Capacity of MIMO systems with asynchronous PAM

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.

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

Everyone in the world always wants to use a better wireless network which provide high capacity and high data rates. High data rates and capacity can be produce by Multiple Input Multiple Output(MIMO) systems. This paper mainly focused on MIMO systems because the MIMO systems have multiple number of antennas in transmitting and receiving end which can produce more data rates and capacity compared to SISO, SIMO, MISO systems. The multiple antennas allow MIMO systems to produce multiplexing gain and diversity gain. The capacity of MIMO systems will be increased by increased the number of antennas in transmitter and receiver end.

Wireless communications using multiple input multiple output (MIMO) systems enable increased spectral efficiency for a given total transmit power. The increased capacity is achieved through the introduction of additional spatial channels (space-time coding). In this paper, MIMO capacity is calculated as a function of environmental factors, including channel complexity, external interference and channel estimation error. The capacity of MIMO systems, where both the transmitter and receiver know the channel (channel estimate feedback), is compared with single input multiple output (SIMO) and MIMO systems, where only the receiver knows the channel. The channel complexity is studied using a simple statistical physical scattering model. Finally, an expression for capacity loss particular to channel estimation error at the transmitter is introduced.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

The capacity of multiple-input multiple-output (MIMO) wireless communication systems over spatially correlated Rayleigh distributed flat fading channels with complex Gaussian additive noise is investigated. Specifically, the probability density function of the mutual information between transmitted and received complex signals of MIMO systems is derived. Using this density the closed-form ergodic capacity (mean), delay-limited capacity, capacity variance and outage capacity formulas for spatially correlated channels are derived and then these formulas are evaluated numerically. Numerical results show how the channel correlation degrades the capacity of MIMO communication systems. It is also shown that the density of mutual information of correlated/uncorrelated MIMO systems can be approximated by a Gaussian density with derived mean and variance, even for a finite number of inputs and outputs.

Model reference robust control (MRRC) is derived for MIMO systems that have a right Hermite normal form which is SPR and diagonal, and then for systems whose right Hermite normal form is diagonal but not SPR. It is shown that MRRC of multiple input multiple output (MIMO) systems is an extension of model reference control (MRC) of MIMO systems and MRRC of SISO systems. Unwanted coupling exists in many physical MIMO systems. The recursive backstepping procedure used in non-SPR SISO systems cannot be directly applied to diagonal MIMO non-SPR systems without the introduction of the augmented matrix or a precompensator. It is shown that MRRC decouples MIMO systems using only input and output measurements rather than state feedback.

Orbital Angular Momentum (OAM) has been considered as a new dimension in wireless transmission. In Multiple Input Multiple Output (MIMO) system, OAM can also be utilized as the new Degrees of Freedom (DoF) to manipulate the beamforming instead of the traditional Channel State Information (CSI) feedback, so that the computation complexity of the beamforming dramatically decreases, as well as the capacity of the transmission is maintained approaching the upper bound of the MIMO system. For example, this beamforming manipulation with OAM is extremely efficient in the Line of Sight (LoS) MIMO system. In this paper, the mathematical model of the DoF in beamforming manipulation in MIMO with OAM is established. The computation complexity is evaluated, and compared in LoS environment within MIMO, MIMO-OAM, and OAM. The quantitatively simulation confirms the fact that MIMO-OAM has the best trade-off between the capacity and the computation complexity, which gives a consolidated evidence that the MIMOOAM scheme can be adopted as an efficient beamforming scheme in LoS backhaul links between the base station and the self backhaul small cell station, or Near Field Communications (NFC) directly between mobile terminals. Due to these promising applications, MIMO-OAM may become one of the candidate technologies in Beyond 5G (B5G) mobile communications.

Physical limitations of the wireless medium create a technical challenge for reliable wireless communication. Moreover, the need for high-speed wireless Internet has led to the demand for technologies delivering higher capacities and link reliability than achieved by current systems. Multiple -input multiple-output (MIMO) based communication systems are capable accomplishing these objectives. Multiple input multiple output (MIMO) systems take advantage of spatial diversity obtained through the spatially separated antennas in a dense multipath scattering environment. Spatial diversity can increase the gain diversity consequently increase the reliability of the wireless link. Theoretical studies indicate that the capacity of MIMO systems grows linearly with the number of transmit antennas used. In this paper Space Time Coding techniques is covered which leads to much improvement in Symbol Error Rate within the same spectrum and bandwidth. Also a comparison is made between different modulation techniques.

The research of closed form expressions for the pdf of the lth ordered eigenvalue of a Wishart matrix has received a great attention in the past years owing to its applications in the performance analysis of multiple input multiple output (MIMO) systems in fading environments. Although several closed form expressions for this pdf were obtained in the past years, to the authors' knowledge, no one was very friendly for further analysis. We propose a methodology to obtain the pdf for the lth largest eigenvalue whose expression is given as a sum of terms xβe-xδ. This expression is easily usable to obtain closed form results for the performance of many MIMO systems, such as, for instance, MIMO beamforming, and MIMO with Singular Value Decomposition (SVD). The methodology is valid for both uncorrelated and correlated central Wishart, allowing the investigation of MIMO systems with uncorrelated and correlated Rayleigh fading. © 2008 IEEE.

Wireless Sensor Network (WSN) is used in various applications. Sensors acquire samples of physical data and send them to a central node in different topologies to process the data and makes decisions. A main performance factor for WSN is the battery life that depends on energy consumption on the sensor. To reduce the energy consumption, an energy efficient transmission technique is required. Multiple Input Multiple Output (MIMO) systems showed good utilization of channel characteristics. This leads to enhance the transmission and hence reduce energy consumed by the sensor. In MIMO systems multiple signals are combined at the transmitter and transmitted using multiple antennas. This provides each receiver the whole combined signal and hence, array processing techniques helps in getting better performance. To further enhance the transmission of data, a Low Density Parity Check (LDPC) Coded MIMO wireless sensor network is proposed. The system implements space diversity through Multiple Antennas and temporal diversity through LDPC code and uses a clustering procedure to optimize the forming of the MIMO system. Results showed that, if the number of sensors is greater than the number of receiving antennas, time or frequency multiplexing is possible to keep good performance for the devised system. And by controlling the encoder we can create a temporal and spatial code among the transmitted signals enhancing the BER results in longer battery life at sensor nodes.

The fifth generation cellular communication is all about high speed internet access with devices consuming less amount of power. Millimeter wave Multiple Input Multiple Output (MIMO) systems is a promising technology that offers high data rates due to a large bandwidth. These systems are compact with minimal antenna heights which allows us to deploy these systems more widely. The mm-Wave Massive MIMO systems enhances the data rates to a large extent. But due to the increasing number of antennas to support high data rates, the power consumption of the system goes high. In this paper, a limited channel state information (CSI) based precoding technique has been used to optimize the power consumption and to reduce the computational complexity of the system. The use of Hybrid Precoder architecture with sub array of precoding blocks helps in reducing the computational complexity by many folds. The conditional switching between two stages minimizes the number of computations required. The estimation of Angle of arrival (AoA) enables the precoder to steer the beam at the transmitter for higher throughput. The smart antenna selection algorithm optimizes the power consumption of the system. The two – staged precoding algorithm proposed employs all these techniques to optimize the performance of mm-Wave MIMO systems.

This paper has proposed an adaptive antenna selection technique in multiple input multiple output (MIMO) system that optimizes the system energy efficiency to achieve maximum throughput. Though throughput increases with the dimension of MIMO system, the energy consumed per RF module is a major concern. The antenna selection technique exploits the channel state information (CSI) at transmitting end to reduce the number of RF module to achieve maximum benefit out of MIMO system. However, simultaneously achievement of best throughput and energy efficiency, with fixed number of transmitted antennas, is not possible. In this paper, the number of transmitting antenna to be selected is varied as per channel condition. The proposed technique aims to maximize the instantaneous throughput with optimum energy efficiency.

This paper reports on investigations into the effects of spatial correlation on channel estimation and capacity of a multiple input multiple output (MIMO) system. Minimum mean square error (MMSE) method is applied for channel estimation under a correlated channel scenario. Simulation results for a 4 times 4 MIMO system with the Jakes model describing a channel between a base station and a mobile station demonstrate a trade-off between the effects of spatial correlation, channel estimation and capacity. For low values of signal to noise ratio (SNR), spatial correlation decreases channel estimation errors and improves capacity. For high values of SNR, spatial correlation has a less pronounced effect on improving channel estimation and thus the MIMO capacity is adversely affected by the decreased channel matrix rank.

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