MassiveMIMOCommunications

Multiple-Input Multiple-Output (MIMO) technologies utilize multiple antennas at the transceivers and enable enhanced diversity or multiplexing gain. The first implementations of MIMO technology appeared in cellular communication and Wi-Fi systems. One of the main problems with early MIMO systems is their limited performance in meeting the requirements of next generation communication systems. Specifically, the parameters like peak data rates, spectrum efficiency and energy efficiency constraints cannot be met with existing MIMO architectures. Hence, early MIMO techniques evolved to massive MIMO (mMIMO) type of communication as the number of antennas at both transmitters and receivers increased. Included in 5G New Radio standard, mMIMO enabled an increase the spectral efficiency and coverage compared with earlier MIMO techniques. Nowadays, there exists an ever-increasing interest in the application of advanced MIMO technologies in 6G networks. These applications include ultra massive MIMO (mMIMO), holographic MIMO and intelligent reflecting surface (IRS) assisted MIMO. These new advanced MIMO techniques either employ larger number of antennas or extremely large aperture arrays compared with mMIMO and are named as extremely large-scale MIMO. This paper presents a detailed overview of all these techniques. Joint design of wireless communication and radar sensing, named as integrated sensing and communication (ISAC) as well, has shown to improve spectrum efficiency and reduce power consumption. Recent novel research on ISAC exploits the application of MIMO technology specifically in joint communication and radar sensing (JCS) systems. In this aspect, the other objective of this work is to exploit MIMO communication and MIMO radar coexistence. After summarizing the current state of the art on MIMO radar systems, this paper exploits joint application of advanced MIMO communication and radar sensing. Finally, it aims to offer a perspective on future research directions and the potential of overall MIMO systems discussed in the paper.

Multiple input multiple output (MIMO) technology has evolved over the past few years into a technology with great potential to drive the direction of future wireless communications. MIMO technology has become a solid reality when massive MIMO systems (MIMO with large number of antennas and transceivers) were commercially deployed in several countries across the world in the recent past. Moreover, MIMO has been integrated into state-of-the-art paradigms such as fifth-generation (5G) networks as one of the main enabling technologies. MIMO possesses many attractive and highly desirable properties such as spatial multiplexing, diversity gains, and adaptive beamforming gains that leads to high data rates, enhanced reliability, and other enhancements. Nevertheless, beyond 5G technologies demand wireless communication systems with, among other properties, immensely higher data rates and better reliability simultaneously at the same time. In this work, a new, novel MIMO technique for simultaneously achieving multiplexing and diversity gains as well as completely eliminating any processing at the MIMO receiver, leading to advantages such as low complexity and low power consumption, is proposed. The proposed technique employs the design of interference-canceling matrices, which are calculated from the channels between the transceiver antennas, where the matrices are employed at the base station to help achieve multiplexing and diversity gains simultaneously. The novelty and efficiency of the introduced paradigm is demonstrated via mathematical models and validated by Monte Carlo simulations. Results indicate that the proposed system outperforms conventional MIMO models.

The multiple-input multiple-output (MIMO) wireless system can either be massive or conventional “small-scale” ones. It has been verified that the massive MIMO technology has a great potential to improve transmission range, spectral efficiency, and coverage compared to conventional MIMO. For realistic antenna configurations systems, there is an increase in the computational complexity associated with signal detection in massive MIMO systems. Several theoretical iterative techniques have been proposed to balance the computational complexity and performance in massive MIMO systems. However, most of these methods have very slow convergence rate. Therefore, we employ the multiple search direction conjugate gradient (MSD-CG) method in massive MIMO uplink systems to reduce the data detection complexity and enhance the performance. The efficiency of the proposed method has been improved by a projection technique that requires a search direction in each sub-domain rather than forming all search directions conjugate to each other. In this paper, we compare the proposed approach to the approximate and exact methods. Our results indicate that the proposed algorithm with realistic antenna configurations is superior to the existing methods for large-scale MIMO systems.

Massive MIMO is an extension of MIMO (Multiple-input Multiple-output), which essentially groups together several number of transmitting antennas and receiving antennas in order to provide better spectrum efficiency and better throughput. Massive MIMO technology will almost be a key component of the super-fast 5G networks of the future generation. However, the Multiple User Interference (MUL) is the major problem that needs to be address to detect each user appropriately at the base station. Hence, detection of encoded signals is one of the crucial receiver functions in MIMO wireless communication. The well-known detection algorithms like Zero forcing detector (ZF) and MMSE provides less complex design at the cost of reduced performance. Maximum Likelihood detector (ML) gives the optimal performance, but its computational complexity limits its usage in practical systems. Thus, to balance the trade-off between complexity and performance, the suboptimal Likelihood Ascent Search (LAS) algorithm has been proposed. However, the LAS algorithm detection performance depends on selection of initial solution. So, we propose Neighborhood search algorithm to select the initial vector and this healthy initial vector can be used for LAS algorithm to find solution. This algorithm is iterative in nature and it searches for that vector which minimizes the maximum likelihood (ML) cost in the neighborhood. The proposed algorithm is expected to balance the tradeoff between performance and complexity of design.

Multiple-Input Multiple-Output (MIMO) wireless communication system, which is also called Multiple-Antenna system, is well known as one of the most important technologies and widely studied nowadays (Winters, 1987; Foschini & Gans, 1998; Marzetta & Hochwald, 1999; Raleigh & Cioffi, 1998). The main idea of MIMO wireless communication is to utilize the spatial degree of freedom of the wireless multi-path channel by adopting multiple antennas at both transmit and receive ends to improve spectrum efficiency and transmission quality of the wireless communication systems. MIMO technology is able to extremely improve the transmission data rates and alleviate the conflict between the increasing demand of wireless services and the scarce of electromagnetic spectrum. Two famous techniques of the MIMO systems are spatial multiplexing (SM) and transmit diversity (TD) (Nabar et al, 2002). In the scheme of SM, multiple data pipes between transmit and receive ends provide multiplexing gain to dramatically increase the channel capacity linearly with the number of antennas (Telatar, 1999; Bolcskei et al, 2002). The TD technologies, such as space-time coding, are adopted to improve the link reliability of wireless communication, especially in the multi-path fading channels (Marzetta & Hochwald, 1999; Tarokh et al, 1998; Bolcskei et al, 2001). The channel knowledge is not required in the transmit end for TD technologies. MIMO is the key technology for future wireless communication systems, such as 3GPP LTE, WiMAX 802.16, IEEE 802.20, IMT-Advanced and so on.

3rd Generation Partnership Project (3GPP) has recently completed the specification of the Long Term Evolution (LTE) standard. Majority of the world's operators and vendors are already committed to LTE deployments and developments, making LTE the market leader in the upcoming evolution to 4G wireless communication systems. Multiple input multiple output (MIMO) technologies introduced in LTE such as spatial multiplexing, transmit diversity, and beamforming are key components for providing higher peak rate at a better system efficiency, which are essential for supporting future broadband data service over wireless links. Further extension of LTE MIMO technologies is being studied under the 3GPP study item to meet the requirement of IMT-Advanced set by International Telecommunication Union Radiocommunication Sector (ITU-R). In this paper, we introduce various MIMO technologies employed in LTE and provide a brief overview on the MIMO technologies currently discussed in the LTE-Advanced forum.

Very large multiple input multiple output (MIMO) system (referred to as massive MIMO) is currently investigated as a novel technology to enhance system throughput for future cellular systems. In this paper, we study a practical implementation of massive MIMO technology in LTE-Advanced systems. Firstly, two three dimensional(3D) dynamic beamforming schemes are proposed to realize full spatial resolution of the elevation as well as the traditional azimuth domain. These schemes are implemented through combining a couple of precoder matrix indictor (horizontal PMI and vertical PMI) during the channel state information (CSI) feedback stage. Furthermore, we also propose a limited feedback precoding algorithm named PMI-based regularized zero-forcing precoding (P-RZF) to achieve multi-user MIMO (MU-MIMO). System level simulation is performed to evaluate the proposed schemes.

Diversity is an integral technique in the advancement of wireless communications. Multiple-input multiple-output (MIMO) technology continues to occupy a pivotal position in the fourth-generation (4G) standards and beyond. With the advent of full-dimension (FD) MIMO and massive MIMO, new paradigms are being targeted in the disruptive technology. Third-generation partnership project (3GPP) has also emphasized the advanced MIMO technology for achieving its vision of International Mobile Telephony (IMT) 2020. However, implementing multiple antennas can be cumbersome due to limiting factors such as size, cost, and hardware limitations. In such a scenario, where implementing many antennas is not feasible, the benefits of diversity can be reaped by employing cooperative communication. Single antenna mobiles can share their mobile stations giving rise to a virtual MIMO system. In this paper, the authors have analyzed the performance of cooperative mobile stations in a frequency selective channel. Orthogonal frequency division multiplexing (OFDM), known for its robustness against multipath fading, is the modulation technique employed to circumvent the hazardous effects of intersymbol interference (ISI). MIMO is the key technology for achieving capacity advantage and improving link reliability. The blend of these two cutting edge technologies in a cooperative relay system presents encouraging results and inferences. Maximal Ratio Combining (MRC) is employed at the relay and the receiver to maximize the signal-to-noise ratio (SNR). Decode and forward (DF) protocol is used at the relay node owing to its regenerative nature and adaptability. The closed-form expressions for bit error rate (BER) and outage probability (Pout) are derived and compared with the experimental results. The effect of power decay factor is analyzed and simulation is done for 2 × 2, 2 × 3, and 2 × 4 transmit and receive antennas. The number of antennas at the relay is kept as 2. The analytical results agree with the simulation results.

Multiple-input multiple-output (MIMO) has been accepted as a promising technology for its potential to achieve low bit error rate (BER) by space time coding [1] or to achieve large capacity by multiplexing [2]. MIMO multiplexing has been widely adopted to realize high speed data communications. The capacity of MIMO systems in the point-to-point transmission without external interferences has been studied in [3, 4] to show that large capacity can be achieved in a rich scattering environment. In a cellular environment, the same frequency/frequencies can be used in neighboring cells. As a result, co-channel interference exists and the channel between the base-station (BS) and the mobile-station (MS) is changed from noise-limited channel to interference-limited channel. Recently, the capacity of MIMO systems in the cellular environment has attracted much interest. Uplink (transmission from MS to BS) capacity with variable-rate transmissions is studied in [5]. By modeling the co-channel interference as additive white Gaussian noise (AWGN), the uplink capacity is also studied in [6, 7]. On the other hand, from the users’ stand point, the downlink capacity may be more interesting. However, the results for the downlink capacity of cellular MIMO systems presented in the literature are mainly based on the simulation results. Very detailed simulation results for the downlink MIMO capacity in 3G FDD WCDMA cellular systems can be found in [8]. By assuming single-frequency-reuse (the frequency reuse factor (FRF) equals to 1), the capacity of downlink cellular MIMO systems is studied by simulations and the results are presented in [9, 10]. These results are given in terms of the number of antennas, the modulation schemes, the propagation parameters as well as the cell size. However, FRFs other than 1 are not considered. By taking various FRFs into consideration, a comparative study on the capacity of cellular MIMO systems is presented in [11]. A comprehensive comparison between the capacity of SISO, SIMO, STBC-MISO and MIMO systems in a cellular environment is made based on the simulation results. In general, fixed FRF has been considered in cellular systems. It is reported in [12] that a flexible FRF may help to improve the capacity for cellular single antenna (SISO) systems. However, flexible design of FRF for cellular MIMO systems is rarely available in the literature. In this chapter, the downlink capacity of cellular MIMO systems is theoretically analyzed in terms of both the ergodic and outage capacities. The theoretical results of the best and worst situation capacities suggest that the greatest capacities may be achieved by using FRF 1 or FRF 3 adaptively according to the situations. Therefore, a hybrid frequency reuse scheme is

The advent of 5G technology has revolutionized wireless communication, offering unprecedented data rates, reduced latency, and enhanced connectivity. A critical component driving these advancements is Multiple-Input Multiple-Output (MIMO) technology. MIMO utilizes multiple antennas at both the transmitter and receiver ends to improve communication performance. In the context of the Internet of Things (IoT), MIMO plays a pivotal role in enhancing network efficiency, reliability, and capacity and can improve system capacity and reduce interference between different users. By leveraging MIMO, IoT devices can achieve higher data throughput and better signal quality, even in challenging environments. This is particularly important for IoT applications that require real-time data transmission and low latency, such as smart cities, autonomous vehicles, and industrial automation. Additionally, MIMO technology helps in mitigating interference and improving spectrum utilization, making it an essential enabler for the massive connectivity demands of IoT networks in the 5G era. However, due to the high channel dimension, complex channel estimation and precoding algorithms in the system, the system hardware and software overhead will increase. The precoding algorithms of massive MIMO systems are divided into three types: digital, analog and hybrid. The three types of precoding algorithms are summarized and compared, and the advantages and disadvantages of different precoding algorithms and applicable scenarios are summarized. The channel estimation schemes are divided into training estimation and blind estimation, and the advantages and disadvantages of the two types of schemes are summarized. It is pointed out that the reasonable use of the channel sparsity of massive MIMO can improve the quality of channel estimation and reduce the estimation overhead.

Multiple-input multiple-output (MIMO) technology equips the transmitters with the multiple antennas. It can combine with energy harvesting (EH) to lift the spectrum efficiency and make use of a greener energy resource. A power grid is added to serve as a supplementary source to regulate the not-so-stable harvested energy supply of the system. Besides the MIMO technology being used, the power allocated to the user provided by both EH and the power grid is subject to the epoch power upper bound constraints. The background of these constraints comes from field requirements, such as avoiding the saturation of power allocated to the user(s), avoiding system-level out-of-band power leakage, and reducing interference with other transmitter(s) due to the non-linearity generated via the transmitting mechanisms to the user(s). The epoch power upper bound constraints make this problem more challenging, with the controllable power grid energy budget and its allocation. This paper applies our recently proposed geometric water-filling with group upper bounded power constraints and recursion machinery to form the proposed algorithm for solving the proposed throughput maximization problem. Our algorithm is precisely defined, and further provides the exact solution via the lower degree polynomial complexity. This point is very suitable for the massive MIMO system. To the best of our knowledge, no prior algorithm has been reported in the open literature to solve the targeted problem in this paper.

Massive MIMO (multiple-input multiple-output) antenna technology can provide significant performance improvement for cellular systems in terms of both throughput and energy efficiency. It is widely recognized that inter-user interference can be eliminated with a large number of antennas because of the asymptotical orthogonality among users when linear MF (Matched Filter) downlink precoding is used in the eNodeB. In this paper, system level simulation performance for the downlink, based upon current LTE systems, provides an indication of the achievable potential system performance improvement by employing massive MIMO in future (5G) cellular networks. System simulations consider various performance limiting factors including non-ideal asymptotical orthogonality, transmit channel correlation, imperfect channel estimation and pilot contamination. It is demonstrated that even as the number of transmit antennas is increased to a large number, the accumulative effects of the non-ideal asymptotical orthogonality among different users constitute a significant limiting degradation to the system performance. Likewise, a significant performance decrease is also incurred due to transmit channel correlation, imperfect channel estimation, and pilot contamination.

A dual-port MIMO antenna system for 5G and Wi-Fi 6E communications using a parasitic element

Massive MIMO is a multi-user MIMO (multiple-input multiple output) technology that has the ability to provide uniformly better service to wireless terminals in high-mobility environs. The major concept behind this is to equip base stations with arrays of more antennas that are utilized to serve various terminals simultaneously. The term "massive" refers to the count of antennas and not the physical size. However, deployment of multiple antennas at each node obviously maximizes the hardware cost, system complexity, and transmitter power. Particularly, in MIMO systems, each antenna needs a specific radio frequency chain that rapidly increases the system complexity and is unpleasant since the antenna count increases. Therefore, it is disincentive to employ multiple antennas at all the nodes. As a solution to this, transmit antenna strategy (T AS) is evolved that minimizes system complexity, hardware cost and so on. Still, the problem rises in the selection criteria of transmit antenna. Recently, more research works have been focusing on this issue by offering advanced algorithms or models. Such a way that, this research work concentrates on optimal transmit antenna selection to solve the problems like system complexity, cost, etc. In this paper an improved optimization model is introduced for the optimal selection of antenna. The proposed algorithm is named as Modified Velocity vector-based GSA (MV -GSA), which is the enhanced version of Gravitational Search Algorithm (GSA) that defines the count of antenna. This work goes on with the determination of solving multi-objective problem that increases the capacity and relative Energy Efficiency (EE) as well. At last, this model proved as better performer compared over conventional models, like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Firefly (FF) Particle Swarm Optimization (PSO), and conventional GSA, respectively.

Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.