On the MIMO capacity with residual transceiver hardware impairments

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.

Large-scale multiple-input multiple-output (MIMO) communication systems can bring substantial improvement in spectral efficiency and/or energy efficiency, due to the excessive degrees-of-freedom and huge array gain. However, large-scale MIMO is expected to deploy lower-cost radio frequency (RF) components, which are particularly prone to hardware impairments. Unfortunately, compensation schemes are not able to remove the impact of hardware impairments completely, such that a certain amount of residual impairments always exists. In this paper, we investigate the impact of residual transmit RF impairments (RTRI) on the spectral and energy efficiency of training-based point-to-point large-scale MIMO systems, and seek to determine the optimal training length and number of antennas which maximize the energy efficiency. We derive deterministic equivalents of the signal-to-noise-and-interference ratio (SINR) with zero-forcing (ZF) receivers, as well as the corresponding spectral and energy efficiency, which are shown to be accurate even for small number of antennas. Through an iterative sequential optimization, we find that the optimal training length of systems with RTRI can be smaller compared to ideal hardware systems in the moderate SNR regime, while larger in the high SNR regime. Moreover, it is observed that RTRI can significantly decrease the optimal number of transmit and receive antennas.

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) 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.

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.

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.

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.

Large‐scale multiple‐input multiple‐output (MIMO) system is considered one of promising technologies to realise next‐generation wireless communication system (5G). So far, channel estimation problem is a big obstacle to develop large‐scale MIMO system design due to high computational complexity and curse of dimensionality, which are caused by the long delay spread as well as a large number of antennas. Hence, devising any low‐complexity channel estimation method could promote the successful development of the large‐scale MIMO system. Due to the fact that, large‐scale MIMO channels often exhibit sparse or/and cluster‐sparse structure, in this study, the authors propose an effective low‐complexity large‐scale MIMO channel estimation method by using affine combination of sparse adaptive filtering filters. First, problem formulation and standard affine combination of adaptive least mean square (LMS) filters are introduced. Then they propose an effective affine combination method with two sparse LMS filters and design an approximate optimum affine combiner according to stochastic gradient search method. Later, to verify the proposed algorithm for large‐scale MIMO channel estimation, both theoretical analysis and numerical simulations are provided to confirm effectiveness of the proposed algorithm which can achieve better estimation performance than the traditional methods.

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.

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 communication using multiple-input multiple-output (MIMO) systems enables increased spectral efficiency for a given total transmit power. MIMO capacity is a strong function of the singular value distribution of the channel matrix, the receive antenna to transmit antenna transfer matrix. In this paper the capacity is calculated using an asymptotic large random matrix approach. Bounds on the maximum spectral efficiency of MIMO systems in which both transmitter and receiver know the channel (channel estimate feedback), are compared with MIMO systems in which only the receiver knows the channel. In addition, MIMO performance in the presence of cooperative and uncooperative interfering MIMO systems is presented.

The main limitation of using a multiple antenna architecture is the complexity and high cost of the hardware in the radio frequency (RF) section, which rise with an increase in the number of antennas. In addition, RF circuit mismatches [1-3] and coupling [4] grow with an increase in the number of antennas; consequently, these factors also limit the use of high numbers of antennas at the transceiver.One solution to compensate for the extra hardware cost and RF circuit imperfection is the utilization of a single RF front-end in a multiple input multiple output (MIMO) system, where a single RF path is used instead of multiple parallel RF paths. This results in an RF section that has lower complexity and cost, a simpler RF design, a compact size, and lower power consumption.A conventional MIMO receiver with N antennas at the receiver is shown in Figure 10.1. The receiver uses multiple parallel RF front-ends, where the number of RF front-ends is equal to the number of antennas. In this architecture, the baseband processing section decodes N received baseband paths to recover the signal and obtain the diversity or spatial multiplexing gain. To realize a single RF frontend path, orthogonal transmission of multiple RF streams over a single front-end must be recognized. In the following subsections, the different techniques to realize a single RF front-end in a MIMO system are discussed.KeywordsRadio FrequencyAntenna SelectionRadio Frequency CircuitRadio Frequency TransceiverSingle Radio FrequencyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Motivated by the study of the optimization of the quality of service for multiple input multiple output (MIMO) technique in a wireless communication system. The capacity of MIMO channel in independent Rayleigh channels grows linearly as the number of antennas. However, some limitations on the MIMO capacity is due to the correlation between individual sub-channels of the matrix channel. In this paper, we investigate the MIMO channel capacity in correlated channels using a new correlation matrix model. After some mathematical recalls we derive the general upper bound on the MIMO channel capacity. We give the analytical results, which measure the effect of correlation on the MIMO capacity for a proposal model. Then, we use the correlation matrix approach to compute the capacity of MIMO with respect to signal-to-noise (snr) and then predict the numbers of antennas. By fixing the snr variable to a specific value, we extract informations on the optimal numbers of MIMO antennas. Finally, we have given the variation of the MIMO capacity in the limiting case of N rarr infin for our proposal model and the exponential correlation matrix model.

Consider a multiple input multiple output (MIMO) system with pulse amplitude modulation (PAM), where each transmit antenna is allowed to choose a different transmission delay. (We refer to this system as a MIMO system with asynchronous PAM.) Such MIMO systems have been studied before for increasing diversity and avoiding unintentional beamforming. We quantify the capacity/outage capacity improvement due to asynchronism and the corresponding spectral leakage (if any) for MIMO point-to-point and MIMO broadcast channels. We show that under commonly used transmitter spectral mask constraints, the channel has unbounded degrees of freedom. Synchronous PAM exploits only a finite number of these degrees of freedom, and asynchronism helps to improve capacity. For most cases of interest, two distinct shifts attain most of the capacity improvement. These gains are robust to interference from adjacent carriers. Analysis of the spectrum of the asynchronous PAM suggests that optimal water-filling based schemes are useful for improving capacity only in directional links due to possible spectral leakage, while uniform power allocation leads to capacity improvement without any extra spectral leakage.

The scaling up of antennae and terminals in large-scale multiple-input multiple-output (MIMO) systems helps increase the spectral efficiency at the penalty of prohibitive computational complexity. In conventional linear detection such as the minimum mean square error (MMSE) signal detection, the high complexity is mainly caused by solving the inversion of random matrix, especially in large-scale MIMO systems. In order to reduce the complexity of the matrix inversion, we proposed a low-complexity MMSE detection scheme based on symmetric successive over relaxation (SSOR) method, referred to as MSSD method. The proposed method exploits the channel hardening phenomenon, which means the off-diagonal terms of the HHH matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix H increases. In addition, we point out that the channel estimation scheme used in channel hardening-exploiting message passing receiver (CHEMP) is very suitable for the MSSD algorithm. For the considered large MIMO settings, simulation results show that the performance of the MSSD algorithm is very close to the classical MMSE detection algorithm with a small number of iterations.