Proposed Hybrid Beamforming Research Articles

Adaptive Non-Uniform Hybrid Beamforming for mmWave Train-to-Ground Communications in High-Speed Railway Scenarios

Higher frequency bands, especially millimeter wave (mmWave), are a key enabler for future high speed railway (HSR) wireless communication systems. Hybrid analog and digital beamforming (HBF) schemes operating at mmWave frequency bands can exploit the sparse nature of the channel by using large scale antenna arrays (LSAs) to shape multiple beams that overcome the severe path loss. However, due to the long and straight geometric characteristic of the rail track, there is a significant variance in the train-to-ground communication rate when the train is near or far from the BS. To address this issue, a novel hybrid beamforming scheme is developed based on a non-uniform beam design. We first model and investigate different beam design schemes in terms of spectral efficiency. Then, an efficient straight route based adaptively analog beamforming scheme with the non-uniform beam is proposed for HSR, which is designed to resist path loss. Finally, an adaptive non-uniform HBF algorithm based on the orthogonal matching pursuit method (OMP) is developed to minimize the rate gap between different locations by selecting the appropriate predefined beams. Numerical results show that the proposed hybrid beamforming scheme can efficiently ensure signal coverage, and reduce the variance of the train-to-ground communication rate along a straight track route.

Hybrid Beamforming With Sub-Connected Structure for MmWave Massive Multi-User MIMO Relay Systems

In this paper, we investigate the hybrid beamforming design with sub-connected structure for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) amplify-and-forward (AF) relay systems. Considering the constant-modulus and block-diagonal constraints, an iterative algorithm is proposed to sequentially design the analog beamforming of the base station, relay, and users. Next, the relay baseband combiner is designed by transforming the highly complicated non-convex mutual information maximization problem into an easily tractable weighted minimum mean squared error one. To mitigate the inter-user interference, a successive serial interference cancellation based and a piecewise successive approximation based methods are developed for the uniform sparse distribution and dense distribution scenarios, respectively. Simulation results demonstrate that the two proposed hybrid beamforming schemes can achieve good performance in terms of sum-rate and energy efficiency, and outperform benchmarks significantly. In addition, the two proposed schemes are shown to be robust to imperfect channel state information (CSI), even though they are studied based on perfect CSI.

Hybrid Beamforming for Multi-User Millimeter-Wave Heterogeneous Networks

Millimeter-wave (mmWave) communications are a critical technique with next-generation network characteristics such as ultra-dense small cells can meet the skyrocketing demand for mobile data. Hybrid precoding, which combines analog and digital processing to provide both spatial diversity gains and beamforming, is commonly studied for mmWave communications to lower the power and cost consumption of radio frequency (RF) networks. However, the combination of ultra-dense small cells and ever-increasing data traffic results in massive interference. In this paper, we propose a minimum mean square error (MMSE)-based hybrid beamforming strategy for downlink mmWave MIMO two-tier heterogeneous networks (HetNets). The analog beamforming is generalized by an orthogonal matching pursuit technique. The analog beamforming problem is formulated as a sparse signal recovery problem. An MMSE-based digital beamforming algorithm is proposed to minimize the sum MSE of the user-intended data streams so that the inter- and intra-tier interferences are mitigated iteratively. The simulation results demonstrate the advantageous performance of the proposed hybrid beamforming schemes under different cellular cooperation and data transmission scenarios when hardware constraints are taken into account.

Hybrid beamforming for millimeter-wave massive MIMO heterogeneous networks

In this paper, we investigate the hybrid beamforming designs in millimeter wave (mmWave) massive MIMO relaying heterogeneous networks (HetNet), where small cells are overlaid onto the service area of a macrocell. The deployment of HetNet and utilization of the adequate bandwidth in the mmWave band are capable of addressing the explosive growth of data rates in next-generation wireless networks. However, the severe path loss and attenuation in the mmWave band and complex interference in HetNet should be addressed. For the macrocell, we propose a minimum mean squared error (MMSE)-based alternative iterative hybrid analog/digital beamforming algorithm for the downlink multi-user mmWave relaying system. With the constant modulus constraints and the higher-order polynomial function, we reformulate the optimization problem as three convex quadratic subproblems, and the coupling approximate optimal precoders and combiners are jointly obtained by the proposed alternative iterative algorithm. For the picocells, we propose a hybrid beamforming algorithm for downlink mmWave massive MIMO system. An alternative iterative optimization algorithm based on manifold optimization is developed to approximate the optimal beamforming designs. The baseband digital beamforming scheme is investigated by leveraging the mean square error (MSE) criteria for macrocell users and picocell users to manage cross-tier and inter-user interference. Simulation results demonstrate the obvious advantages of the proposed hybrid beamforming algorithms in terms of achievable sum-rate for various setting scenarios.

Multi-UAV Aided Millimeter-Wave Networks: Positioning, Clustering, and Beamforming

In this paper, we propose to employ multiple unmanned aerial vehicle (UAV) base stations to serve ground users in the millimeter-wave (mmWave) frequency bands. To improve the spectrum efficiency, uniform planar arrays are equipped at the UAVs and users for compensation of the high path loss and for mitigation of interference. We formulate a problem to jointly optimize the UAV positioning, user clustering, and hybrid analog-digital beamforming (BF) for the maximization of user achievable sum rate (ASR), subject to a minimum rate constraint for each user. Since the problem is highly non-convex and involves high-dimensional variable matrices and combinatorial programming variables, we develop a suboptimal solution via alternating optimization, successive convex optimization, and combinatorial optimization. First, we design the UAV positioning and user clustering under the assumption of ideal beam patterns, which significantly decouples the UAV positioning and directional BF. Then, the transmit and receive BF variables are successively optimized to approach the ideal beam patterns. Our simulation results verify the convergence and superiority of the proposed algorithm. Significant performance gains can be obtained compared to some benchmark schemes in terms of the ASR, and the proposed hybrid BF solution closely approaches a performance bound given by fully-digital BF.

Self-Organizing mmWave MIMO Cell-Free Networks With Hybrid Beamforming: A Hierarchical DRL-Based Design

In a cell-free wireless network, distributed access points (APs) jointly serve all user equipments (UEs) within their coverage area by using the same time/frequency resources. In this paper, we develop a novel downlink cell-free multiple-input multiple-output (MIMO) millimeter wave (mmWave) network architecture that enables all APs and UEs to dynamically self-partition into a set of independent cell-free subnetworks in a time-slot basis. For this, we propose several network partitioning algorithms based on deep reinforcement learning (DRL). Furthermore, to mitigate interference between different cell-free subnetworks, we develop a novel hybrid analog beamsteering-digital beamforming model that zero-forces interference among cell-free subnetworks and at the same time maximizes the instantaneous sum-rate of all UEs within each subnetwork. Specifically, the hybrid beamforming model is implemented by using a novel mixed DRL-convex optimization method in which analog beamsteering between APs and UEs is conducted based on DRL while digital beamforming is modeled and solved as a convex optimization problem. The DRL models for network clustering and hybrid beamsteering are combined into a single hierarchical DRL design that enables exchange of DRL agents&#x2019; experiences during both network training and operation. We also benchmark the performance of DRL models for clustering and beamsteering in terms of network performance, convergence rate, and computational complexity. Results show a significant rate enhancement due to the proposed hybrid beamforming scheme compared to its conventional all-digital counterpart. This performance enhancement becomes more significant as the number of network partitions increases. For DRL-based network clustering, the policy gradient (PG) algorithm offers the best possible performance in terms of stability and convergence rate while the state-action-reward-state-action (SARSA) algorithm suffers from significant variance, slower convergence, and slightly inferior performance than other algorithms. For DRL-based beamsteering, the soft actor-critic (SAC) algorithm with continuous action space shows the best performance. Also, online training of the agents with varying channel state information (CSI) is observed to increase the variance of the Q-values and decrease the convergence rate, with no significant effect on the average reward. The simulation codes are available at: <monospace><uri>https://github.com/yasser-aleryani/mmWaveCellFree.git</uri></monospace>

2-D Orthogonal Multibeam Antenna Arrays for 5G Millimeter-Wave Applications

In this article, a 2-D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula> Butler matrix-based passive multibeam antenna array (MBAA) and a 2-D active–passive hybrid MBAA (APHMBAA) are both presented for fifth-generation (5G) millimeter-wave (mmWave) applications, which demonstrate that a 2-D coverage can be achieved by two types of 1-D beamformers in two orthogonal planes at analog beamforming level. For the 2-D passive MBAA (PMBAA), the 16 beams are controlled by two stages of substrate integrated waveguide (SIW) Butler matrices placed orthogonally. A novel 2-D interconnection method for connecting these two-stage Butler matrices is implemented based on the flexibility of the SIW without any connectors, cables, and soldering operations and then possesses the advantages of low cost, extensible, and integrated easily in the mmWave bands. The measured results exhibit that the operating bandwidth is 15.38% for the single-stage Butler matrix-based antenna array. Meanwhile, the maximal beam gain of the 2-D PMBAA is 9.4 dBi and its accurate beam directivity is also observed. For the 2-D APHMBAA, the multiple beams are generated by the Butler matrix-based passive beamformers in the vertical direction, whereas the beamforming on the horizontal direction is realized by the active networks utilizing mmWave active beamformer chips. Compared to the proposed 2-D PMBAA, the horizontal scanning performance of the hybrid framework is more flexible. What is more, its costs and complexity are both reduced tempestuously in comparison with an active multibeam array. The measured results show that the maximum measured beam gain of the array is 10.9 dBi and the chip channel gain is 27 dB. A wide 2-D spatial angle is covered simultaneously by the proposed hybrid beamforming scheme.

Hybrid beamforming with relay and dual-base stations blockage mitigation in millimetre-wave 5G communication applied in (VIOT)

Wireless millimetre-wave communication has been adopted in fifth-generation telecommunication due to its larger bandwidth, low interference, and higher resolutions. Despite these advantages, millimetre-wave communication faces many limitations due to path loss and blockages in the environment. In this paper, a spectral-efficient hybrid beamforming approach was proposed to increase network connectivity and performance, which involved several processes. A relay node was selected as a forwarder to connect a blocked user to a base station using the multi-objective Squirrel search optimization algorithm. Furthermore, the optimal base station was selected based on significant parameters for effective connectivity using the dynamic Shannon entropy-based Fuzzy VIKOR algorithm. Drone base stations were also deployed to improve the network coverage and quality of service. Furthermore, the connectivity of a drone base station was influenced by its positioning and altitude, which was controlled using the trust-region policy optimization algorithm. The selected base station executed hybrid beamforming based on an accurate channel state information prediction once the user equipment was directly or indirectly (i.e. via relay node or drone base station) connected to it. In particular, the bi-directional gated recurrent unit model was used to accurately predict the channel state information. MATLAB (R2017b) was used to simulate the proposed hybrid beamforming model and evaluate its performance metrics in terms of throughput, spectral efficiency, bit error rate, number of server users, and achievable rate. The robustness of our approach is validated under two scenarios with perfect and imperfect CSI. The obtained results were compared with the results of two previous studies, which showed that the proposed model achieved a significant performance improvement.

User Association and Hybrid Beamforming Designs for Cooperative mmWave MIMO Systems

Hybrid analog and digital beamforming has emerged as a key enabling technology for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems since it can balance the trade-off between system performance and hardware efficiency. Owing to the strong ability of central control, cooperative networks show great potential to enhance the spectral efficiency of mmWave communications. In this paper, we consider cooperative mmWave MIMO systems and propose user association and hybrid beamforming design algorithms for three typical hybrid beamforming architectures. The central processing unit (CPU) of the cooperative networks first matches the service pairs of base stations (BSs) and users. Then, an iterative hybrid beamforming design algorithm is proposed to maximize the weighted achievable sum-rate performance of the mmWave MIMO system with fully connected hybrid beamforming architecture. Moreover, a heuristic analog beamforming design algorithm is introduced for the fixed subarray hybrid beamforming architecture. In an effort to further exploit multiple-antenna diversities, we also consider the dynamic subarray architecture and propose a novel antenna design algorithm for the analog beamforming design. Simulation results illustrate that the proposed hybrid beamforming algorithms achieve a significant performance improvement than other existing approaches and the dynamic subarray architecture has great advantages of improving the energy efficiency (EE) performance.

Hybrid Beamforming for Massive MIMO Using Rectangular Antenna Array Model in 5G Wireless Networks

Fifth and future generation (5G and B5G) wireless networks aim to serve users with higher data rates and lower latency. Data traffic due to the rapid growth in communication has motivated the study of Multiple Input Multiple Output (MIMO) systems. They utilize multiple antennas in both transmitter and receiver sides. It is necessary to improve the existing technology to achieve fast and reliable communication. In this research work, a rectangular array antenna based hybrid beamforming in a massive MIMO model has been proposed to improve the spectral efficiency of the system. Thus channel capacity with small RF chains is used. To achieve the high signal strength in the main lobe, Chebyshev tapering has been used to suppress the side lobes signals. In this manner, the proposed Hybrid Beamforming for Massive Output MIMO has been realized with a small complexity and higher spectral efficiency. In this research work, the spectral efficiency of both proposed Hybrid and fully-digital beamforming with a different number of RF chains for a various number of antennas at the transmitter, the receiver side has been analyzed. From the simulation results, it has been observed that the proposed rectangular array antenna based Hybrid beamforming in a massive MIMO system reduces the computational complexity up to 99% as compared with conventional fully digital beamforming to achieve the same spectral efficiencies, which is a productive model for 5G wireless networks.

Hybrid Beamforming for Massive MIMO Over-the-Air Computation

Over-the-air computation (AirComp) has been recognized as a promising technique in Internet-of-Things (IoT) networks for fast data aggregation from a large number of wireless devices. However, the computation accuracy of AirComp highly depends on the devices with the worst channels condition, which degrades severely when the number of devices becomes large. To address this issue, we exploit the massive multiple-input multiple-output (MIMO) with hybrid beamforming, in order to enhance the computational accuracy of AirComp in a cost-effective manner. In particular, we consider the scenario with a large number of multi-antenna devices simultaneously sending data to an access point (AP) equipped with massive antennas for functional computation over the air. Under this setup, we jointly optimize the transmit digital beamforming at the wireless devices and the receive hybrid beamforming at the AP, with the objective of minimizing the computational mean-squared error (MSE) subject to the individual transmit power constraints at the wireless devices. To solve the non-convex hybrid beamforming design optimization problem, we propose an alternating-optimization-based approach, in which the transmit digital beamforming and the receive analog and digital beamforming are optimized in an alternating manner. In particular, we propose two computationally efficient algorithms to handle the challenging receive analog beamforming problem, by exploiting the techniques of successive convex approximation (SCA) and coordinate descent (CD), respectively. It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas. Furthermore, numerical results show that the proposed hybrid beamforming design substantially enhances the computation MSE performance as compared to other benchmark schemes, while the SCA-based algorithm performs closely to the performance upper bound achieved by the fully-digital beamforming.

Efficient Hybrid Beamforming Design in mmWave Massive MU-MIMO DF Relay Systems With the Mixed-Structure

In this paper, we consider the decode-and-forward (DF) relay system in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, and propose a hybrid beamforming design method for the mixed structure, which contains the fully-connected and sub-connected structures. To satisfy constant-modulus and block-diagonalization (BD) constraints, the analog beamforming is designed by the idea of sorted successive interference cancellation (SSIC). More specifically, the proposed method first sorts the capacities of different analog sub-channels in descending order, and then designs the analog beamforming serially according to the order of the capacities. To efficiently mitigate the inner-user and inter-user interference, we propose a modified baseband BD technology to reduce the information loss in digital beamforming design, thereby improving the system capacity. In addition, the proposed hybrid beamforming algorithm is designed by considering both uniform linear arrays (ULAs) and uniform planar arrays (UPAs). Simulation results demonstrate that the proposed hybrid beamforming design scheme can obtain good performance in terms of the achievable sum-rate and power efficiency in ULAs and UPAs.

A Reconfigurable Planar Fresnel Lens for Millimeter-Wave 5G Frontends

Besides the prohibitive cost, complexity, and baseband scale, the development of millimeter-wave (mmWave) digital-beamforming fifth-generation (5G) frontends is facing a physical-layer challenge of thermal dissipation. This is caused by the massively integrated power amplifiers (PAs) and the extremely compact volume due to millimeter-scale wavelengths. In this work, we theoretically analyze and experimentally demonstrate a reconfigurable planar Fresnel lens, based on which a new-concept mmWave beamforming frontend can be implemented with significantly reduced number of PAs. It overcomes the inherent shortcomings of analog beamforming solutions relying on massive use of power dividers and phase shifters and can replace the convex lens used in the recently proposed hybrid beamforming solution. The implemented Ka-band lens is simple-structured, cost-effective, and easy to control, providing multiple modes for beamspace massive-multiple-input multiple-output (MIMO) communications. Experimental results verified its potential in different mmWave 5G systems such as base stations and point-to-point links.

Advanced Hybrid Beamforming Technique in MU-MIMO Systems

This paper proposes a high performance wireless commmunication technology in MU-MIMO systems. The millimeter wave (mmWave) communication technology was considered for the future wireless communication systems such as the fifth-generation new radio (5G NR). In 5G NR, the mmWave communication technology was studied to increase the use of wide bandwidth and the data rate. Therefore, MU-MIMO systems can be used in mmWave. To decrease the complexity of conventional digital beamforming system, the hybrid beamforming system was studied. In particular, the proposed hybrid beamforming system improves the error performance and average sum rate in partially connected structure (PCS) hybrid beamforming system. The proposed PCS hybrid beamforming system forms variously combined beam patterns using the information of azimuth and elevation angles for the multi-paths according to the number of bits. In addition, the azimuth and elevation angles among the formed beam patterns are estimated according to the received signal strength (RSS). In the simulation results, the proposed PCS hybrid beamforming system has better error performance and the average sum rate than the conventional hybrid beamforming system.

Capacity Improvement for DVB-NGH with Dual-Polarized MIMO Spatial Multiplexing and Hybrid Beamforming

Multiple-input multiple-output (MIMO) antenna scheme is an effective technique for future terrestrial broadcasting systems such as digital video broadcasting-next-generation handheld (DVB-NGH) to overcome the limits on information theory of traditional single-antenna wireless communications without any additional bandwidth or increased transmit power. In this paper, we propose a hybrid beamforming scheme for dual-polarized MIMO spatial multiplexing DVB-NGH broadcasting systems. The system of interest makes use of phase shifters and amplitude attenuators for the digital-analog precoder at beamforming stage of the transmitter end to maximize the signal-to-noise ratio to increase the channel capacity of the DVB-NGH systems. At the receiver end, the maximum likelihood (ML) detector is used to evaluate the system performance. We consider the signal-to-interference-and-noise ratio (SINR) and the achievable average capacity for the DVB-NGH MIMO with various FFT sizes, number of transmit antennas, and different modulation schemes. The performance results on bit error rate, channel capacity, and beampatterns show that the proposed hybrid beamforming and dual-polarized MIMO spatial multiplexing schemes provide more robustness against signal interference by beamforming and/or nulling techniques. The simulation results also show that the proposed system achieves higher capacity than the existing MIMO schemes for the DVB-NGH systems.

Hybrid Beamforming in MU-MIMO Using Partial Interfering Beam Feedback

We propose a hybrid beamforming scheme with partial interfering beam feedback for a codebook-based multi-user multiple-input multiple-output (MU-MIMO) system, where users feed back information only about the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> transmit beams. For the analog part of the precoding, we consider two codebooks, the conventional Discrete Fourier Transform (DFT) codebook with uniform amplitude beamforming vectors and the Taylor codebook with non-uniform amplitude beamforming vectors. For the digital precoding part, the effective channel matrix is approximated and used for zero-forcing (ZF). We also propose a beam pairing algorithm that results in reduced inter-beam interference and simplifies beam and user selection in MU-MIMO. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is equal to the number of beams in the transmit codebook, the proposed scheme includes an existing scheme with full effective channel matrix feedback as a special case. Numerical results show that the proposed hybrid beamforming performs better than an existing hybrid precoding scheme based on channel reconstruction.

Dynamic Hybrid Beamforming With Low-Resolution PSs for Wideband mmWave MIMO-OFDM Systems

Analog/digital hybrid beamforming is considered as a key enabling multiple antenna technology for implementing millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications since it can reduce the number of costly and power-hungry radio frequency (RF) chains while still providing for spatial multiplexing. In this paper, we introduce a novel hybrid beamforming architecture with dynamic antenna subarrays and hardware-efficient low-resolution phase shifters (PSs) for a wideband mmWave MIMO orthogonal frequency division multiplexing (MIMO-OFDM) system. By dynamically connecting each RF chain to a non-overlapping antenna subarray via a switch network and PSs, multiple-antenna diversity can be exploited to mitigate the performance loss due to the employment of practical low-resolution PSs. For this dynamic hybrid beamforming architecture, we jointly design the hybrid precoder and combiner to maximize the average spectral efficiency of the mmWave MIMO-OFDM system. In particular, the spectral efficiency maximization problem is first converted to a mean square error (MSE) minimization problem. Then, an efficient iterative hybrid beamformer algorithm is developed based on classical block coordination descent (BCD) methods. An analysis of the convergence and complexity of the proposed algorithm is also provided. Extensive simulation results demonstrate the superiority of the proposed hybrid beamforming algorithm with dynamic subarrays and low-resolution PSs.

Hybrid beamforming design based on unsupervised machine learning for millimeter wave systems

SummaryThis article proposes a hybrid beamforming design with reduced channel state information (CSI) feedback. We use a beam sweeping procedure to provide channel measurements and feed a CSI report scheme. Thereby, the base station (BS) can perform an adequate estimation of the channel characteristics with reduced signaling overhead. Consequently, we need short pilot sequences and very few precoding matrix indicators (PMIs) to properly describe channel behavior. Moreover, we also evaluate different user selection strategies based on unsupervised machine learning framework that exploits the channel information provided by the proposed beam sweeping scheme. Our performance evaluation indicates that the user selection based on fuzzy c‐means is able to efficiently explore the reduced CSI. The proposed hybrid beamforming scheme reduces the multi‐user interference and achieves significant gains in total data rate as channel conditions and interference environment becomes more challenging.

Hybrid Beamforming With Dynamic Subarrays and Low-Resolution PSs for mmWave MU-MISO Systems

Analog/digital hybrid beamforming architectures with large-scale antenna arrays have been widely considered in millimeter wave (mmWave) communication systems because they can address the tradeoff between performance and hardware efficiency compared with traditional fully-digital beamforming. Most of the prior work on hybrid beamforming focused on fully-connected architecture or partially-connected scheme with fixed-subarrays, in which the analog beamformers are usually realized by infinite-resolution phase shifters (PSs). In this paper, we introduce a novel hybrid beamforming architecture with dynamic subarrays and hardware-efficient low-resolution PSs for mmWave multiuser multiple-input single-output (MU-MISO) systems. By dynamically connecting each RF chain to a non-overlap subarray via a switch network and PSs, we can exploit multiple-antenna and multiuser diversities to mitigate the performance loss due to the use of practical low-resolution PSs. An iterative hybrid beamformer design algorithm is first proposed based on fractional programming (FP), aiming at maximizing the sum-rate performance of the MU-MISO system. In an effort to reduce the complexity, we also present a simple heuristic hybrid beamformer design algorithm for the dynamic subarray scheme. Extensive simulation results demonstrate the advantages of the proposed hybrid beamforming architecture with dynamic subarrays and low-resolution PSs compared to existing fixed-subarray schemes.

Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks

Wireless networks employing millimeter-wave (mmWave) and Massive Multiple-Input Multiple-Output (MIMO) technologies are a key approach to boost network capacity, coverage, and quality of service (QoS) for future communications. They deploy symmetric antennas on a large scale in order to enhance the system throughput and data rate. However, increasing the number of antennas and Radio Frequency (RF) chains results in high computational complexity and more energy requirements. Therefore, to solve these problems, this paper proposes a low-complexity hybrid beamforming scheme for mmWave Massive-MIMO 5G wireless networks. The proposed algorithm is on the basis of alternating the minimum mean square error (Alt-MMSE) hybrid beamforming technique in which the orthogonal properties of the digital matrix were designed, and then the MSE of the transmitted and received signal was reduced. The phase of the analog matrix was obtained from the updated digital matrix. Simulation results showed that the proposed hybrid beamforming algorithm had better performance than existing state-of-the-art algorithms, and similar performance with the optimal digital precoding algorithm.