband precoder stage is constructed via regularized zero-forcing (RZF) technique using the effective channel seen from baseband to reduce the intra-group interference between the users, considering three approaches: joint-group-processing (JGP), per-group-processing (PGP) and common-group-processing (CGP). Illustrative results indicate that the proposed two-stage hybrid precoding schemes with the reduced hardware cost/complexity and relaxed CSI estimation overhead can closely approach the sum- rate performance of the ideal single-stage fully- digital precoding. Moreover, their performance gap becomes negligible as the array size increases.

In this paper, performance of two-way full-duplex cooperative systems under residual loop- interference (LI) is analyzed in terms of outage probability over Nakagami-$m$ fading channels. At the relay node of the proposed system, physical- layer-network-coding technique and variable-gain full-duplex amplify-and-forward relaying method are combined for two-way transmission. End-to-end signal-to-interference-plus-noise-ratio (SINR) expression is derived for different power transmissions at the source and relay nodes. New exact outage probability expression is obtained in a single-integral form by using cumulative distribution function of the end-to-end SINR. The analytical results are verified by Monte-Carlo simulations. We also provide lower-bound and asymptotic expressions in closed-form for the outage performance. It is shown that the outage performance of the system is enhanced as long as either the transmit power or the efficiency of LI cancellation process increases. We also observe that the outage probability converges to an error floor due to the residual LI component at the source and relay nodes.