Performance Verification of MIMO Technology in WLAN System and Diversity Gain Issue

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.

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.

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.

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.

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

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

MIMO (Multiple Input Multiple Output) enabled systems are characterized by higher reliability and transmission rates, as compared to conventional SISO (Single Input Single Output) systems. However, unless administered properly, the MIMO technology may not facilitate very high throughputs on point-to-point wireless links. Therefore, it becomes imperative for the network architect to design such networks in ways that fully exploit the inherent properties of MIMO. In this paper, we first conduct an extensive experimental study, using a powerful hardware platform, in order to understand the behavior of MIMO links in different topological scenarios. Our experiments involve scenarios with MIMO links in isolation, as well as in competition with other MIMO and SISO links. Second, we perform measurements with different commercial platforms towards assessing the ability of each platform to efficiently support the MIMO technology. Based on our experimental observations we deduce that the CPU processing speed of the underlying hardware platform is an important factor that can hide the performance benefits of a MIMO enabled tranceiver. We comment on the applicability of the different hardware choices that we test; furthermore, we suggest the most appropriate choice for building a MIMO testbed, taking into account the cost, the extend-ability and the re-usability of the selected platform. Finally, having adopted this choice in our testbed design, we provide a description of our testbed architecture.

Over the last two decades, multiple‐input, multiple‐output (MIMO) technology has been successfully deployed on a wide scale in cellular communication systems. MIMO technology involves the use of multiple antennas at one or both ends of a communication link to boost the performance and reliability through strategies such as beamforming, diversity transmission, spatial multiplexing, and interference suppression. The currently‐deployed 4G/LTE cellular standards (LTE Rel‐8/9/10) support a comprehensive suite of MIMO techniques for up to eight antenna ports in a single sector on the downlink and up to four transmit antennas at a mobile station.For 5G cellular communications, massive MIMO, sometimes called full dimension MIMO, is a promising technology for enhancing system performance for frequency bands ranging from under 6 GHz to 100 GHz. Also, for 5G systems deployed in higher frequency bands such as cmWaves (6–30 GHz) and mmWaves (30–100GHz), large‐scale antenna arrays will be a prerequisite for overcoming the poor propagation characteristics in those bands.This chapter will describe the basics of massive MIMO and how it will satisfy the high‐data‐rate demands of 5G cellular systems for frequency bands up to 100 GHz. The current state of the art of MIMO technology are reviewed, and the application of large‐scale antenna arrays to 5G are described. The chapter also surveys current trends in massive MIMO technology and system concepts, with a focus on methodologies for significantly enhancing cellular system performance. Various trends and promising concepts are identified, and various practical issues highligh ted.

Multiple input multiple output (MIMO) is the next-generation technology for the 5G mobile terminals. MIMO uses multiple antennas and integrates multiple antennas within constrained space which is a crucial issue. This requires high isolation between antennas. MIMO technology will increase system capacity, link reliability, and the data rate required for 5G communication. This paper presents a recent MIMO dielectric resonator antenna (DRA) proposed by authors for future 5G applications. It also compares various techniques used to improve isolation between antennas and gain for MIMO DRA while maintaining a compact size. In this paper, we have compared different 5G antennas available in the literature based on parameters like isolation, gain, radiation efficiency, and envelope correlation coefficient (ECC).

MIMO (Multiple input–Multiple Output) technology is a promising and upcoming technology for the future wireless generation system, as it has got high throughput and diversity gain. MIMO makes antennas work smarter by enabling them to combine data streams arriving from different paths and at different times to effectively increase receiver signal-capturing power. MIMO is used in modern wireless standards, including in IEEE 802.11, 3GPP LTE, and mobile WiMAX systems.Sphere decoding algorithm is a lattice reduction algorithm introduced for low complexity MIMO detection SD is a powerful and attractive tool to achieve a high performance close to the Maximum Likelihood (ML),Zero Forcing (ZF) but with less complexity. This paper presents a simulation study on the sphere decoding method and compares the performance with other classical MIMO scheme such as VBLAST and Zero forcing receivers. The simulation tool used is MATLAB communication tool box version 7.1. The performance metric considered is Bit Error Rate (BER) and the channel is assumed to be Rayleigh fading. It is inferred that detection is possible with complexity and gives a better result in terms of Bit Error Rate when comparing general sphere decoding and MMSE.

Software defined radios (SDRs) are a promising technology to enable dynamic channel access and sharing. Multiple Input Multiple Output (MIMO) is another radio technology breakthrough for increasing wireless throughput. To obtain the full benefits brought by SDR and MIMO technologies in wireless mesh networks (WMNs), the higher layer mechanisms should exploit their capabilities in a systematic way. In this paper, we propose a Stream Controlled Multiple Access (SCMA) scheme for multi-hop multi-channel WMNs, which is responsible for scheduling links and assigning channels for data transmission and controlling MIMO operation mode in a cross-layer approach. It enables efficient spectrum access and sharing in temporal, frequency, and spatial dimensions, and adapts to varying network conditions. The evaluation results show that the proposed SCMA scheme greatly improve the network performance compared to the traditional schemes.

The V-BLAST (Vertically — layered Bell Laboratories Layered Space-Time) algorithm is a multilayer symbol detection scheme. The proposed work focuses on V-BLAST technique with a Multiple Input Multiple output (MIMO) technology followed by receiver detection techniques like Zero forcing (ZF), Minimum Mean Square Error (MMSE) with back substitution SIC (Symbol Interference Cancellation). The proposed methodology adopts BPSK, QAM modulation methods. The numerical analysis is conducted using MATLAB. The performance improvement is also significant and Simulation results shows that V-BLAST achieves better Bit error rates.

The free space optical (FSO) communication system has attracted many researchers from different countries, owning to its advantages such as high security, high speed and anti-interference. Among all kinds of the channels of the FSO communication system, the atmosphere channel is very difficult to deal with for two typical disadvantages at least. The one is the scintillation of the optical carrier intensity caused by the atmosphere turbulence and the other is the multipath effect by the optical scattering. A lot of studies have shown that the MIMO (Multiple Input Multiple Output) technology can overcome the scintillation of the optical carrier through the atmosphere effectively. So the background of this paper is a MIMO system which includes multiple optical transmitting antennas and multiple optical receiving antennas. A number of particles such as hazes, water droplets and aerosols exit in the atmosphere widely. When optical carrier meets these particles, the scattering phenomenon is inevitable, which leads to the multipath effect. As a result, a optical pulse transmitted by the optical transmitter becomes wider, to some extent, when it gets to the optical receiver due to the multipath effect. If the information transmission rate is quite low, there is less relationship between the multipath effect and the bit error rate (BER) of the communication system. Once the information transmission rate increases to a high level, the multipath effect will produce the problem called inter symbol inference (ISI) seriously and the bit error rate will increase severely. In order to take the advantage of the FSO communication system, the inter symbol inference problem must be solved. So it is necessary to use the channel equalization technology. This paper aims at deciding a equalizer and designing suitable equalization algorithm for a MIMO free space optical communication system to overcome the serious problem of bit error rate. The reliability and the efficiency of communication are two important indexes. For a MIMO communication system, there are two typical equalization methods. The first method, every receiving antenna has an independent equalizer without the information derived from the other receiving antennas. The second, the information derived from all of the receiving antennas mixes with each other, according to some definite rules, which is called space-time equalization. The former is discussed in this paper. The equalization algorithm concludes training mode and non training mode. The training mode needs training codes transmitted by the transmitter during the whole communication process and this mode reduces the communication efficiency more or less. In order to improve the communication efficiency, the blind equalization algorithm, a non training mode, is used to solve the parameter of the equalizer. In this paper, firstly, the atmosphere channel is described focusing on the scintillation and multipath effect of the optical carrier. Then, the structure of a equalizer of MIMO free space optical communication system is introduced. In the next part of this paper, the principle of the blind equalization algorithm is introduced. In addition, the simulation results are showed. In the end of this paper, the conclusions and the future work are discussed.

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.