1. Introduction
The relentless pursuit of innovation in wireless communication technologies has paved the way for the evolution of telecommunication networks from the early days of 2G to the current standards of 5G [1]. As the demand for higher data rates, lower latency, and increased connectivity continues to surge, researchers and industry experts are already setting their sights on the next frontier: Beyond 5G (B5G) and Sixth-Generation (6G) networks [2]. Central to the realization of these ambitious objectives is the revolutionary concept of massive Multiple-Input Multiple-Output (M-MIMO) systems.
The innovative M-MIMO is a paramount wireless access technology that can significantly manage the ever-increasing global data demand. While small, many smart antennas equipped at the Tx and Rx sides can maintain spectrum and energy consumption efficiencies with minimum processing complications. M-MIMO can play a revolutionary role in B5G/6G heterogenous networks through seamless integration with various technologies such as the Internet of Flying Things (IoFTs), Internet of medical things (IoMTs), smart grids, smart gadgets, smart homes, mobile devices, industry, satellites, and Vehicle-to-Everything (V2X) [3,4], as depicted in Figure 1.
In current NR 5G networks, M-MIMO is one of the essential technologies that helps in smooth precoding procedures, and energy and power management. It enables high-frequency communication by exploiting different beamforming (BF) techniques, which assist in achieving superior diversity array gain and providing a proficient user experience [5]. In contrast, for B5G/6G cellular networks, various other robust transceiver solutions and handling strategies have been introduced, such as reconfigurable intelligent surfaces (RISs), cell-free (CF) M-MIMO, sourced random access, etc. However, when utilizing the full potential of an upper-frequency spectrum with active antenna elements, issues persist in hardware complications, energy consumption, the number of RF chains, and the algorithm designs, thus demanding further simplification and performance improvement [6]. At its core, M-MIMO represents a paradigm shift in wireless communication, offering unprecedented gains in spectral efficiency, energy efficiency, and reliability [7]. The M-MIMO system, mmWave spectrum, and BF mechanisms are already employed in various 5G practical arenas. These technologies mature and prosperously respond to different signal processing, channel propagation, interference, and data transmission responsibilities [8]. However, some persistent challenges in established technologies, ever-growing data requisite, and various upcoming technological integrations are threatening challenges in future mobile communication.
However, realizing the full potential of M-MIMO in the context of B5G and 6G networks necessitates a nuanced understanding of its associated concepts and challenges. Therefore, in this review paper, we broadly discuss M-MIMO and the different valuable features of the upper spectrum and intelligent learning frameworks for the efficient operation of B5G/6G networks. In the context of established technologies, we are only focusing on the M-MIMO, mmWave, and BF techniques, and various other accessing established strategies are beyond our review paper scope. This article aspires to deepen the understanding of M-MIMO technology and catalyze future research directions in the dynamic realm of B5G and 6G networks. By highlighting the challenges and emerging trends in M-MIMO design, this article aims to contribute to advancing knowledge and fostering innovation in telecommunications. This paper will help prospective researchers understand and utilize the contemporaneous adventure of multiple technologies for real-life communication purposes.
1.1. Related Literature
Several recent survey papers have addressed the utilization of MIMO configurations in mobile communications. The study conducted by [9] primarily focused on analyzing and presenting detection algorithms for M-MIMO systems, incorporating ML methodologies. Similarly, the study in [10] extensively examined M-MIMO detectors employing deep learning (DL) techniques. The study in [11] offers a survey on the challenges and advantages of mmWave massive MIMO systems. It addresses enhancements in user throughput, spectral efficiency, and energy efficiency. Various factors impacting system performance are discussed, including modulation schemes, signal waveforms, multiple-access techniques, user scheduling algorithms, fronthaul design, antenna array architectures, and precoding algorithms. In [12], the authors categorized different DL approaches based on their application in 5G domains such as channel coding, M-MIMO, and resource allocation. The study in [13] addressed various aspects of radio resource management (RRM) procedures utilizing ML algorithms, presenting a similar categorization as seen in [10]. The study in [14] presents an exhaustive examination of linear precoding techniques for massive MIMO systems in a single-cell scenario. It evaluates the performance of various linear precoding methods in terms of sum rate and spectral efficiency. While the related literature provides valuable insights into individual components or methodologies within M-MIMO systems, the existing literature lacks a holistic view of M-MIMO design and does not cover the integration aspects of MIMO into future network paradigms in B5G/6G. Similarly, the related literature partially covers the emerging B5G/6G paradigm for prevailing challenges and potential research directions in M-MIMO. Table 1 provides a summary of the recent related survey in the subject domain.
1.2. Motivation and Contribution
This comprehensive article embarks on the intricacies of M-MIMO design and its pivotal role in shaping the future landscape of telecommunications. With a particular focus on the seamless integration requirements for B5G and 6G networks, this article aims to guide researchers and practitioners to embrace the challenges and opportunities presented by this transformative technology. This article navigates these complexities and critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations. Furthermore, this article explores cutting-edge developments and emerging trends that promise to redefine the boundaries of wireless communication. From the integration of artificial intelligence (AI) for three-dimensional beamforming to the exploitation of reconfigurable intelligent surfaces (RISs) and the utilization of the tera hertz (THz) spectrum, this article offers invaluable insights into the future trajectory of M-MIMO technology. Noteworthy topics covered in the article include AI-enabled M-MIMO three-dimensional beamforming and THz spectrum utilization. Moreover, this article delves into key open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems, dissected with precision, offering a roadmap for future research endeavors. The overall organization of this review is shown in Figure 2.
2. Massive MIMO
Multi-antenna technology, i.e., MIMO, is an over-the-air accessing technology that can simultaneously transmit (Tx) and receive (Rx) two or more data packets over the same radio frequency band. The traditional MIMO and M-MIMO antenna selection, precoding, decoding, and processing schemes satisfactorily manage 5G applications and workplaces, either indoor or outdoor scenes [15]. Moving forward, focusing on 6G mobile service platforms, traditional antenna technologies have been considered incompetent not only because they fail to match the ever-increasing data requirements but also because they noticeably demand enhancement in precoding/decoding methods, signal assessment processes, and computational time efficiencies to unlatch many unprecedented delay-sensitive cases [16]. Since 6G will be an extension of the 5G network’s protocols and architecture, the interplay of the predecessor’s technologies and new schemes would enrich the wireless ecosystem. This section is summarized for a consolidated overview in Table 2.
2.1. Multiplexing Gain and Index Modulation
With the swift progress in mobile communication and unconventional paradigms, various novel techniques have recently been developed along with the enhancement of antenna data sensing strategies [17]. For instance, sparse code multiple access (SCMA), non-orthogonal multiple access (NOMA) in the power domain, and orthogonal multiple access are the most promising and scalable solutions for successful mobile connection establishment for B5G/6G networks [18]. This is particularly because conventional multiplexing technologies were conceptualized and purposefully designed for human-centric mobile network architecture. In contrast, after realizing the intelligent node’s growth in each small cell site and current network dynamics where almost everything will support wireless connectivity, baseline multiplexing technologies are not sufficient for machine/user-centric mobile carriers and would incur problems in (i) signal detection algorithmic complexities at the Rx node and (ii) sophisticated constellation matrix design. In contrast to the standard approaches in developing new and concrete radio communication accessing schemes, many researchers focus on the uncustomary recourse wherein handling of RF propagating characteristics is delved into. For instance, many recent studies have focused on controlling the environmental factors of reflection, refraction, meta-surfaces, facades, hoardings, and scattering of propagating RF signals to enhance the quality-of-service (QoS) and achievable throughput rate. In this context, emerging index modulation schemes, [19,20,21] including media-based modulation, beam index modulation, quadrature index modulation, and spatial scattering modulation, principally use variants in the signature of received signals by exploiting reconfigurable antennas and Tx additional information bits in rich scattering scenarios and eventually boost the signal quality at the point of interest [22]. Thus, modern schemes offer careful governance and allocation of spectrum resources and enhance power utilization efficiency by implying a large array of antenna elements with minimal intensity in power transmission [23]. Also, it provides flexibility, minimizes interference chances, and ensures ubiquitous seamless coverage to the user all the time [24,25]. Though current M-MIMO antennas are performing extraordinarily in a real practical environment for different scenarios, a few persistent issues that are discussed in this section still demand sharp and concrete approaches to optimize the operation of an M-MIMO processing system further [26].
2.2. RF Chain Reduction
The RF electronic chain circuits are a key element in digital antenna Rx designs; they allow the passband communication data signals to be manipulated in the baseband and achieve greater BF gain, spatial multiplexing gain, and SINR levels. The process becomes more beneficial in M-MIMO mode, wherein the number of RF chains increases typically from 16 to 64 and successfully enables high bandwidth (BW) applications [27]. The benefits of M-MIMO are directly concerned with the number of RF chains, and unfortunately, larger RF chains lead to greater cost, computational time, and complexity, and enormous energy consumption issues, particularly with higher frequency carriers. In contrast to traditional phase shifters using a phase gradient over the antenna array aperture to produce RF beams, phase shifting has been employed in the transmitted waves in the near field of the active antenna arrays without the assistance of phase shifters [28]. The authors have fabricated a parasitic layer consisting of metal strips with enhanced geometry, dimension, and position in the near field location of the antenna array, and a prototype evaluation conclusively achieved superior beam performance with the low number of RF chains.
2.3. Unsourced Random Access
In sporadic traffic scenarios where the arrival of symbols is unpredictable, generally, a limited number of users/machines are active to transmit small data payloads, commonly carried in the order of 100 bits, in a particular system. It is envisaged that future sporadic traffic cases will proliferate, and extreme throughput requirements will be striven for. In line with this, more efficient multiple-access techniques are needed to handle the network’s resources and accommodate the burgeoning count of smart nodes. Previously, grant-based random access techniques have been extensively employed for IoT systems, and the end nodes were found to be greatly affected by the antenna’s high power dissipation along with the latency problem [29,30]. Focusing on the 6G networks and delay-critical applications, a grant-free random-access scheme has been proposed, wherein the devices/machines arbitrarily select unique training pilots for channel state information (CSI) and activity detection before transmitting data. Also, it does not perform mandatory handshaking protocols with the base station (BS), which is the reason for the high latency in the grant-based system. However, overall performance and channel connectivity support are limited by a certain number of users due to the confined pilot sequence resources. A new grant-free unsourced random access has been introduced as a probable alternative solution to subdue the impact of limited pilot resources [31,32]. Herein, machines/users compulsively exploit the same codebook, and the BS only requires obtaining a list of transmitted data without legally binding them to the particular active users. Deliberating the unsourced random access using existing approaches, such as ALOHA and CDMA, the authors [31] claimed that the results were unsatisfactory, hence different revised coding techniques [33], for example, modified coupled coding, covariance-based maximum likelihood estimation, and approximate message passing have been proposed under the assumption of a block-fading M-MIMO channel [34,35]. Still, the works mentioned above completely rely on independent and identically distributed (I.I.D) M-MIMO channels, which is indeed impractical in many practical outdoor environments because the antennas on the device and the BS locations are subjected to strong correlations.
2.4. Pilot Contamination
To ensure wireless data transmission efficiency and minimize the pilot signaling overhead, pilot training sequences, either orthogonal or non-orthogonal, are reused in the neighboring cells in a mobile network architecture. The sole reason is that pilot training sequences are restricted by the channel coherence time interval, and with the increase in the number of smart nodes, more pilot sequences need to be reassigned [36]. Thus, the reuse of pilot sequences in the neighboring cells becomes the reason for frequent interference issues. However, with the large-scale availability of antennas at the BS commendably eliminating the fast fading and additive white Gaussian noise effects, the resulting pilot training issues endure and constitute a major concern in M-MIMO transmission. Since the advent of the M-MIMO system, several studies have been performed on the mitigation of pilot training errors, and after much deliberation, the authors in [37] have classified the existing studies into four categories: (i) precoding, (ii) pilot assignment, (iii) pilot design, and (iv) channel estimation. The authors have proposed a joint method of pilot assignment and pilot design-based CSI scheme to minimize the interference effect and showed that the pilot assignment has outperformed the random pilot and exhaustive search algorithms at the cost of computing time and complexities.
2.5. Cell-Free M-MIMO
Concentrating on B5G requirements, an intense approach supporting ultra-reliability with multi-connectivity besides low power consumption is much needed. A cell-free (CF) M-MIMO is a system that comprises several access points (APs) and coherently provides services to a much smaller group of users on the same time/frequency resources [38]. The new physically distributed antenna model keeps the same QoE for all users with less signal processing computation [39]. The eminent contribution of CF M-MIMO is its advocacy in channel propagation and channel hardening. The APs are arbitrarily distributed all over the cell coverage area, and smart products can connect simultaneously to multiple closely related AP antenna nodes. Hence, to conduct a fast-paced packet data transmission process and manage scarce spectrum resources, the network needs to operate in time division duplexing (TDD) mode and exploit uplink (UL)–downlink (DL) channel reciprocity [40]. By meeting all the essential aspects, it can satisfy high reliability and a very low packet drop rate due to the multi-connectivity feature, and it consumes a very low amount of power. A few other promising aspects of CF M-MIMO that make it beneficial over a centralized antenna design are (i) the short-range link between AP and users, (ii) its coverage of a maximum area in space and support of ubiquitous connectivity, (iii) its increased intelligent device capacity and provision of flexibility in AP deployment, and (iv) its capitalization of energy efficiency (EE) and spectral efficiency (SE). These prodigious traits of CF M-MIMO are certainly practical and prudent for cellular, IoT, and D2D services in B5G networks [41]. Moreover, CF M-MIMO shows sustenance for higher-frequency spectrums, maintains timely data symbol delivery, minimizes path losses, and achieves a higher SINR level. Additionally, the CF multi-antenna process cultivates macro-diversity gain by diminishing scattering, shadowing, and fading effects [42]. CF M-MIMO has potential for practical cases and is certainly appropriate for current and forthcoming deployments in hot-spot spaces and indoor coverage areas, for example, shopping malls, stadiums, subways, smart train stations, community service centers, etc. [43].
2.6. Hybrid Precoding
Hybrid precoding is an attractive tool for extremely-high-frequency M-MIMO communication systems as it can significantly reduce the number of RF chains without affecting the total sum rate. In the current literature, most hybrid precoding utilizes either high-resolution phase shifters (PSs) or impractical narrow-band mmWave RF channel models. Concentrating on the hybrid precoding attributes, a CEO-based hybrid precoding method has been applied with one-bit PSs for the frequency-selective wideband mmWave M-MIMO system [44]. The simulation test validated that the outlined framework achieved an acceptable sum-rate value and relatively higher EE compared to some available techniques. Similarly, while discussing the limitations of conventional hybrid precoding strategies, the authors in [45] proposed the energy-reliable switch and inverter (SI)-based hybrid precoding and adaptive cross-entropy (ACE)-based hybrid precoding designs. Experimental results showed that constructed algorithms can achieve a satisfactory sum-rate value and much better EE than traditional methods on a given model. A new joint beam selection process for analog precoding under a discrete lens array scheme was presented in [45]. The authors have confirmed the excellence of the devised scheme by improving the system sum rate, minimizing inter-user interference, and reducing computational complications.
Table 2M-MIMO system components.
| Area | Ref. | Technologies | Impact | Limitations |
|---|---|---|---|---|
| Multiplexing Gain and Index Modulation | [19,20,21] | Emerging index modulation | Variants in the received signal signature for improved quality | Requires reconfigurable antennas and Tx additional information bits |
| [22] | Media-based modulation | Boosts signal quality at the point of interest | Intensity in the transmission of power needs to be minimal | |
| [23] | The large array of antenna elements | Enhances power utilization efficiency | Persistent optimization issues in M-MIMO processing system | |
| [26] | M-MIMO antennas | Performance optimization in various scenarios | Sharp and concrete approaches needed for persistent optimization issues | |
| RF Chain Reduction | [27] | Phase shifting in the near field | Increase in BF gain, spatial multiplexing gain, SINR level | Cost, computational time, complexity, energy consumption |
| [28] | Parasitic layer with enhanced metal strips | Achieved superior beam performance with a low number of RF chains | Integration challenges in B5G due to unknown compatibility and scalability issues | |
| Unsourced Random Access | [29] | Grant-based random access | Proposes a grant-free random-access scheme for delay-critical applications | Limited channel connectivity support due to confined pilot sequence resources |
| [31,32] | ALOHA, CDMA | Introduces a new grant-free unsourced random-access scheme | Unsatisfactory results with existing approaches such as ALOHA and CDMA | |
| [33] | Modified coupled coding, | Revised coding techniques for unsourced random access under block-fading M-MIMO channels | Relies on the impractical assumption of I.I.D M-MIMO channels, which is not suitable for many outdoor environments | |
| Pilot Contamination | [36] | Pilot training sequences | Reuse of pilot sequences in neighboring cells leads to interference issues | Potential interference issues due to frequent reuse of pilot sequences |
| [37] | Pilot assignment, channel estimation | Classification of existing studies into four categories for pilot training error mitigation | Increased computing time and complexities in implementing the joint pilot assignment | |
| Cell-Free M-MIMO | [39] | Cell-free M-MIMO | Advocacy in channel propagation and channel hardening | Requires significant infrastructure deployment |
| [41] | Cellular, IoT, D2D services in B5G networks | Provides ubiquitous connectivity and flexibility in AP deployment | Potential scalability issues with an increasing number of devices | |
| [42] | Higher-frequency spectrums | Sustains higher-frequency spectrums | May face challenges in compatibility with legacy systems | |
| Hybrid Precoding | [44] | CEO-based hybrid precoding | Achieved acceptable sum-rate value and relatively higher EE | Dependence on one-bit PSs may limit performance in complex environments |
| [45] | Energy reliable SI | Proposed algorithms achieved satisfactory sum-rate value | Energy-efficient designs may require complex hardware implementations |
3. M-MIMO in B5G/6G Technologies
As the demand for high-speed and reliable wireless communication continues to soar, the emergence of B5G and 6G technologies promises groundbreaking advancements. Among these, M-MIMO stands out as a pivotal technology reshaping the wireless landscape. Leveraging AI/ML approaches, M-MIMO optimizes network performance by dynamically adapting to changing conditions and user demands and integrating reconfigurable intelligent surface (RIS)-based M-MIMO systems by manipulating electromagnetic waves, thus improving coverage and spectral efficiency, as depicted in Figure 3. Furthermore, innovations such as visible light communication, hybrid beamforming, and three-dimensional beamforming complement M-MIMO, offering enhanced connectivity and throughput in diverse environments. The exploration of the tera hertz spectrum opens up new frontiers for M-MIMO and wireless backhaul, enabling ultra-high-speed data transmission and unlocking the potential for futuristic applications. The subsequent subsections thoroughly analyze emerging concepts for M-MIMO integration in B5G/6G technologies.
3.1. M-MIMO with AI/ML Approaches
Current cellular carrier networks pose various intimidating challenges that need immediate attention. For example, it is hard to estimate the channel characteristics of colossal data due to the frequent and large counts of SER. Secondly, a substantial volume of data is generated and sensed by BSs every day to accommodate different users in multi-purpose scenarios. It is a challenging task to examine and characterize useful information accurately [46]. Another important factor is learning the beam combination under the operation of FD, a fast-paced and enormous data transmission mode [46]. Thus, to manage such constraints in wireless networks, artificial intelligence (AI) and machine learning (ML) algorithms become effective treatments to mitigate contemporary issues and enhance overall network performance [47]. The idea behind AI/ML with M-MIMO amalgamation is to design a less complicated algorithm and to improve synchronization. Moreover, an improvement in the acquisition of users for accessing the link restrains regular system faults and avoids problems in receiving system knowledge from radio networks [48]. Lately, preliminary evaluations of the exercise of AI/ML algorithms in B5G cellular networks have been investigated by the standardization authorities to validate the idea, including the International Telecommunication Union (ITU), third-generation partnership project (3GPP), and 5GPPP, as well as other study groups such as FuTURE, and the telecom infra project (TIP). Although the cooperative association of many radio accessing mechanisms with M-MIMO antenna elements is expected to dispense the unprecedented requirement of wireless data services, the fundamental concern in the concurrent operation of major data propagation technologies is high operational complexities and computation times. It vigorously affects delivering valuable resources to the destination and greatly damages the mobile network’s performance and QoE, which is totally unacceptable.
Recent works on the successful operation of wireless communication with AI/ML M-MIMO with other facilitating technologies have been presented in this section. Advanced ML and situational awareness tools have been integral to wireless systems in addressing various issues in the physical layer. Following this theme, the application of ML for mmWave beam alignment has been exhaustively examined to solve complicated non-linear problems and gain potential advantages. In one article [49], a beam alignment process with partial beams using an ML (AMPBML) scheme was investigated for the MU-mmWave M-MIMO network. The proposed method minimized training slots, aligned beams for multiple users concurrently, and successfully conducted MU mmWave M-MIMO communication. The authors discussed the mmWave beam prediction issue in a highly mobile vehicular environment [50]. A novel ML tool and situational awareness availability were proposed to learn the beam information. Consequently, situational awareness helped improve the prediction accuracy, and the model managed to achieve good throughput at the cost of little loss of performance.
Many studies have confirmed that ML and deep learning approaches are prolific in estimating information’s angle of arrival (AoA). In another article [51], the authors recently gathered AoA information via appropriate mmWave beam selection in the UL direction using learning-based methods. They proposed two learning processes—k-nearest neighbors and support vector classifiers—and one deep learning method—multilayer perception. A unique beamformer set with a bigger and configurable beamwidth was also established. To validate the plausibility of the scheme, a computer simulation revealed that in terms of classification accuracy and sum-rate performance, the proposed solution is relatively close to exhaustive search results.
In yet another article [52], the deep learning compressed sensing (DLCS) technique in MU-mmWave M-MIMO systems was evaluated for robust channel estimation. The results proved that the channel estimation performance of the implemented scheme via a simulation test was better than that of contemporary processes. The most challenging phenomena in the full exploitation of mmWave RF signals in M-MIMO antenna systems are the power consumption and massive hardware cost. It is defined as the total number of RF chains needed for BF and the increased power utilization with the antenna ratio. Therefore, many authors have focused on the applicability of lens antenna arrays and proposed dictionary-trained beam selection matrices [53]. Experimental values verified the essence of the proposed technique by enhancing channel estimation performance. Few authors have tried to extract the benefits of the deep learning method and attempted a low-rank channel recovery scheme for a hybrid BF array-based M-MIMO system to acquire full CSI [54]. Analytical studies revealed that the novel design has the potential to attain the full-rank LS solution. The authors furnished a neural hybrid BF/combining strategy to tackle hybrid precoding constraints and overcome the traditional issues in the mmWave multi-antenna streams [55]. The authors experienced results that showed that the proposed scheme established higher BER compared with other linear matrix decomposition methods. This subsection is summarized in Table 3.
3.2. Reconfigurable Intelligent Surfaces
In order to overcome the intrinsic deficiencies of antenna technologies, conventional relaying approaches, path loss problems, algorithmic difficulties, and computing time, a prospective RIS concept has been prompted, especially for high-precision delay-sensitive arenas [56]. By definition, it is a programmable metasurface comprising passive electronic components with very low power consumption, and each element can positively regulate the incident signal to a specific degree of phase shift via a central controller connected by RIS and approximately achieve an equal threshold of passive BF gains [57]. Each passive component on the metasurface has the potential to fine-tune the signals independently and reflected signals from RIS can positively converge to the desired location, hence achieving very high data success reliability [58]. However, the larger the size and geographical distribution of the RIS array in a particular environment, the greater the data success probability ratio but the more the algorithmic complications would intimidatingly escalate and add a significant latency in delivering the packet, even in short symbol transmission [59]. Therefore, the array’s size is paramount because many future mobile services and robotic assignments will demand stringent delay management with ultra-high reliability, and a diligent evaluation of RIS array size in line with the surrounding environment is crucial [60,61]. Recently, a comparative study has been conducted to demonstrate the elegance of the RIS-aided M-MIMO systems over conventional M-MIMO systems by adopting a genetic framework that completely depends on statistical CSI [62]. Similarly, portraying the essence of RIS in a complex dynamic environment under statistical CSI in M-MIMO systems, the authors analyzed that the interplay of M-MIMO and RIS metasurfaces would certainly enrich the communication environment besides decreasing the implementation complications and signaling overhead cost [63]. In another study [64], the researchers investigated the performance of RIS-aided M-MIMO under imperfect CSI with ZF detectors and identified that a greater system capacity can be achieved with minimal RIS complexity. Still, latency evaluation and energy management have remained open issues that demand further exploration. This subsection is summarized in Table 4.
3.3. Visible Light Communication
More than 70% of the data volume is predicted to be generated from indoor environmental locales [65]. To release the congestion of the current RF spectrum and enable low-cost as well as extremely reliable data-access solutions, visible light communication (VLC), which is also referred to as LiFi in the current radio access network (RAN) architecture, evolved as a viable supplement owing to many its desirable aspects, for instance (1) low hardware and processing costs because VLC uses a standard lighting framework to reap illumination and communication benefits; (2) very large BW resources (of the order of THz); (3) green communication because of the low energy consumption; (4) high SINR (due to the illumination size of lux); and (5) security and interference avoidance, which makes VLC compatible for hospitals, aircraft, healthcare centers, schools, and localities where RF transmission are not befitted. Moreover, to meet the future extreme data rate demand, the throughput rate of VLC systems also heavily relies on the number of transmitting light emitting diode (LED) arrays and receiving photodiodes (PDs) and coined the term M-MIMO VLC system [66]. In spite of that, the performance of the M-MIMO VLC system is severely affected by two factors that need further investigation: (1) amplification noise at the receiver using ZF or MMSE, and (2) non-linear transfer characteristics of LED, resulting in non-linear distortion, and poor symbol rate performance [67]. A novel approach was introduced in the study in [68] to address spatial multiplexing challenges in VLC MIMO systems and enhance spectral efficiency (SE). The approach uses joint IQ independent component analysis (ICA) and leverages ML techniques specifically tailored for a 2 × 2 MIMO system in the VLC domain. The proposed ML methodology allows two optical signals to be effectively split into parallel streams, mitigating spatial cross-talk and inter-symbol interference. Similarly, the study in [69] proposed an artificial neural network (ANN)-based joint spatial and temporal equalization scheme for an MIMO-VLC system. This ANN-based solution surpasses traditional decision feedback equalization (DFE) by effectively addressing non-linear transfer functions and cross-talk within a real optical MIMO communication channel, whether imaging or non-imaging. The data structure feeding the ANN incorporates both predicted signal vectors with feedback delay lines and receives signal vectors with feedforward delay lines, thereby optimizing signal processing for improved system performance. This subsection is summarized in Table 5.
3.4. Hybrid Beamforming
BF was first proposed and designed by Zhang, and Molisch [70], exploiting the combinatorial work of digital precoding and analog BF schemes. The interplay of analog and digital beamformers was initiated to balance both scheme cost and overall performance. Hybrid BF, in general, is considered a spatial filter that has the potential to strengthen the desired signal elements and circumvent the impact of unwanted signal components in proximity. The major advantage that leads to hybrid BF selection is the number of RF chains, which is lower and limited by the number of transmitted packet data symbol streams. The BF and diversity gains are subject to the number of antenna elements. In a practical scenario, firstly on the Tx side, digital BF is executed at the baseband level (i.e., the transmitted signal and amplitude are calculated at the baseband frequency level). Next, analog BF helps control the antenna’s transmitted RF energy phase with sophisticated phase shifters [71]. The technique reduces energy in the sidelobe and simultaneously receives data packets in a specific direction.
Similarly, the wide-scale M-MIMO antenna arrays offer many DoFs that help to improve the network performance by minimizing fading effects. Implementing hybrid BF is considered an optimal choice for M-MIMO antenna systems compared to the case of digital BF for B5G networks. The legacy digital BF request for at least one RF chain for each antenna element thus resulted in huge algorithmic complexities and costs, especially in mmWave M-MIMO communication [72]. Conversely, hybrid BF utilizes analog phase shifters with fewer RF chains, ultimately supporting fewer complexities and cost efficiency with almost the same network service. Overall, hybrid BF is a method that adjusts a sharp tradeoff between the SE, EE, and sophisticated hardware difficulties to reap the benefits of analog and digital BF techniques.
The BF techniques have shown prolific behavior in a real NR 5G communication environment and mitigated several conventional constraints. Previously, two-dimensional (2D) BF was employed to achieve spatial diversity gains and avoid in-air transmission losses [73]. This theme has justified the 5G radio communication demands, yet 2D beam features are severely restricted to design issues. It is primarily because its beam patterns only propagate in two planes, either vertical or horizontal. Thus, it can solely differentiate users from two different angles i.e., either horizontal or vertical.
Following the continuous progression of various intelligent wireless access technologies and deficiencies in conventional mode, 2D BF has also steadily evolved to three-dimensional (3D) BF methods [74]. The advanced 3D BF technique is regarded as a major modification in beam management and antenna lobes patterns, as it can regulate the strength of the RF beam patterns spatially in different directions. Realizing the small aperture of a large-scale MIMO antenna packed closely at the BS, the 3D BF technique is manifested as a suitable candidate due to its agility in identifying users in space [75]. It also helps in achieving spatial domain usage efficiency by forming the random cubic angle dimensions of beam patterns. Nonetheless, the performance of the 3D BF in cellular communication extensively relies on the properties of antenna RF beam patterns. It is a prime technology for B5G that can significantly improve network performance and SE. Thus, in-depth analysis and applications of 3D BF in the presence of M-MIMO communication are indispensable. This subsection is summarized in Table 6.
3.5. Tera Hertz Spectrum and M-MIMO
The THz frequency bands are already on the horizon, and many public and private wireless accessing research sectors are testing different ranges of the THz spectrum [76]. The wireless standardization authorities firmly stated that THz bands in the range between 0.1 THz and 10 THz are potential candidates for the upcoming mobile 6G communications networks and will assist in managing the speedy data demand of the future Internet of Everything (IoE) [77]. Tremendously high-frequency radio bands are competent in raising the different parameters of wireless carriers. Specifically, secrecy and privacy, large access to a green line spectrum, reputable energy and power consumption, flexible and stringent wireless backhaul connection, support of ultra M-MIMO antennas, sophisticated and portable hardware components inclusive of focused beam patterns, and an interference-friendly propagation environment [78,79]. The initial concept started circulating after the successful completion of 3GPP release-17 standards and protocols; after that, a lot of attention has been dedicated to enabling a tremendous high-frequency radio spectrum in indoor and outdoor B5G use cases. Likewise, further analysis and bench tests, particularly for extreme URLLC cases, are utterly needed and worthwhile for radio communication furtherance [80].
In the THz spectrum, radio waves above 300 GHz would be highly advantageous in spectroscopy, holographic, industry 4.0, remote surgery, and ultra-massive scale operational amenities [81]. The mmWave and THz untapped spectrum and availability of a bundle of different channel BWs, besides their attractive propagation qualities and applicability in real environment scenarios, are also the major motives behind the inclusion of M-MIMO in ongoing 5G and future networks [82]. Generally, single-antenna propagation has very bad directivity and random radiation patterns of RF waves. It is critical in the current wireless communication and for B5G to implement very high directive antennas. Researchers have suggested that this could be conceivable by deploying massive antenna elements in the given electronic and geometrical composition without compromising the size of arrays [83]. However, antenna construction and design for 1000 GHz and above frequency bands, deployment scenarios, channel categorization, the impact of environmental effects, etc., need extensive further exploration [84,85]. Another impactful propagation property of the THz spectrum is its efficiency in diminishing many traditional antenna transmission issues. Link budgeting can display a high sense of reliability and adaptability in radio signal propagation. Nonetheless, new techniques overcome many challenges but always introduce various other unprecedented obstacles [86]. For example, spatial multiplexing and dedicated user-centric beam transmitting mechanisms are highly susceptible to various types of blockages such as inter-cluster interference, control channel interference, etc. [87], whereas issues related to SER performance, receiver model and structure, channel modeling, and spectrum sensing need to be addressed to achieve the potential of THz M-MIMO transmission systems [88]. Some of the recent technical works that have been derived by different researchers across the world for the energetic and ultra-reliable seamless transmission of data symbols using THz in the M-MIMO system are discussed in this section. This subsection is summarized in Table 7.
3.6. Wireless Backhaul with THz and M-MIMO Systems
To institute an ultra-dense network (UDN) with high consistency, well-grounded infrastructure, and cost-effective network operation management, a higher spectrum (i.e., GHz and THz) backhaul connection between high power BS and low power nodes is vital [89,90,91]. A large-scale spatial stream M-MIMO armed with a BF mechanism and distributed over a large geographical area using high-frequency bands would be beneficial to avoid fiber optic and copper cables, and other pertinent hardware deployment costs [92,93]. It has been decidedly validated by scientific societies, engineers, and academia that extreme frequency channel BW is acceptable and reliable to securely govern the wireless backhaul in UDN multi-tier HetNet systems [94]. The prominent attributes of mmWave and THz spectrums over backhaul transmission are as follows: (i) Most of the spectrum is untapped in mmWave and THz bands, and the underutilized frequency bands are leveraged to ignite the GHz and higher transmission BW. (ii) M-MIMO or ultra M-MIMO with narrow beams using BF and RIS would empower the ultra-high-frequency resources to support signal directivity and link quality by resisting path losses [61]. THz communications for backhaul links present a promising advancement in migrating present-day urban 5G systems, offering ultra-high data rates and low latency. This technology addresses the increasing demand for bandwidth and supports the high-speed data transfer required for densely populated urban environments. The primary challenge lies in the susceptibility of THz waves to atmospheric attenuation and obstacles, which requires advanced signal processing techniques and robust infrastructure. Potential solutions include the development of adaptive beamforming, advanced error correction algorithms, and the use of relay stations to enhance signal strength and coverage. The authors in [95] analyze THz communications, focusing on 300 GHz backhaul and fronthaul links. It provides planning and simulation of these links, using off-the-shelf modems, advanced RF, and photonic technologies in the Horizon 2020 EU-Japan project ThoR. However, the reliance on existing modem technology can constrain performance, advancements, and scalability. Similarly, the study in [96] presents a design of a cell-free backhaul network for smart cities that uses hybrid free-space optics (FSO) and THz communication between streetlights as APs to support 6G networks. That study demonstrated increased efficiency through an innovative switching-combining method and evaluations under different weather conditions. However, the performance of the hybrid FSO/THz system can be significantly affected by environmental factors, such as rain, fog, and atmospheric turbulence, which can degrade the signal quality. Integrating THz communications into existing 5G networks will significantly bolster network capacity and performance, paving the way for future 6G deployments. This subsection is summarized in Table 8.
3.7. MIMO System in Non-Terrestrial Networks (NTNs)
Recent developments in Release 18 for 5G NR2 (New Radio) have introduced significant enhancements, especially for Non-Terrestrial Network (NTN) scenarios. These advancements are pivotal for integrating the “Internet of Flying Things” and providing satellite coverage that complements terrestrial 5G networks. The authors in [97] introduced an enhanced method for detecting the Primary Synchronization Signal (PSS) in Low Earth Orbit (LEO) satellite communications for 5G networks through frequency domain analysis to handle low SNR and residual Doppler-induced frequency offsets. However, the proposed method has limitations in practical deployment due to the complexity of real-time implementation and processing. LEO satellites are the back boon of NTNs that are cascaded formations orbiting at 600 km and 1200 km altitudes. These satellites create a robust and reliable network capable of maintaining continuous and high-quality connectivity. The dual-orbit approach enhances coverage and resilience that provides a wider range of applications from IoT devices in urban environments to critical communication needs in isolated areas. Thereby, NTN significantly broadens the scope and impact of 5G technologies.
NTN has notable implications in enhancing the performance and reliability of 5G networks when integrated with massive MIMO technology. In NTN scenarios, MIMO systems can significantly improve the capacity and coverage of satellite communication networks. The authors in [98] introduced a framework for 6G IoT services in NTNs by integrating multicast massive MIMO technology based on the 3GPP Release 17. It provides multicast beamforming and user grouping to improve spectral efficiency and reduce latency for IoT applications. However, it is challenging due to the complexity of implementing precise multicast MIMO precoding across diverse IoT devices in real-world deployment. For instance, in the “Internet of Flying Things,” MIMO can handle multiple data streams, simultaneously ensuring robust and efficient communication in dense and reliable connectivity of aerial vehicles and drones. Similarly, MIMO can help mitigate interference and maximize the use of available spectrum in urban areas with high device density. In dense urban environments, massive MIMO-enhanced NTNs can provide additional bandwidth and improved service quality for overwhelmed terrestrial 5G networks [99]. In remote and maritime communications, where terrestrial infrastructure is lacking, LEO satellites equipped with massive MIMO technology offer a unique solution.
Similarly, massive MIMO is poised to revolutionize UAV communications by enabling robust, spectrally efficient, and high-capacity communication. The study in [100] explores the integration of NTN into 6G mobile communications by utilizing UAVs with massive MIMO technology. The study evaluates the effects of channel communication performance by measuring CSI for UAVs at different heights and paths and using spectral efficiency (SE) with Maximum Ratio Combining (MRC). However, the specific design for rotary-wing drones potentially limits adaptability to other types of UAVs and broader UAV network configurations. Integrating massive MIMO in UAV communications within the 5G ecosystem can substantially elevate the performance and reliability of UAV networks. The study in [101] analyzed NTN’s effects on UAVs by considering the channel model, UAV height from ground users, and the capacity of MIMO antenna systems. The findings indicate improved prediction of UAV signal path loss in NTNs because a higher BS height increases the optimal BS antenna tilt angle. However, the proposed models do not account for real-world environmental factors like urban structures or weather conditions, potentially limiting the applicability of the derived optimal tilt angles. Furthermore, the beamforming capabilities of massive MIMO can precisely direct signals toward UAVs, enhancing signal strength and reducing the likelihood of link failures. MIMO-based advancements in UAV applications include real-time high-definition video streaming, autonomous navigation, and coordinated multi-UAV operations.
A massive MIMO system with reflecting intelligent surfaces (RISs) is a widely researched approach for enhancing wireless communication in 5G and beyond. RISs involve the deployment of programmable surfaces that can reflect and manipulate electromagnetic waves to control the propagation environment effectively. The authors in [102] introduced UAV integration with reconfigurable intelligent surfaces (RISs) to enhance coverage in a massive MIMO network. The approach maximizes network throughput by optimizing power control, phase shifts, and an iterative algorithm for efficient computation. However, the proposed optimization technique requires substantial computational resources for large-scale networks and rapid adaptation to dynamic environments. 5G signal coverage and capacity can be significantly improved by strategically placing RISs in urban environments to avoid physical obstructions and signal blockages. RIS technology can enhance spectral efficiency and increase data rates by creating favorable propagation conditions and enabling better utilization of high-frequency bands in 5G networks. This subsection is summarized in Table 9.
4. Challenges for M-MIMO in B5G/6G
4.1. Propagation Loss Issue
The data rates requisite in the mobile communication system are increasing rapidly because of the simultaneous interaction of enormous intelligent devices. Concerning this, the THz band will also play a key role in future high BW applications’ performance, as severe signal distortion and poor diffraction characteristics remain the limiting elements of the radio coverage area. The authors in the paper [103] have proposed a combined method of RIS and low-complexity beam training and a hybrid BF design for the THz multi-user M-MIMO system to reduce THz band propagation loss. Also, a ternary tree has been proposed for the BS and users to reduce search complexities. After performing a numerical analysis, the results showed that with adequate quantization resolution with M-MIMO, the designed scheme would be applicable to future THz communication. In another article [104], the researchers discuss the issues of inter-symbol and inter-user interferences, in addition to propagation loss at THz frequency bands. A single-carrier minimum mean square error (MMSE) precoding and detection framework has been presented for frequency-selective THz channels to eliminate the inevitable obstacles. The authors have suggested via a simulation test that the proposed scheme is capable of holding satisfactory performance in terms of bit error rate, while a few researchers have fabricated a joint BF method based on fractional programming in plasmonic ultra M-MIMO antenna elements to overcome the THz frequency band attenuation problem in [105]. The proposed methodology achieved reputable channel performance and has shown high reliability in a realistic communication environment. In the article in [106], the authors attempted to resolve the propagation constraints and enhance the THz communication range as well as achievable capacity. It presented the ultra M-MIMO concept by using plasmonic nano-antenna arrays to overcome the limitation of channel propagation and eventually enable wireless tera-bits/sec links between compact smart products over several tens of meters. Preliminary results of the proposed scheme have shown that frequencies in the 0.06–1 THz range metamaterial nanoantenna system can be considered to design plasmonic nano-antenna elements with hundreds of elements in a few cm2 for both transmission and reception.
4.2. Hardware Cost and Algorithmic Complexities
Although the hybrid precoding process has exhibited excellent results in the reduction in hardware costs and algorithmic complications in mmWave M-MIMO networks, THz communication blockages and path loss have a significant impact on the link performance, and a modified hybrid precoding technique is demandable. Therefore, in a recently published paper [107], the authors have employed a two-way amplify and forward (AF) relay in an OFDM-based THz M-MIMO system and achieved higher performance than other existing schemes regarding sum rate and EE. Meanwhile, a low-complexity beam squint mitigation method and an orthogonal matching pursuit (OMP) mechanism with low training overhead have been utilized for the interference and propagation constraints of the THz spectrum [108]. The simulation results validated that the designed scheme enabled accurate CSI acquisition in low SNR and advised that wideband M-MIMO will play a key role in optimizing THz mobile networks. In the article in [109], some researchers attempted to combat the fundamental challenge of millidegree-level 3D direction-of-arrival (DoA) prediction and millisecond-level beam tracking with a reduced pilot overhead in THz dynamic array-of-subarray (DAoSA) systems. A DAoSA-MUSIC and a DL-based DCNN scheme have been exploited to mitigate the super-resolution DoA estimation issue. The proposed methods accomplished super-resolution DoA estimation eliminated 50% pilot training overhead and exploited much better performance over contemporary methods.
4.3. High Power Consumption
Recently, several studies have shown the great potential of CF M-MIMO in wireless communication. The authors in [110] compared the EE and SE between CF and cellular M-MIMO systems along with practical deployment metrics for rural, urban, and suburban environments. While a high power consumption issue in CF networks was considered [111], a novel low-complexity power control technique with zero-forcing (ZF) precoding design has been formulated to tackle the constraint. Likewise, the authors in [112] have investigated the performance of CF M-MIMO systems by examining the DL coverage probability and achievable rate. The results showed that CF M-MIMO outperformed the small cell design under coverage and rate. Since low-quality antennas are required to guarantee energy and economic efficiencies in realizing a CF M-MIMO in a real environment, hardware impairments become a challenging problem. The authors in [113] have proposed a steady hardware distortion model to analyze the performance of UL and DL CF M-MIMO systems with transceiver hardware design impairment. Consequently, the proposed scheme’s numerical results validated the performance of CF M-MIMO without compromising the quality of service (QoS), whereas the authors in [114] have investigated the impact of hardware impairments on the physical layer security for a CF M-MIMO system with a pilot spoofing problem. An LMMSE channel estimator for the proposed design has been derived, and a closed-form ergodic secrecy rate has been attained. In the consequence phase, noise drastically degraded the antenna performance, and LOS components may be exposed to fading variations for particular cases.
4.4. Channel Estimation and Feedback Overhead
The limitations of channel estimation and feedback overhead in massive MIMO configurations present significant challenges for next-generation broadband networks. In massive MIMO, accurate channel state information (CSI) is essential for optimal beamforming and spatial multiplexing. The study in [115] proposed a DL-based framework for joint channel estimation and feedback in massive MIMO systems to handle the challenge of obtaining accurate downlinks. The framework uses an explicit network with a multi-SNR approach for channel estimation and a deep residual network for reconstruction to compress pilots for feedback for reduced network parameters and quantization errors. However, the complexity of implementing and maintaining DL models for real-time channel estimation can pose challenges in terms of computational resources and latency. Similarly, obtaining precise CSI in real-time is extremely challenging in dynamic environments with high mobility or rapid channel variations [116]. Moreover, the complexity is further enhanced due to the feedback loop for fast and reliable communication between the base station and user devices. Developing advanced algorithms for efficient CSI estimation and reducing feedback overhead is critical to overcoming these challenges of massive MIMO in next-generation networks.
4.5. Physical Space and Aesthetics
Deploying massive MIMO arrays in urban areas for 5G and beyond poses significant physical space and aesthetics challenges. Urban environments are densely populated with buildings, streets, and other infrastructure; therefore, it leaves limited space for the installation of large antenna arrays [117]. The size and number of antennas required for massive MIMO can be obtrusive and clash with the architectural aesthetics. The massive M-MIMO arrays need to be strategically placed to ensure optimal coverage and performance for 5G networks to achieve their full potential. Moreover, the placement of massive MIMO antennas on existing structures, such as rooftops and utility poles, must consider both structural integrity and the visual impact on the skyline. The study in [118] utilizes a phased array design to support long-range wireless communication, achieving high isolation between the elements and directional radiation patterns by leveraging a 3D volumetric form factor. However, the electrochemical metallization process and the reliance on 3D printing for small inter-element spacing can introduce manufacturing challenges and variability in antenna performance. Innovative solutions, such as integrating antennas into street furniture or designing more compact and visually appealing antenna arrays, are being explored to address these concerns. The deployment of massive MIMO in urban settings also involves considerations for maintenance access and potential radiation exposure concerns from the public.
4.6. Security
The prevalence of wireless access devices and hefty data volume usage for assorted mobile applications, such as mobile payment, banking, social media activities, etc. [119], provide agility and readiness in human routine activities. Unfortunately, privacy and information security are two prime concerns that remain vulnerable issues, owing to the dynamic broadcast nature of wireless communication. Concerning this, the authors in [120] have developed a spoofing attack detection method, i.e., channel virtual/beamspace representation for static and online detection algorithms for dynamic mmWave M-MIMO 5G networks. The results showed 25% and 99% improvements in detection rate with the channel virtual and learning-based schemes, respectively. Authentication plays a pivotal role in providing robust security service by confirming a requesting node’s identity and avoiding adversarial impersonation to access the channel. The authentication method has become a prominent requirement in M-MIMO antenna systems due to the incompatibility issues of conventional cryptography-based authentication protocols. Numerous studies have been conducted on the physical layer protocol authentication methods [121,122,123], and the authors in [124] have classified them into three broad types: watermarking, channel-based, and fingerprinting authentications. Nonetheless, all of the proposed strategies are precisely based on the non-M-MIMO systems and considered an ideal transceiver hardware design, which is not practical. Though few recent studies have utilized intelligent learning techniques [125,126], particularly for upcoming aerial platform scenarios [27,127], more advanced approaches are much needed. Similarly, the complex nature of massive MIMO systems introduces several security vulnerabilities. The extensive use of beamforming can also be exploited by adversaries to eavesdrop on communications or launch jamming attacks. One key security issue is the increased risk of physical layer attacks, such as pilot contamination, where attackers send fake pilot signals to interfere with legitimate channel estimation processes, degrading system performance and compromising data integrity. Additionally, it is challenging to detect and mitigate unauthorized access and malicious activities in the amplified attack surface of massive MIMO antennas and devices. The authors in [128] propose using a Generative Adversarial Network (GAN) to detect pilot contamination at the base station by generating and discriminating between real and synthetic signals. However, the computational complexity of GANs can lead to high resource consumption, making real-time implementation challenging in large-scale systems. Advanced encryption and authentication mechanisms at the physical layer are essential to protect data privacy and ensure secure communication. Moreover, the integration of massive MIMO with other technologies like network slicing and edge computing in 5G networks further complicates the security landscape. Ensuring end-to-end security across different network slices and edge devices requires robust and adaptable security frameworks. Addressing the security challenges at the physical layer is essential to safeguarding the integrity and confidentiality of data in massive MIMO-based 5G and future networks.
5. Open Issues and Future Research Directions
Various methods and experimental tests have been developed for the efficient and smart usage of M-MIMO systems in 5G cellular networks. Moreover, new wireless technologies such as ultra-massive MIMO, THz, sub-mmWave, and VLC require immense research effort and practical analysis before appropriate deployment in the current radio communication environment. A few of the prospective research activities in M-MIMO and other discussed technologies for B5G networks are discussed below.
5.1. Narrow Aperture Antenna Nodes
The massive-scale deployment of advanced intelligent antenna systems can minimize the effects of fading, with noise as interference. This wide-ranging implementation increases the load on the system performance and raises computational and algorithmic complexities. Future M-MIMO antenna arrays should be configured at minimum cost and with sophisticated small-area components [129]. Fairness among the smart objects is another important aspect in M-MIMO deployment due to its prevalence all over the cell. In a conventional approach, the M-MIMO system throughput can be enhanced by only scheduling users with high SINR values and ignoring the rest of the access requests, especially those who reside at cell edges and have poor channel conditions. This would certainly demean the user reputation and can escalate overall unsatisfactory network performance; thus, high-level fairness among all the smart nodes must be ensured [130].
5.2. Plasmonic Antenna Arrays
To increase antenna elements, at the BS, beyond the scope of M-MIMO and remove the barrier of λ\2 sampling of the space dimension, the graphene-based plasmonic structure can be a probable solution for developing nanoscale transceiver designs with a maximum space dimension of λ\20 and eventually densely integrating large elements in small footprints (1024 elements under the 1mm2 dimension). Regrettably, graphene does not conduct appropriate signal processing at high-frequency bands, and further study on the incorporation of met surfaces into plasmonic transceiver arrays has been suggested [131]. The plasmonic reflect array antenna is another promising approach to enable ultra M-MIMO in a 3D environment with sizes ranging from 1 mm2 to 100 mm2 depending on mmWave or THz operating frequency bands. Undeniably, the sub-wavelength size of reflect array elements permits controlled reflections in non-specular directions and reflections with polarization conversion [132].
5.3. Integrated Communication and Sensing with M-MIMO
The upcoming 6G wireless services will be exclusively based on simultaneous radio communication and sensing paradigm exposure. In mobile networks, sensing precisely concerns the location of objects, design, detection, and the estimation of moving object speed via RF signals. The implementation of sensing using WiFi focusing on indoor scenarios has been comprehensively discussed in [133]. In terms of sensing for outdoor use cases using cellular networks, application scenarios could be traffic operation and monitoring, the discovery of free parking slots in dense proximity, humidity detection in agriculture cases, vehicle and pedestrian detection, etc.; therefore, further research in this direction would enrich future 6G wireless systems [134]. Furthermore, the authors in [135] have presented the vision of distributed joint communication and sensing systems exploiting distributed M-MIMO antenna systems for purposes such as ultra-reliable communication, precise localization, and distributed radar capabilities in passive mode. In the future, factories will not only stipulate very high data success probability but also strive for extremely accurate localization, and stringent integrated communication and sensing approaches with distributed M-MIMO have been characterized as an attractive theme for 6G networks.
5.4. Federated Learning in M-MIMO
D2D communication can be the key element for improving the efficiency of distributed federated learning computations [136]. In this regard, the channel transmission characteristics need to be perfectly modeled to maintain the SER boundary up to the 10−8 percentile and evade convergence rate performance issues. Unfortunately, the reconstruction of local gradient vectors at the central server node, which is assessed and computed and then transmitted from the smart devices, is a paramount challenge in system design. To overcome this challenge, a compressive sensing scheme to trigger the server and iteratively locate the linear MMSE of the transmitted signal by using its sparsity aspect was presented in [137]. The results showed perfect reconstruction and reduced the performance gap between centralized and federated learnings; the same approach could be further analyzed for the device scheduling method, or a transmission strategy could be designed for broadcasting the parameter vector to the radio devices and for investigating the performance of federated learning over an M-MIMO system. Since accurate channel estimation and pilot training overhead are critical parameters in low-latency and high-reliability data symbol transmission for time-sensitive critical cases, the rollout of 5G networks has facilitated support for data-intensive applications, moving towards global connectivity and improved network infrastructure through technologies like network function virtualization (NFV). ML algorithms have shown promise in addressing complex network optimization problems, enhancing the efficiency of these networks in diverse environments [138]. Recently, researchers have analyzed FL approaches to resolve the everlasting estimation and signaling overhead problems [139,140]. For this topic, academic explorers can design federated and distributed learning-based channel estimation schemes that can be exploited in multiple scenarios without extra training [141].
5.5. Infrared-Based Industry 5.0 in MIMO
The joint beam-steering and control signaling in massive MIMO is a potential approach to revolutionize industrial automation and connectivity of infrared-based industry 5.0 solutions for IoT in factories. The authors in [142] introduced a MIMO uplink transmission scheme for optical wireless communication to achieve efficient data transmission over infrared channels. However, due to increased signal complexity and noise, the proposed scheme can encounter challenges in maintaining low BER and peak-to-average power ratio (PAPR) under high modulation orders. This technology promises to enhance data transmission efficiency, reduce latency, and improve overall network reliability. However, the challenges are significant due to the requirement of precise alignment and synchronization of infrared signals, managing interference, and ensuring robust security against cyber threats. One possible solution is the integration of advanced machine learning algorithms to optimize beam-steering and control signaling dynamically. Developing hybrid systems that combine infrared with other wireless technologies can also mitigate some of the inherent limitations.
6. Conclusions
Various new designs and advancements in technologies such as large-scale multi-antenna arrays, hybrid BF, higher spectrum, and IAB links have been introduced to overcome the conventional obstacles of wireless networks. This article presents a discussion on the M-MIMO system, including its feasibility, applicability, and key aspects in B5G networks. Additionally, this paper provides insight into the effectiveness of M-MIMO with other enabling technologies, precisely BF, higher frequency bands (i.e., mmWave and THz), IAB communication, and AI/ML algorithms. Although a wide-scale M-MIMO system possesses enormous benefits for current 5G and upcoming 6G wireless networks, many implementations and signal optimization issues still need to be diminished to reach the full potential of smart active antenna element processing. Similarly, other cornerstone technologies, such as BF and mmWave, demand upgrades in antenna designs and propagation strategies. Moreover, the essence of the THz spectrum is also a key element in supporting the abundance of wireless equipment and meeting future necessities. This paper highlights the recent trends and state-of-the-art research schemes presented in the literature.
Conceptualization, F.Q., M.T. and S.H.A.K.; methodology, F.Q. and K.A.Z.A.; visualization, F.Q. and K.A.Z.A.; writing—original draft preparation, F.Q., M.T. and S.H.A.K.; writing—review and editing, S.H.A.K., K.A.Z.A. and Q.N.N.; funding acquisition, F.Q. and Q.N.N., Supervision, Q.N.N. All authors have read and agreed to the published version of the manuscript.
The authors thank the respected editors and reviewers for their support.
The authors declare no conflicts of interest.
Footnotes
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Summary of the related literature.
| Ref. | Technologies | Contribution | Limitations |
|---|---|---|---|
| [ | M-MIMO, ML methodologies | Analysis and presentation of detection algorithms for M-MIMO systems | Lacks coverage of integration aspects into future network paradigms in B5G/6G |
| [ | M-MIMO detectors, DL techniques | Examination of M-MIMO detectors employing deep learning techniques | Partial coverage of the emerging B5G/6G paradigm and integration aspects of MIMO |
| [ | mmWave massive MIMO systems | Survey on challenges and advantages of mmWave massive MIMO systems | Addresses enhancements in user throughput, spectral efficiency, and energy efficiency but lacks integration into B5G/6G |
| [ | DL approaches in 5G domains | Categorization of different DL approaches based on their application in 5G domains | Provides insights into individual components but lacks a holistic view of M-MIMO design |
| [ | RRM procedures utilizing ML algorithms | Presentation of various aspects of radio resource management procedures utilizing ML algorithms | Focuses on RRM procedures and ML algorithms, lacking integration aspects into future network paradigms in B5G/6G |
| [ | Linear precoding techniques for massive MIMO | Examination of linear precoding techniques for massive MIMO systems in a single-cell scenario | Evaluates performance of linear precoding methods but does not cover integration aspects into future network paradigms |
M-MIMO systems and AI/ML approaches.
| Tech. | Ref. | Area | Impact | Limitations |
|---|---|---|---|---|
| M-MIMO with AI/ML Approaches | [ | ML (AMPBML) | Minimized training slots, concurrent beam alignment for multiple users | Potential loss of performance |
| [ | ML | Improved mmWave beam prediction in highly mobile vehicular environments | Minor loss of performance | |
| [ | ML (k-NN, SVM, multilayer perception) | Improved angle of arrival (AoA) estimation | Computational inefficiency | |
| [ | Deep learning (DLCS) | Better channel estimation performance in MU-mmWave M-MIMO systems | Potential power consumption and hardware cost challenges | |
| [ | Dictionary-trained beam selection matrices | Enhanced channel estimation performance | Experimental validation required | |
| [ | Neural hybrid BF/combining strategy | Higher BER compared to other linear matrix decomposition methods | Potential limitations in practical implementation |
M-MIMO systems in B5G/6G.
| Tech. | Ref. | Area | Impact | Limitations |
|---|---|---|---|---|
| Reconfigurable Intelligent Surfaces | [ | Algorithmic difficulties, computing time | High-precision delay-sensitive arenas with programmable metasurface capabilities and low power consumption | Increasing the size and geographical distribution of the RIS array escalates algorithmic complications. |
| [ | RIS, passive BF | Each element of RIS can independently fine-tune signals, leading to high data success and reliability. | Complicates algorithms and adds latency in packet delivery | |
| [ | RIS, passive components | RIS can positively converge reflected signals to desired locations | Requires stringent delay management and ultra-high reliability | |
| [ | RIS, algorithmic complications, latency | The size of the RIS array significantly impacts the data success probability ratio and latency. | Increasing the size of the RIS array adds to algorithmic complications. | |
| [ | RIS, delay management, ultra-high reliability | Achieves stringent delay management and ultra-high reliability | It is difficult to ensure ultra-high reliability and manage delays in communication systems | |
| [ | RIS-aided M-MIMO, imperfect CSI, ZF detectors | Investigation of RIS-aided M-MIMO performance under imperfect CSI with ZF detectors | Latency evaluation and energy management require further exploration. |
VLC for M-MIMO systems in B5G/6G.
| Tech. | Ref. | Area | Impact | Limitations |
|---|---|---|---|---|
| VLC | [ | LiFi, RF spectrum congestion, indoor environments | Release congestion of RF spectrum—enables low-cost, reliable data access | -Performance affected by amplification noise at the receiver ZF or MMSE |
| [ | M-MIMO VLC system | Future data rate demand can be met through M-MIMO VLC systems | -Affected by amplification noise at the receiver and non-linear LED | |
| [ | VLC MIMO systems, joint IQ independent component analysis (ICA), ML techniques | Addressing spatial multiplexing challenges, enhancing spectral efficiency (SE) | -ML processing and computational requirements in resource constraint devices | |
| [ | MIMO-VLC system, (ANN), joint spatial and temporal equalization | Surpassing traditional decision feedback equalization (DFE), addressing non-linear transfer functions and cross-talk | -Increased complexity and interoperability issues |
Hybrid Beamforming M-MIMO systems in B5G/6G.
| Tech. | Ref. | Area | Impact | Limitations |
|---|---|---|---|---|
| Hybrid Beamforming | [ | LiFi, RF spectrum congestion, indoor environments | Release congestion of RF spectrum—Enable low-cost, reliable data access | Performance affected by amplification noise at the receiver ZF or MMSE |
| [ | M-MIMO VLC system | Future data rate demand can be met through M-MIMO VLC systems | Affected by amplification noise at the receiver and non-linear LED | |
| [ | Beamforming (BF), hybrid BF | Explored the combination of digital precoding and analog BF schemes, balancing cost and performance | Legacy digital BF requires one RF chain per antenna element. | |
| [ | Digital BF, analog BF, phase shifters | Reducing sidelobe energy, directing RF energy, improving network performance, minimizing fading effects | Complex algorithmic requirements, high costs in legacy digital BF, especially in mmWave M-MIMO communication | |
| [ | Hybrid BF | Combining analog and digital BF, utilizing fewer RF chains, reducing complexity and cost | Difficulty in balancing the tradeoff between spectral efficiency (SE), energy efficiency (EE), and hardware difficulties | |
| [ | 2D beamforming | Achieving spatial diversity gains, avoiding in-air transmission losses | Severe restriction to design issues due to propagation limited to two planes (vertical or horizontal) | |
| [ | 3D beamforming | Major modification in beam management and antenna lobe patterns | Performance extensively relies on properties of antenna RF beam patterns |
THz and M-MIMO systems in B5G/6G.
| Tech. | Ref. | Area | Impact | Limitations |
|---|---|---|---|---|
| Tera Hertz Spectrum and M-MIMO | [ | THz frequency bands (0.1 THz to 10 THz) | Potential candidates for 6G networks | Need for further analysis and bench tests, particularly for extreme URLLC cases. |
| [ | THz spectrum | Enhance wireless carriers’ parameters (secrecy, privacy, energy efficiency, etc.) | Deployment challenges, environmental impacts | |
| [ | mmWave, untapped spectrum | Attractive propagation qualities | Challenges in achieving very high directive antennas | |
| [ | THz spectrum (above 300 GHz) | Advantages in spectroscopy, holographic, industry 4.0, remote surgery | Need for extensive exploration of antenna design | |
| [ | M-MIMO in 5G and future networks | Utilization of mmWave and THz spectrum, attractive propagation qualities | Susceptibility to various types of blockages | |
| [ | Very high directive antennas | Achieving high directive antennas for B5G | Challenges in deployment, environmental impacts | |
| [ | RF spectrum in mmWave and THz | Impactful propagation properties of THz spectrum | Deployment challenges, susceptibility to blockages | |
| [ | THz M-MIMO transmission systems | Energetic and ultra-reliable seamless transmission of data symbols | Issues related to SER performance, receiver model and structure, channel modeling, spectrum sensing | |
| [ | Spatial multiplexing | Achieving the potential of THz M-MIMO transmission systems | Susceptibility to inter-cluster interference, control channel interference | |
| [ | Different bands’ channel parameters and characteristics | Comparison between bands’ characteristics | Need for addressing issues related to SER performance, receiver model and structure, channel modeling, and spectrum sensing. |
Wireless Backhaul with THz and M-MIMO systems in B5G/6G.
| Tech. | Ref. | Area | Impact | Limitations |
|---|---|---|---|---|
| Wireless Backhaul with THz and M-MIMO Systems | [ | Higher-spectrum backhaul | Enables ultra-dense network (UDN) with high consistency and cost-effectiveness | Potential challenges in hardware deployment and maintenance |
| [ | Large-scale spatial stream M-MIMO | Reduces reliance on fiber optic and copper cables and lowers hardware deployment costs | Interference and coordination issues due to densely packed nodes | |
| [ | Extreme frequency channel BW | Enhances wireless backhaul reliability and efficiency | Compatibility issues with existing infrastructure and devices | |
| [ | RIS for backhaul transmission | Improves signal directivity and link quality | Challenges in beamforming and susceptibility to atmospheric conditions | |
| [ | 300 GHz backhaul and fronthaul links | Advanced RF and photonic technologies in the Horizon 2020 EU-Japan project ThoR | Constraints for performance, advancements, and scalability related to modems | |
| [ | Hybrid free-space optics (FSO) and THz | Increased efficiency through innovative switch combining | Hybrid FSO/THz system can be significantly affected by environmental factors, such as rain and fog |
MIMO system in Non-Terrestrial Networks (NTNs).
| Tech. | Ref. | Area | Impact | Limitations |
|---|---|---|---|---|
| MIMO System in NTN | [ | Low Earth Orbit (LEO) satellite NTN communications | Frequency domain analysis to handle low SNR and residual Doppler-induced frequency offsets | Complexity in real-time implementation and processing |
| [ | NTNs multicast in the 3GPP Release 17 massive MIMO technology | Multicast beamforming and user grouping to improve spectral efficiency | The complexity of implementing precise multicast MIMO precoding across diverse IoT | |
| [ | Integration of NTN into 6G mobile communications | Evaluates the effects of channel communication performance by measuring CSI for UAVs | The specific design for rotary-wing drones potentially limits adaptability to other types of UAVs | |
| [ | NTN effects on UAVs by considering the channel model | Improved prediction of UAV signal path loss in NTNs | The proposed models do not account for real-world environmental factors like urban structures or weather conditions | |
| [ | UAV integration with RISs | The approach maximizes network throughput by optimizing power control and phase shifts, utilizing an iterative algorithm. | The proposed technique requires substantial computational resources for large-scale networks. |
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; Syed Hussain Ali Kazmi 1
; Khairul Akram Zainol Ariffin 1
; Tayyab, Muhammad 2 ; Nguyen, Quang Ngoc 3
1 Centre for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia;
2 School of Computer Science and Engineering (SCE), Taylor’s University Lake-Side Campus, Subang Jaya 47500, Selangor, Malaysia;
3 Faculty of Science and Engineering, Waseda University, Shinjuku-ku, Tokyo 169-0051, Japan; Faculty of Telecommunications, Posts and Telecommunications Institute of Technology, Hanoi 100000, Vietnam