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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.

Hybrid beamforming is a viable method for lowering the complexity and expense of massive multiple-input multiple-output systems while achieving high data rates on track with digital beamforming. To this end, the purpose of the research reported in this paper is to assess the effectiveness of the three architectural beamforming techniques (Analog, Digital, and Hybrid beamforming) in massive multiple-input multiple-output systems, especially hybrid beamforming. In hybrid beamforming, the antennas are connected to a single radio frequency chain, unlike digital beamforming, where each antenna has a separate radio frequency chain. The beam formation toward a particular angle depends on the channel state information. Further, massive multiple-input multiple-output is discussed in detail along with the performance parameters like bit error rate, signal-to-noise ratio, achievable sum rate, power consumption in massive multiple-input multiple-output, and energy efficiency. Finally, a comparison has been established between the three beamforming techniques.

The trend for dense deployment in future 5G mobile communication networks makes current wired backhaul infeasible owing to the high cost. Millimetre-wave (mm-wave) communication, a promising technique with the capability of providing a multi-gigabit transmission rate, offers a flexible and cost-effective candidate for 5G backhauling. By exploiting highly directional antennas, it becomes practical to cope with explosive traffic demands and to deal with interference problems. Several advancements in physical layer technology, such as hybrid beamforming and full duplexing, bring new challenges and opportunities for mm-wave backhaul. This article introduces a design framework for 5G mm-wave backhaul, including routing, spatial reuse scheduling and physical layer techniques. The associated optimization model, open problems and potential solutions are discussed to fully exploit the throughput gain of the backhaul network. Extensive simulations are conducted to verify the potential benefits of the proposed method for the 5G mm-wave backhaul design.

Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate the inherent inter-cell interference (ICI) of cellular networks. In such networks, several distributed access points (APs) together with precoding/detection processing can serve many users while sharing the same time-frequency resources. Each AP can be equipped with single or multiple antennas, and hence, can provide a consistently adequate service to all users regardless of their locations in the network. This paper presents a detailed overview of the current state-of-the-art on CF systems. First, it performs a literature review of the conventional CF and scalable user-centric (UC) CF mMIMO systems in terms of the limited capacity of the fronthaul links and the connection between APs and user equipments (UEs). As beyond networks will rely on higher frequency bands, it is of paramount importance to discuss the impact of beamforming techniques that are being investigated. Finally, some of the CF promising enabling technologies are presented to emphasize the main applications in these networks.

Millimeter-wave (mmWave) and massive multi-input–multi-output (mMIMO) communications are the most key enabling technologies for next generation wireless networks to have large available spectrum and throughput. mMIMO is a promising technique for increasing the spectral efficiency of wireless networks, by deploying large antenna arrays at the base station (BS) and perform coherent transceiver processing. Implementation of mMIMO systems at mmWave frequencies resolve the issue of high path-loss by providing higher antenna gains. The motivation for this research work is that mmWave and mMIMO operations will be much more popular in 5G NR, considering the wide deployment of mMIMO in major frequency bands as per 3rd generation partnership project. In this paper, a downlink multi-user mMIMO (MU-mMIMO) hybrid beamforming communication system is designed with multiple independent data streams per user and accurate channel state information. It emphasizes the hybrid precoding at transmitter and combining at receiver of a mmWave MU-mMIMO hybrid beamforming system. Results of this research work give the tradeoff between multiple data streams per user and required number of BS antennas. It strongly recommends for higher number of parallel data streams per user in a mmWave MU-mMIMO systems to achieve higher order throughputs.

In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multiple-output (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.

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications.

Energy efficiency (EE) is a key enabler for sustainable green millimeter wave (mmWave) communication. However, conventional hybrid beamforming methods suffer from energy efficiency performance loss due to hardware limitations and the connected structure. To overcome these limitations, this work investigates a novel adaptive connected (AC) hybrid beamforming (HBF) design for multi-user mmWave systems. We formulate the problem as an EE maximization problem subject to the constraints of the adaptive connected structure, constant modulus, and power budget. Addressing this complicated non-convex problem, we harness the characteristics of the AC structure and introduce an iterative HBF design algorithm grounded in fractional programming. Numerical results demonstrate the effectiveness and flexibility of the proposed AC-based HBF design in terms of EE enhancement.

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 meta- heuristic 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.

Hybrid (analog-digital) beamforming has received tremendous attention for realizing multiuser multiple-input-multiple-output systems at millimeter wave frequencies. However, almost all of the hybrid beamforming literature characterizes the spectral and energy efficiency performance for randomly located user terminals. In stark contrast, this article evaluates and compares the multiuser downlink ergodic sum spectral efficiency (ESSE) using different combinations of analog and digital beamforming techniques when users are closely-spaced. For any given combination of hybrid beamforming, we derive an analytical expression characterizing the loss of per-user ergodic spectral efficiency relative to fully digital beamforming. To get the negligible ESSE loss for both hybrid and fully digital beamforming, we quantify the angle-of-departure separation across multiple users, it is the term associated to the channel correlation induced by the user positions. We show the generality of the derived expression by testing it across a wide range of system dimensions, number of radio-frequency (RF) chains, and signal-to-noise ratios. Our results demonstrate that an extra RF chain may be sufficient to compensate most of the loss in ESSE due to closely-spaced terminals.