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As the demand for high-capacity and energy-efficient wireless communications grows, the exploration of cost-effective and power-optimized hybrid digital-analog beamforming techniques for millimeter-wave massive MIMO systems has gained substantial traction. Adopting low-resolution phase shifters offers a more practical solution in terms of reducing power consumption and implementation costs. We investigate hybrid beamforming strategies for mmWave massive MIMO systems that employ low-resolution phase shifters, with a specific focus on designing and optimizing the analog beamforming component. To exploit the full potential of analog phase shifter network (APSN) in mmWave massive MIMO system while maintaining low-power consumption, we propose a two-layer APSN with low-resolution PSs to replace the conventional single-layer APSN with high-resolution PSs. Under the constraints imposed by the two-layer APSN hardware architecture, we propose a two-level codebook structure and an adaptive-weighted cross-entropy optimization algorithm that offers superior performance while substantially reducing the computational burden compared to the conventional cross-entropy optimization algorithm. We further develop two alternative schemes through the maximal gain harvest method to reduce complexity. We also investigate the case that only one steering vector is shared in the inner beamformer and present two low-complexity beamforming algorithms. Through simulations, the proposed two-layer APSN structure and the proposed algorithms have been shown to outperform existing methods, with notable effectiveness in scenarios where low-resolution phase shifters are utilized. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid beamforming (HBF) structure for gain-aware grouping transmit antennas and users in multiuser multiple-input single-output (MU-MISO) systems. In the conventional HBF structure, all transmit antennas form a beam to each user. In this case, the gain of each antenna varies depending on the location of the base station and each user, and the transmit power after the digital beamformer is allocated to the antenna with the smallest gain. Signals transmitted from antennas with small gains are susceptible to noise and interference. Therefore, this paper proposes an HBF structure in which only the antenna with the highest gain forms the beam for each user. In the proposed scheme, the transmitting antennas are grouped and the beam is formed only by the group of antennas with the highest gain for each user. Simulation results show that the proposed scheme can reduce the number of phase shifters used on the transmit side compared to the conventional HBF scheme while maintaining sum-rate performance when the number of transmit antennas and users are the same. It was also shown that there is a trade-off between the reduction in the number of phase shifters used to form the beam and the improvement in performance as the number of transmit antennas increases. Furthermore, it is shown that when antenna selection is used, although there is a trade-off between the number of phase shifters and performance improvement, the number of phase shifters can be reduced while maintaining performance even when the number of transmit antennas increases. [ABSTRACT FROM AUTHOR]

Millimeter waves (mmWave) are the only viable solution that is practical to fulfil the enormous bandwidth requirement of the modern era communication systems. Accurate estimation of the wireless channels is necessary for mmWave based multiple input multiple output (MIMO) systems that use directional beamforming. Nevertheless, due to the short wavelengths, mmWave channels exhibit significant variability, which poses a major obstacle to their successful recovery within limited training durations traditionally, there are two conventional channel estimation methods have been used for the operation. One is by using the low rank property of channel matrix and the other is to exploit the sparsity of the channel matrix in angle domain. However, the conventional estimation approaches may have necessitated a high-dimensional channel matrix, so substantially augmenting the intricacy of the traditional channel estimation process. This study addresses the channel estimation problem of wideband mmWave MIMO system by utilizing both low rank and sparsity simultaneously. The fundamental concept of the proposed technique is to frame the channel estimation issue as a combined optimization problem that creates an objective function comprising of l 1 - n o r m and nuclear norm minimization problems. The joint optimization problem is tackled using the expanded version of alternating direction method of multipliers (ADMM) algorithm which takes the advantage of the low rank and sparseness of the channel matrix by using the relaxation technique of the variables. The simulation findings clearly illustrate the superiority of proposed approach in comparison to other benchmarks. [ABSTRACT FROM AUTHOR]

Cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) can effectively overcome the shadow fading effect and provide macro gain to boost the throughput of communication networks. Nevertheless, the majority of the existing studies have overlooked the user-centric characteristics and practical fronthaul capacity limitations. To solve these practical problems, we introduce a resource allocation scheme using statistical channel state information (CSI) for uplink user-centric cell-free mmWave MIMO system. The hybrid beamforming (HBF) architecture is deployed at each access point (AP), while the central processing unit (CPU) only combines the received signals by the large-scale fading decoding (LSFD) method. We further frame the issue of maximizing sum-rate subject to the fronthaul capacity constraint and minimum rate constraint. Based on the alternating optimization (AO) and fractional programming method, we present an algorithm aimed at optimizing the users' transmit power for the power allocation (PA) subproblem. Then, an algorithm relying on the majorization–minimization (MM) method is given for the HBF subproblem, which jointly optimizes the HBF and the LSFD coefficients. [ABSTRACT FROM AUTHOR]

This study proposes the integration of a beamforming neural network (BFNN) and multiple-input single-output based non-orthogonal multiple access (MISO-NOMA) with hybrid beamforming (HBF) for cell edge users (CEU) in a millimeter wave (mmWave)-based beyond 5G cellular communication system. This system is referred to as MISO-NOMA-HBFBFNN. The proposed scheme has been implemented to support multiple users simultaneously and also to considerably enhance and significantly improve the overall the sum channel capacity (SC) and user channel capacities. Additionally, the simulation results demonstrate the superiority of the proposed MISO-NOMA-HBF-BFNN scheme over the existing MISONOMA with HBF and MISO-OMA with HBFBFNN based schemes in terms of user capacities and SC. [ABSTRACT FROM AUTHOR]

To achieve the best performance in terms of accuracy and complexity of massive multiple-input multiple-output (Massive MIMO) in wireless communication systems, hybrid beamforming (HBF) is a promising technique that provides high data rate multiplexing gains and enhances the spectral efficiency (SE) of the system. In this paper, a novel received signal strength indicator (RSSI) method is proposed to design an HBF for Massive MIMO BF via multitasking deep learning (DL) that minimizes the reliance on the channel state information (CSI) feedback. The trade-off between the enhancement SE of the system and the deep neural networks (DNNs) performance is optimized, and the results reveal that the proposed novel DL techniques achieve predicted spectral efficiencies with accuracy of 99.23% and 95.64% for Deep-HBF and Deep-AFP, respectively. The processing times for Deep-HBF and Deep-AFP are 709.2914 sec and 1425.864 sec, respectively. Notably, Deep-AFP exhibits a higher range of computational complexity compared to Deep-HBF. It is worth mentioning that the proposed techniques utilize the same DNN architecture. [ABSTRACT FROM AUTHOR]

Due to cost and energy concerns with digital beamformers, much of the beamforming is done by hybrid beamformers in mm-wave (mm) massive multiple input multiple output (MIMO). Various works in hybrid beamforming structures considered either phase shifters, switches, or radio frequency lenses individually as switching mechanisms between antennas and precoding systems. Works that consider the hybrid use of phase shifters, switches, and radio frequency lenses need further investigation since there is a tradeoff between cost and system performance in each switching mechanism. The main aim of this research is to analyze the performance of a hybrid switch, a 1-bit phase shifter, and radio frequency (RF)-Lens in a hybrid beamforming network as a switching network. Simulation results showed that the hybrid of three has a spectral efficiency (SE) performance of 59.04 bps/Hz, which increases by 6.9 bps/Hz from that of the switch and lens antenna array network. The energy efficiency (EE) of the switch, phase shifter, and lens showed a performance of 46.41 bps/Hz/W, while the switch and lens antenna array, phase shifter, and lens antenna array showed a performance of 48.52 bps/Hz/W. The result also shows that the hybrid network achieves optimum performance at the expense of higher computational complexity. [ABSTRACT FROM AUTHOR]

As the world braces for an era of ubiquitous and seamless connectivity, hybrid beamforming stands out as a beacon guiding the evolutionary path of wireless communication technologies. Several hybrid beamforming technologies are explored for millimeter-wave multiple-input multi-output (MIMO) communication. The aim is to provide a roadmap for hybrid beamforming that enhances wireless fidelity. In this systematic review, a detailed literature review of algorithms/techniques used in hybrid beamforming along with performance metrics, characteristics, limitations, as well as performance evaluations are provided to enable communication compatible with modern Wireless Intelligent Transport Systems (WITSs). Further, an in-depth analysis of the mmWave hybrid beamforming landscape is provided based on user, link, band, scattering, structure, duplex, carrier, network, applications, codebook, and reflecting intelligent surfaces to optimize system design and performance across diversified user scenarios. Furthermore, the current research trends for hybrid beamforming are provided to enable the development of advanced wireless communication systems with optimized performance and efficiency. Finally, challenges, solutions, and future research directions are provided so that this systematic review can serve as a touchstone for academics and industry professionals alike. The systematic review aims to equip researchers with a deep understanding of the current state of the art and thereby enable the development of next-generation communication in WITSs that are not only adept at coping with contemporary demands but are also future-proofed to assimilate upcoming trends and innovations. [ABSTRACT FROM AUTHOR]

Abstract As the demand for high-capacity and energy-efficient wireless communications grows, the exploration of cost-effective and power-optimized hybrid digital-analog beamforming techniques for millimeter-wave massive MIMO systems has gained substantial traction. Adopting low-resolution phase shifters offers a more practical solution in terms of reducing power consumption and implementation costs. We investigate hybrid beamforming strategies for mmWave massive MIMO systems that employ low-resolution phase shifters, with a specific focus on designing and optimizing the analog beamforming component. To exploit the full potential of analog phase shifter network (APSN) in mmWave massive MIMO system while maintaining low-power consumption, we propose a two-layer APSN with low-resolution PSs to replace the conventional single-layer APSN with high-resolution PSs. Under the constraints imposed by the two-layer APSN hardware architecture, we propose a two-level codebook structure and an adaptive-weighted cross-entropy optimization algorithm that offers superior performance while substantially reducing the computational burden compared to the conventional cross-entropy optimization algorithm. We further develop two alternative schemes through the maximal gain harvest method to reduce complexity. We also investigate the case that only one steering vector is shared in the inner beamformer and present two low-complexity beamforming algorithms. Through simulations, the proposed two-layer APSN structure and the proposed algorithms have been shown to outperform existing methods, with notable effectiveness in scenarios where low-resolution phase shifters are utilized.

Due to cost and energy concerns with digital beamformers, much of the beamforming is done by hybrid beamformers in mm-wave (mm) massive multiple input multiple output (MIMO). Various works in hybrid beamforming structures considered either phase shifters, switches, or radio frequency lenses individually as switching mechanisms between antennas and precoding systems. Works that consider the hybrid use of phase shifters, switches, and radio frequency lenses need further investigation since there is a tradeoff between cost and system performance in each switching mechanism. The main aim of this research is to analyze the performance of a hybrid switch, a 1-bit phase shifter, and radio frequency (RF)-Lens in a hybrid beamforming network as a switching network. Simulation results showed that the hybrid of three has a spectral efficiency (SE) performance of 59.04 bps/Hz, which increases by 6.9 bps/Hz from that of the switch and lens antenna array network. The energy efficiency (EE) of the switch, phase shifter, and lens showed a performance of 46.41 bps/Hz/W, while the switch and lens antenna array, phase shifter, and lens antenna array showed a performance of 48.52 bps/Hz/W. The result also shows that the hybrid network achieves optimum performance at the expense of higher computational complexity.

Hybrid analog and digital beamforming is gaining attention for its practical application in large-scale antenna systems. It offers significant cost savings, reduced complexity, and lower power consumption compared to entirely digital beamforming, all while maintaining comparable performance. This article proposes a hybrid beamforming architecture aimed at addressing these challenges by using a reduced number of radio frequency (RF) chains while achieving performance comparable to entirely digital schemes. The study demonstrates that matching the number of RF chains to the total number of data streams enables hybrid beamforming to compete effectively with entirely digital beamformers. The adopted approach focuses on computing analog and digital precoders and combiners using the metaheuristic method of genetic algorithms, in a point-to-point multiple input multiple output (MIMO) system scenario. The objective is to simplify the system and reduce costs by optimizing the number of antennas, RF chains, and data streams, all while maintaining comparable performance to entirely digital beamforming. The study's results show that increasing the number of antennas significantly impacts the quality and capacity of the hybrid massive MIMO beamforming system. Conversely, reducing the number of RF chains has a negligible effect on quality and capacity, but simplifies the design and minimizes costs. [ABSTRACT FROM AUTHOR]

Hybrid precoding is considered as a promising low-cost technique for millimeter wave (mm-wave) massive Multi-Input Multi-Output (MIMO) systems. In this work, referring to the time-varying propagation circumstances, with semi-supervised Incremental Learning (IL), we propose an online hybrid beamforming scheme. Firstly, given the constraint of constant modulus on analog beamformer and combiner, we propose a new broad-network-based structure for the design model of hybrid beamforming. Compared with the existing network structure, the proposed network structure can achieve better transmission performance and lower complexity. Moreover, to enhance the efficiency of IL further, by combining the semi-supervised graph with IL, we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning, where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk. Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update, all transmissions, even the ones during the model update, would make contributions to model update in the proposed method. During the model update, the amount of unlabelled transmissions is very large and they also carry some information, the prediction performance can be enhanced to some extent by these unlabelled channel data. Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach. Besides, we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple-input–multiple-output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML)-based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP), consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system's ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing the overall system performance. This work leverages MARL's potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks. [ABSTRACT FROM AUTHOR]

For 6G communications, the Ultra Massive Multiple Input Multiple Output (UM-MIMO) systems with Intelligent Reflecting Surface (IRS) assistance are capable since they can efficiently get beyond the limitations of restricted blockage and coverage. However, in the far field, a robust THz channel sparsity is unfavorable to spatial multiplexing, whereas excessive UM-MIMO and IRS dimensions extend the near field region. To address these issues, a hybrid beamforming IRS assisted UM-MIMO THz system with Deep Siamese Capsule Network is designed with the cascaded channel. The near and far field codebook-based beamforming is developed to model the proposed communication channel. The channel estimation is done based on the deep siamese capsule adaptive beluga whale neural network. The simulation results of the bit error rate, Normalized Mean Square Error (NMSE), spectral efficiency, sum rate, data rate, normalized channel gain, beamforming gain, and array gain loss shows that the proposed system achieves reliable performances compared with existing techniques. The suggested approach also demonstrates the outstanding adaptability to various network configurations and good scalability. The method provides a better channel estimation accuracy and less complexity which shows an NMSE of − 11.2 dB at an SNR of 10 dB. [ABSTRACT FROM AUTHOR]

This paper studies the performance of hybrid digital-analog multi-user multipleinput multiple-output (MU-MIMO) downlink communication based on various antenna systems for 5G applications. The analysis is focused on comprehensive numerical simulations comparing hybrid beamforming schemes for collocated vs distributed phased array antennas (PAA) deployments in two indoor office environments. This study uses measured beamforming radiation patterns from two 28 GHz state-of-the-art PAAs. The channel models employed are the standardized statistical 3GPP 38.901 indoor channel models implemented in the QuaDRiGa software. A beam selection algorithm is implemented to maximize the achievable sum rate, assuming either matched-filtering or zero-forcing precoding. The evaluated figures of merit are the gain of the RF power amplifier, the per-user signal-to-interference-plus-noise ratio (SINR), and the resulting achievable sum-rate capacity. Based on the results, the distributed deployment scenario always shows a higher SINR and achievable sum-rate capacity at the user locations while requiring a lesser amplification of the conducted power. Specifically, the largest PAA with slant-polarized 16 x 4 elements producing 16 horizontal analog beams in combination with zeroforcing digital precoding proved to be the best solution in both open and mixed indoor office environments in absolute values. On the other hand, the array based on the same type of elements but in a more compact realization with 4 x 4 elements, but with beams arranged as 8 horizontal times 2 vertical, offered the most significant relative gains. [ABSTRACT FROM AUTHOR]

The exploiting of hybrid beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems can enhance the system's sum rate while reducing power consumption and hardware costs. However, designing an effective hybrid beamformer is challenging, and interference between multiple users can negatively impact system performance. In this paper, we develop a scheme called Subset Optimization Algorithm-Hybrid Beamforming (SOA-HBF) that is based on the subset optimization algorithm (SOA), which effectively reduces inter-user interference by dividing the users set into subsets while optimizing the hybrid beamformer to maximize system capacity. To validate the proposed scheme, we constructed a system model that incorporates an intelligent reflecting surface (IRS) to address obstacles between the base station (BS) and the users set, enabling efficient wireless communication. Simulation results indicate that the proposed scheme outperforms the baseline by approximately 8.1% to 59.1% under identical system settings. Furthermore, the proposed scheme was applied to a classical BS–users set link without obstacles; the results show its effectiveness in both mmWave massive MIMO and IRS-assisted fully connected hybrid beamforming systems. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) array antenna receivers are widely used to suppress wideband interference in navigation countermeasures. However, existing array antenna receivers all adopt a digital array structure and digital beamforming technique, and they have limited analog-front-end (AFE) dynamic range. In strong interference scenarios, AFE saturation will occur, which limits the maximum interference suppression ability of the array receiver. Aiming at this issue, this paper proposes a robust wideband interference suppression method for GNSS array antenna receivers based on a hybrid beamforming technique. Firstly, a novel, fully connected hybrid array receiver structure is proposed. Secondly, the corresponding hybrid beamforming method is proposed at the same time, and it realizes the complete elimination of the strong wideband interference by joint suppression in the analog domain and digital domain. After mathematical simulations, it is verified that, compared to the digital beamforming-based anti-jamming technique, the proposed method can effectively suppress strong wideband interference, and the maximum interference suppression ability is improved by 36 dB. [ABSTRACT FROM AUTHOR]

Signal loss remains a persistent challenge in communication systems, impacting Multiple Input Multiple Output (MIMO) systems, especially in the millimeter-wave (mm-Wave) context. This paper explores the effectiveness of the proposed Hybrid Precoding/Combining Design (HPCD) algorithm within a fully connected structure of an mm-Wave downlink massive MIMO system. The primary objective is to enhance the overall system's performance, specifically focusing on improving spectral efficiency. Simulation results consistently demonstrate the superiority of the HPCD algorithm over state-of-the-art techniques, revealing substantial improvements in spectral efficiency. This thorough examination highlights the potential of the proposed approach, positioning it as a compelling solution for next-generation communication networks. The findings are anticipated to significantly contribute to spectral efficiency optimization, facilitating the seamless integration of the proposed technique into practical communication scenarios. [ABSTRACT FROM AUTHOR]

Due to the increasing number of users and high data rate requirements for multimedia applications in fifth-generation (5G) and beyond wireless systems, it's necessary to incorporate spectral efficient techniques to overcome the limited available resources. The proposal of this paper is to employ a technique that improves the performance of a multi-user-massive multiple input multiple output orthogonal frequency division multiplexing (MUmMIMO-OFDM) system with hybrid beamforming (HBF). The spectral efficiency (SE) is enhanced by incorporating the non-orthogonal multiple access (NOMA) technique within each group of users. Multiple streams of users in a group are served with typical beams that could serve a single stream in existing MU-mMIMOOFDM systems. Two different scenarios are considered for performance analysis. The SE reaches about 60 bits/s/HZ at 5dB signal-to-noise ratio (SNR) when the base station (BS) is equipped with 64 antennas. The simulation results proved that the proposed MU-mMIMO-NOMA-OFDM system outperforms the existing models, where the user's SE is enhanced by 117% when 128 antennas are deployed at the BS. [ABSTRACT FROM AUTHOR]

Hybrid beamforming has gained popularity in recent years due to its ability to enhance the performance of massive multiple-input multiple-output (massive MIMO) systems and improve the efficiency of wireless communication. However, designing a hybrid precoder is a challenging task as it requires accurate channel state information (CSI) and needs to solve an optimization problem. This paper introduces an unsupervised hybrid deep learning technique, namely CNN-BiLSTM, for developing hybrid beamforming in massive MIMO systems based on received signal strength indicator (RSSI) without having prior knowledge of CSI. Additionally, incremental principal component analysis (Incremental PCA) is used to reduce the dataset's dimensionality. The results of our proposed model demonstrate a significant improvement in spectral efficiency. This technique reduces the training overhead significantly and is able to quickly serve all the users. The simulation results indicate that proposed model produces low side lobes when providing coverage to areas. As a result, the performance of our model has a noticeable advantage compared to other models. In addition, proposed method achieves 99.21% accuracy and 5.30 bps/Hz sum-rate that is close to optimal performance and outperforms the state-of-the-art method.

Summary: The advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter‐wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave‐based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub‐connected with phase shifter selection (SPSS), and fully connected and sub‐connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency. [ABSTRACT FROM AUTHOR]

Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied in this paper. The ideal case is first considered where the mmWave half-duplex (HD) AF relay system operates with channel state information (CSI) accurately known. In order to tackle the non-convex problem, a manifold optimization (MO)-based alternating optimization algorithm is proposed, where an optimization problem containing only constant modulus constraints in Euclidean space can be converted to an unconstrained optimization problem in a Riemann manifold. Furthermore, considering more practical cases with estimation errors of CSI, we investigate the robust joint HBF design with the system operating in full-duplex (FD) mode to obtain higher spectral efficiency (SE). A null-space projection (NP) based self-interference cancellation (SIC) algorithm is developed to attenuate the self-interference (SI). Different from the traditional SI suppression algorithm, there's no limit on the number of RF chains. Numerical results reveal that our proposed algorithms has a good convergence and can effectively deal with the influence of different CSI estimation errors. A significant performance improvement can be achieved in contrast with other approaches. [ABSTRACT FROM AUTHOR]

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This paper studies the performance of hybrid digital-analog multi-user multiple-input multiple-output (MU-MIMO) downlink communication based on various antenna systems for 5G applications. The analysis is focused on comprehensive numerical simulations comparing hybrid beamforming schemes for collocated vs distributed phased array antennas (PAA) deployments in two indoor office environments. This study uses measured beamforming radiation patterns from two 28 GHz state-of-the-art PAAs. The channel models employed are the standardized statistical 3GPP 38.901 indoor channel models implemented in the QuaDRiGa software. A beam selection algorithm is implemented to maximize the achievable sum rate, assuming either matched-filtering or zero-forcing precoding. The evaluated figures of merit are the gain of the RF power amplifier, the per-user signal-to-interference-plus-noise ratio (SINR), and the resulting achievable sum-rate capacity. Based on the results, the distributed deployment scenario always shows a higher SINR and achievable sum-rate capacity at the user locations while requiring a lesser amplification of the conducted power. Specifically, the largest PAA with slant-polarized 16 × 4 elements producing 16 horizontal analog beams in combination with zero-forcing digital precoding proved to be the best solution in both open and mixed indoor office environments in absolute values. On the other hand, the array based on the same type of elements but in a more compact realization with 4 × 4 elements, but with beams arranged as 8 horizontal times 2 vertical, offered the most significant relative gains.

This paper presents an innovative approach for terahertz (THz) band communications, utilizing reconfigurable intelligent surfaces (RISs) to implement an angular-based hybrid beamforming (AB-HBF). This study examines two significant THz channel scenarios with cost-efficient solutions utilizing RISs to enhance performance. The first scenario ensures reliable communication in the presence of obstacles blocking the direct path, where RISs offer an alternative transmission path. The second scenario leverages multiple-RIS in line-of-sight dominant or sparse environments to increase the rank of the channel matrix, consequently improving the achievable rate. By introducing an innovative HBF design for multiple-RIS-assisted THz massive MIMO systems, a three-stage design is proposed based on a geometry-based THz channel model. This design encompasses the transmit and receive radio frequency (RF) beamformers, transmit/receive baseband (BB) precoder/combiner, and RIS phase shift matrix. A particle swarm optimization-based solution is employed to design the RIS phase reflections. This design methodology is extended to the multiple-RIS-aided scenario, optimizing the phase shift matrices of each RIS. We also provide sub-connected AB-HBF configuration by offering a remarkable reduction in the total number of phase shifters compared to the fully-connected AB-HBF. Additionally, a deep learning-based phase shift design is incorporated to effectively optimize the RIS configurations, significantly reducing the computational time required for system calibration in dynamic THz environments. Extensive numerical experiments demonstrate the significant enhancement in the achievable rate of THz AB-HBF systems by incorporating RISs, mitigating the wireless propagation disruptions as well as reducing hardware cost/complexity and power consumption for massive MIMO systems.

In this paper, we explore the problem of utilizing Integrated Access and Backhaul (IAB) technology in Non-Terrestrial Networks (NTN), with a particular focus on aerial access networks. We consider an Uncrewed Aerial Vehicle (UAV)-based wireless network comprised of two layers of UAVs: (a) a lower layer consisting a number of flying users and a UAV Base Station (BS) that provides coverage for terrestrial users and, (b) an upper layer designated to provide both wireless access for flying users and backhaul connectivity for UAV BS. By adopting IAB technology, the backhaul and access links collaboratively share their resources, enabling aerial backhauling and the utilization of the same infrastructure and frequency resources for access links. A sum-rate maximization problem is formulated by considering aerial backhaul constraints to optimally allocate the frequency spectrum between aerial and terrestrial networks. We decompose the resulting non-convex optimization problem into two sub-problems of beamforming and spectrum allocation and then propose efficient solutions for each. Numerical results in different scenarios yield insightful findings about the effectiveness of using the IAB technique in aerial networks.

Terahertz (THz) band communication, operating in the range of $(0.1-10)\times 10^{12}$ Hz, holds immense potential for fulfilling the ever-increasing demand for ultra-high data rate wireless communication. However, the unfavorable channel characteristics of THz communication pose a significant challenge in realizing this promising technology. In this paper, we present a novel solution to this challenge by proposing a wideband THz hybrid multiple-input multiple-output (MIMO) system design that leverages the ultra-wide bandwidth and the high spectral efficiency of MIMO schemes. We address the frequency-dependent nature of the THz channel by using a filter-bank-based channel model and a multi-bank orthogonal frequency division multiplexing (MB-OFDM) waveform design, which slices the wideband channel into smaller frequency-independent subbands. Specifically, we illustrate two different THz hybrid MIMO structures, one with a single precoder and combiner in the high sampling domain and another with a band-wise precoder design in the low sampling domain. We have adopted sub-optimal methods that alternatively optimize the beamformers. We evaluate the performance of our proposed MIMO schemes using numerical simulations that validate their feasibility and effectiveness in achieving communication in ultra-wideband frequency-dependent THz channels under various parameters and subband width assumptions. The results also indicate that the proposed MB-OFDM waveform design reduces the peak-to-average power ratio (PAPR) of the spatial streams compared to conventional OFDM.

This paper proposes a scheme to integrate data communication, wireless energy harvesting and anti-eavesdropping functions into a hybrid beamforming downlink system to satisfy the demand for expanded functionalities in a communication system. To solve this difficult nonconvex problem, the Semidefinite Relaxation (SDR) method is employed to obtain the digital transmitter, the SDR method and penalty convex-concave procedure (CCP) method are employed to obtain the analog transmitter. Simulation results shows four conclusions about the system. Firstly, the larger the SINR target or the energy harvest target, the more transmit power is required. Secondly, the lower the data rate obtained by the eavesdropper, the more transmit power is required. Thirdly, the SDR method and the penalty CCP method complement each other. Finally, the transmit power can be reduced by increasing the number of RF chains or transmit antennas.

In-band full-duplex (FD) operation can double the spectral efficiency of massive multiple-input multiple-output (mMIMO) systems by allowing simultaneous transmission and reception over the same time/frequency slot. However, the main challenge encountered in implementing an FD radio wireless transceiver is to greatly suppress the residual self-interference (SI) generated from its own transmitter below the receiver noise floor to avoid performance degradation in the detection of the signal of interest received from remote transmitters. It is pointed out that the single-stage digital beamforming (DBF) scheme would introduce an impractically high complexity unsuitable for implementing FD-mMIMO. This paper considers an FD hybrid beamforming (FD-HBF) scheme for multi-user (MU)-mMIMO systems, in which large SI-suppression is achieved by exploring dynamic transmitter/receiver (Tx/Rx) isolation using RF-beamforming designs, and exploiting the degrees of freedom of a large number of antenna elements in separate Tx and Rx antenna arrays, in conjunction with high-performance baseband (BB) digital fractionally-spaced (FS) SI-cancellation (SIC) techniques. This paper starts with a discussion of the main parameters and impairments (in an mMIMO transceiver) that can impact the SI-suppression performance and the proposed RF-beamforming Tx/Rx isolation and BB SI-cancellation design strategies along with their achieved performance and complexities. Joint-beamforming optimization to maximize both Tx/Rx performance and isolation is discussed with an example of a nature-inspired optimization scheme to achieve an average Tx/Rx RF isolation of 64.4 dB and the best isolation of 76 dB. The paper continues with a description of an FD-mMIMO transceiver prototype using Tx/Rx 8x8-element antenna arrays along with analytical, simulation and measured results. Illustrative results indicate that the proposed combination of RF-beamforming Tx/Rx isolation and BB digital fractionally spaced SI-cancellation can offer an overall SI suppression of more than 131 dB and bring the residual SI below the receiver noise floor.

In this paper, we propose clustering schemes appropriate for a cell-free massive multi-input multi-output (CF mMIMO) system with centralized hybrid beamforming (BF) in the millimeter wave (mmWave) band, with the aim of reducing computational complexity and achieving scalability at the central processing unit (CPU) while ensuring system performance. Conventional access point (AP)-wise clustering schemes have been proposed for a decentralized architecture that mitigates inter-user interference at each AP through local digital BF and analog BF of each radio frequency (RF) chain to efficiently improve performance. However, in a centralized architecture, in which the digital BF weights is designed at the CPU while considering all RF chains of all APs together. In cases where each AP employs multiple RF chains forming different analog beams, the coupling loss (the sum of the path loss and BF gain of the analog beam) of each RF chain may differ even between the same AP and UE due to differences in the analog BF gain. AP-wise clustering is not well suited because RF chains with a high coupling loss (the sum of the path loss and BF gain of the analog beam) may be included among the APs of the cluster assigned to each UE. In the method proposed in this paper, clusters are formed on a per-RF-chain basis (RF chain-wise clustering). In this way, RF chains with lower coupling loss can be selected for each UE regardless of to which AP an RF chain belongs when forming a cluster for each UE. Moreover, the existing clustering schemes do not account for the amount of inter-cluster interference. Accordingly, we propose a cluster recombination scheme to effectively mitigate inter-cluster interference. Then, by combining RF chain-wise clustering and cluster recombination, a higher received signal power can be achieved while mitigating inter-cluster interference. Through simulation-based evaluations, we show that the proposed RF chain-wise clustering and cluster recombination schemes can achieve superior spectral efficiency while effectively reducing the complexity of centralized digital BF.

The directions and gains of the paths with significant power can reconstruct mm-wave channel estimation, attributed to spatial sparsity resulting from severe propagation loss. Leveraging this feature, the two-dimensional beamspace method of direction estimation (2-D BMODE) is developed to estimate the path directions, including angles of departure and arrival. The least-squares(LS) method is then employed to estimate the path gains. Different from the beamspace two-dimensional multiple signal classification (MUSIC) method, the method proposed in this paper significantly ruduces computational complexity without spectral search. Moreover, the DODs and DOAs can be paired automatically by reduced-dimension(RD) MUSIC. Meanwhile, the performance of this method deteriorates when the covariance matrix of gains isn’t of full column rank. This paper also enhances the performance using spatial smoothing technique and oblique projecting to overcome the influence of rank deficiency. The 2-D BMODE method, as illustrated in this paper, outperforms in terms of both unity in computational complexity and estimation accuracy. Numerical simulations validate the advantages and demonstrate the method can serve as a superior alternative.

This paper introduces a novel spatial domain-based self-interference cancellation (SIC) method named constrained minimum mean square error (C-MMSE) for massive multiple-input multiple-output (mMIMO) full-duplex (FD) communication systems. The main idea involves treating the self-interference (SI) signal emitted from an FD node as a distinct spatial stream arriving at the receiver part of that same FD node. This SI signal needs to be spatially postcoded alongside other relevant signals coming from different transmitters, ensuring it falls into the null space of the MIMO channel, which includes the FD node transmitter part as an input. To establish this approach, we first adapt the expressions of spatial combiners for conventional zero forcing (ZF) and minimum mean square error combining (MMSE) criteria. We demonstrate that MMSE alone is insufficient for efficient SI signal cancellation unless an additional constraint is added to enable proper SIC. Consequently, we design the innovative C-MMSE combiner and derive its expression. In addition to our proposal, the originality of our work stems from employing a spherical wave model (SWM) to model the SI channel. The choice is justified by the proximity of the transmit and receive antenna panels in the FD node. We examine and compare the SIC performance of the modified ZF combiner, the modified MMSE combiner, and the newly introduced C-MMSE combiner by evaluating the spectral efficiency (SE). Additionally, we highlight the robustness of the SWM-based SI channel modeling in comparison to conventional planar wave modeling (PWM), emphasizing the relevance of its usage.

In this paper, we consider a simultaneous wireless information and power transfer (SWIPT) system adopting hybrid analog-digital beamforming where a base station (BS) with massive antennas transmits different data streams to multiple single-antenna devices. Our primary objective is to minimize the transmit-power of the BS while ensuring energy harvesting and signal-to-interference-plus-noise ratio (SINR) requirements of each device. We specifically highlight that our approach can accommodate the scenarios where users have different requirements on the harvested energy and the received SINR. We propose two suboptimal solutions in which digital and analog precoders are designed jointly. In the first solution (OptDig-ProRF), the objective function is approximated to be independent of the digital precoder and power splitting ratios, and the local minimum of the objective function is found to obtain an analog precoder. Subsequently, the optimal digital precoder and power splitting ratios are found for the obtained analog precoder using semidefinite relaxation of the original problem. In the second solution (ZfDig-SohRF), to reduce computational complexity, we newly design a zero-forcing (ZF) digital precoder which meets users’ requirements while applying the corresponding analog precoder proposed in the literature. The two precoders are iteratively found until the transmit power difference between two consecutive updates falls below a threshold. Illustrative results show that the OptDig-ProRF scheme yields a very close performance to the fully digital precoder in terms of transmit power, while the ZfDig-SohRF scheme outperforms the optimal digital precoder plus a conventional analog precoder scheme at high SINR constraints.

The hybrid beamforming is a promising technology for the millimeter wave MIMO system, which provides high spectrum efficiency, high data rate transmission, and a good balance between transmission performance and hardware complexity. The most existing beamforming systems transmit multiple streams by formulating multiple orthogonal beams. However, the Neural network Hybrid Beamforming (NHB) adopts a totally different strategy, which combines multiple streams into one and transmits by employing a high-order non-orthogonal modulation strategy. Driven by the Deep Learning (DL) hybrid beamforming, in this work, we propose a DL-driven non-orthogonal hybrid beamforming for the single-user multiple streams scenario. We first analyze the beamforming strategy of NHB and prove it with better Bit Error Rate (BER) performance than the orthogonal hybrid beamforming even with the optimal power allocation. Inspired by the NHB, we propose a new DL-driven beamforming scheme to simulate the NHB behavior, which avoids time-consuming neural network training and achieves better BERs than traditional hybrid beamforming. Moreover, our simulation results demonstrate that the DL-driven non-orthogonal beamforming outperforms its traditional orthogonal beamforming counterpart in the presence of sub-connected schemes and imperfect Channel State Information (CSI).

Cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) can effectively overcome the shadow fading effect and provide macro gain to boost the throughput of communication networks. Nevertheless, the majority of the existing studies have overlooked the user-centric characteristics and practical fronthaul capacity limitations. To solve these practical problems, we introduce a resource allocation scheme using statistical channel state information (CSI) for uplink user-centric cell-free mmWave MIMO system. The hybrid beamforming (HBF) architecture is deployed at each access point (AP), while the central processing unit (CPU) only combines the received signals by the large-scale fading decoding (LSFD) method. We further frame the issue of maximizing sum-rate subject to the fronthaul capacity constraint and minimum rate constraint. Based on the alternating optimization (AO) and fractional programming method, we present an algorithm aimed at optimizing the users’ transmit power for the power allocation (PA) subproblem. Then, an algorithm relying on the majorization–minimization (MM) method is given for the HBF subproblem, which jointly optimizes the HBF and the LSFD coefficients.

As the world braces for an era of ubiquitous and seamless connectivity, hybrid beamforming stands out as a beacon guiding the evolutionary path of wireless communication technologies. Several hybrid beamforming technologies are explored for millimeter-wave multiple-input multi-output (MIMO) communication. The aim is to provide a roadmap for hybrid beamforming that enhances wireless fidelity. In this systematic review, a detailed literature review of algorithms/techniques used in hybrid beamforming along with performance metrics, characteristics, limitations, as well as performance evaluations are provided to enable communication compatible with modern Wireless Intelligent Transport Systems (WITSs). Further, an in-depth analysis of the mmWave hybrid beamforming landscape is provided based on user, link, band, scattering, structure, duplex, carrier, network, applications, codebook, and reflecting intelligent surfaces to optimize system design and performance across diversified user scenarios. Furthermore, the current research trends for hybrid beamforming are provided to enable the development of advanced wireless communication systems with optimized performance and efficiency. Finally, challenges, solutions, and future research directions are provided so that this systematic review can serve as a touchstone for academics and industry professionals alike. The systematic review aims to equip researchers with a deep understanding of the current state of the art and thereby enable the development of next-generation communication in WITSs that are not only adept at coping with contemporary demands but are also future-proofed to assimilate upcoming trends and innovations.

Massive MIMO embraces digital beamforming techniques to direct power to the intended users. However, digital beamforming is power-consuming as each antenna has a dedicated RF chain. To address this issue, a hybrid beamforming architecture was proposed, where analog beamforming is combined with a low dimension digital beamforming. Consequently, the number of RF chains was significantly reduced. The conventional hybrid beamforming architecture deploys phase shifters in analog beamforming, which consumes large power as the number of antennas increases. This work proposes a phase interpolator-based analog beamforming to reduce the number of phase shifters in the fully connected hybrid beamforming architecture. Additionally, we have formulated an optimization problem to search for the best analog and digital precoding in order to maximize spectral efficiency. Solving this problem, we have proposed a tabu search (TS) based algorithm, which uses a genetic algorithm crossover feature to search for the new neighbor. Simulation results show that the proposed method achieves high spectral efficiency and energy efficiency. Specificall, when SNR = 0 dB, the proposed method achieves 95% spectral efficiency of an optimal solution, and achieves 20% higher energy efficiency than conventional method. Furthermore, the proposed method achieves high spectral efficiency with low computational complexity, in comparison to conventional methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple-input–multiple-output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML)-based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP), consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system’s ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing the overall system performance. This work leverages MARL’s potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks.

The exploiting of hybrid beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems can enhance the system’s sum rate while reducing power consumption and hardware costs. However, designing an effective hybrid beamformer is challenging, and interference between multiple users can negatively impact system performance. In this paper, we develop a scheme called Subset Optimization Algorithm-Hybrid Beamforming (SOA-HBF) that is based on the subset optimization algorithm (SOA), which effectively reduces inter-user interference by dividing the users set into subsets while optimizing the hybrid beamformer to maximize system capacity. To validate the proposed scheme, we constructed a system model that incorporates an intelligent reflecting surface (IRS) to address obstacles between the base station (BS) and the users set, enabling efficient wireless communication. Simulation results indicate that the proposed scheme outperforms the baseline by approximately 8.1% to 59.1% under identical system settings. Furthermore, the proposed scheme was applied to a classical BS–users set link without obstacles; the results show its effectiveness in both mmWave massive MIMO and IRS-assisted fully connected hybrid beamforming systems.

In the IRS-aided THz communication system, in order to break the channel sparsity limitation on the system spatial multiplexing gain, a hybrid beam forming architecture with wide-spaced multi-subarrays at the transceiver/transmitter side was proposed and the transmission scheme of IRS multi-partition-aided THz multi-subarrays was designed.Firstly, a non-convex objective function with multivariate coupling and non-convex constraints was constructed based on the principle of maximizing spectrum efficiency.Then, the optimization problem was decoupled into two easy-to-solve sub-problems, namely, the reflection coefficient matrix design of IRS and the hybrid beamforming matrix design at the transceiver/receiver.Finally, the Riemannian manifold optimization algorithm was used to calculate the reflection coefficient matrix of IRS, and the closed solution of the hybrid beamforming matrix design at the transceiver/receiver was obtained through mathematical derivation.Simulation results show that compared with the baseline scheme, the proposed scheme can achieve better spectrum efficiency.

Unmanned aerial vehicles (UAVs) can support low-cost, highly mobile communications while making it possible to establish dedicated terrestrial networks. To overcome the pointing error (PE) and beam misalignment of millimeter-wave large-scale multiple-input multiple-output/free-space optics (MIMO/FSO) caused by UAV jitter, a millimeter-wave massive MIMO/FSO hybrid beamforming method based on tensor train decomposition is proposed. This approach is used for reconfigurable intelligent surface (RIS) network-assisted UAV millimeter-wave massive MIMO/FSO to improve system spectral efficiency. Firstly, the high-dimensional channel of RIS-assisted millimeter-wave massive MIMO/FSO in UAV is represented as a low-dimensional channel by tensor training decomposition. Secondly, the two-way gated recursive unit attention neural network model can effectively solve the FSO PE caused by UAV jitter, and the fast fading channel and Doppler frequency shift are estimated by the Fast Cyclic Tensor Power Method (FCTPM) based on tensor training decomposition. Finally, the RIS phase shift matrix is optimized by singular value decomposition. The hybrid beamforming and RIS phase-shift matrices were estimated by using the low-complexity phase extraction alternating minimization method to solve the beam misalignment problem. Simulation results show that by using the proposed method, the spectrum utilization rate is improved by 23.6% compared with other methods. [ABSTRACT FROM AUTHOR]

In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multipleoutput (MIMO) systems is presented. The ResNeSt-based deep learning method is harnessed to simplify and optimize the beamforming process, consequently improving performance and efficiency of 5G and beyond communication networks. A study of beamforming capabilities has revealed potential to maximize channel capacity while minimizing interference, thus eliminating inherent limitations of the traditional methods. The proposed model shows superior adaptability to dynamic channel conditions and outperforms traditional techniques across various interference scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a new user pairing technique for a power domain nonorthogonal multiple access (NOMA) deploying fully connected and subconnected hybrid beamforming (HBF) structures for a typical urban microcell downlink of line-of-sight (LOS) and non-line-of-sight (NLOS) surroundings. NOMA system's configurations are set up for two (multiple input single output) users per cluster down-linking base station equipped with 128 antennas. HBF processing adopts phased zero forcing (P-ZF) for both fully connected (HBFNOMA) structure (FCS) and sub-connected (HBF-NOMA) structure (SCS) precoders' optimization, and successive interference cancellation zero forcing (SIC-ZF) schemes to optimize the SCS-HBF-NOMA precoder exploiting dynamic power allocation. The new users' pairing exploits users' distance to the base station, namely near and far clusters different from the benchmark angle of arrival-based users' pairing. The proposed users' pairing and the precoding schemes' impact are investigated for finite-resolution HBF structures operating in LOS and NLOS surroundings. The execution under New York University (NYU) mmW channel model is explored for two users in a cluster with different angles of arrival. Results show that the users' pairing based on AoA performs better than the newly proposed users' pairing. However, the proposed users' pairing scheme performs better than their corresponding OMA counterparts, which still make them beneficial for multiple access technique and a scenario, where one of the far cluster users need to access the base station for high data rate service and vice versa. Finally, the modified Liang processing scheme for quantizing SCS-HBF-NOMA precoder is capable of mitigating the quantization error arising from the NLOS environment and the nature of sub-connected (HBF) structure under a low SNR regime. [ABSTRACT FROM AUTHOR]