Linear Precoding for MIMO Systems with Low-Complexity Receivers

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.

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.

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.

A near maximum likelihood performance modified firefly algorithm for large MIMO detection

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.

This paper studies large multiple-input multiple-output (MIMO) communication systems with linear precoding and reduced-rank Krylov subspace receivers. We design precoders and analyze their performance by exploiting large-dimensional random matrix theory. We first devise low-complexity precoding schemes that can improve performance of low-rank Krylov subspace receivers in the regime of high signal-to-noise ratio (SNR). We then introduce a potential theory-based method for analyzing the convergence behavior of the mean-squared error (MSE) for various transmission schemes. This method can be applied to a broader range of problems compared to previous analytical tools. The analysis reveals that the MSE decreases super exponentially with the rank of the receiver. Numerical examples show that the proposed precoders can outperform conventional precoders when low-rank Krylov subspace receivers are used, and that the performance of such receivers can be accurately predicted.

The present generation of wireless communications systems, to a greater extent, do not optimally utilize the scarce radio spectrum and also not flexible in the allocation of the time-frequency resource. This may also be the case with the next generation systems if a viable solution is not found. Many researchers worked on resolving these two problems but suffered some limitations in their results, tools approaches and techniques used. The main problem was that they failed to collectively utilize the advantages of both the high spectral efficiency of Filter Bank Multicarrier Modulation Offset Quadrature Amplitude Modulation (FBMC-OQAM) and the efficient capacity property of large Multiple Input Multiple Output (MIMO) systems. In this paper, we shall propose to use the advantages of both systems in order to transmit FBMC-OQAM data symbols over a large MIMO system by employing a specific Space Time Transmission Scheme (STTS) of FBMC-OQAM for large MIMO. The transmission, however, will be prior to error correction using Hadamard scheme to double the spectral efficiency by restoring complex orthogonality in the imaginary field. The STTS will eliminate imaginary intrinsic interference, thereby enabling the efficient transmission of the FBMC-OQAM data over the large MIMO system. By employing the Hadamard scheme and STTS, we propose to develop a spectrally efficient large MIMO/FBMC-OQAM system with an improved capacity gain over a conventional MIMO/FBMC-OAQM.

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.

We study the uplink transmission in multiple-input multiple-output (MIMO) systems with antenna correlation. We focus on schemes that require only channel covariance information at the transmitter (CCIT), which involves lower cost than full channel state information at the transmitter (CSIT). We start from mutual information analysis and show that a simple CCIT-based scheme, referred to as statistical water-filling (SWF), can perform close to the optimal full CSIT-based one in MIMO systems with more receive antennas than transmit ones. We then focus on the implementation of SWF in practically coded systems. An iterative linear minimum mean squared error (LMMSE) receiver is assumed and an extrinsic information transfer (EXIT) chart type curve matching technique is developed based on Hadamard precoding techniques. Simulation results show that the proposed scheme can obtain significant performance improvement compared to the conventional equal power transmission. Finally, we show that the proposed scheme is also very efficient in multi-user uplink MIMO systems with distributed channel information.

We propose a precoder design to minimize the codeword error rate of multiple-input multiple-output (MIMO) systems in the presence of channel estimation errors. Our proposed scheme only requires knowledge of the second-order statistics of the channel, noise and the estimation errors; an estimate of the instantaneous channel state information (CSI) is not required. Compared to the instantaneous CSI requirement, our assumption is more practical since obtaining an accurate CSI is challenging if the channel fluctuates rapidly, while the channel statistics are likely to remain unchanged for a much longer period. Furthermore, the feedback overhead from the receiver to the transmitter is greatly reduced. When compared to the case without transmit preceding, our proposed design achieves a significant improvement in error performance: (i) for a real 2 × 2 MIMO system with 4-PAM modulation and at codeword error rate of 104, our proposed precoder design achieves coding gains of up to 6 dB and (ii) for a real 4 × 4 MIMO system with BPSK modulation and at codeword error rate of 104, coding gains of up to 6.5 dB can be achieved.

The research work in this dissertation addresses reduced complexity equalization technique for multipath channels with large delay spread. This has been one of the key challenges for high data rate impulse radio based Ultra-Wideband (UWB) communication systems using low cost receiver design. Our research focus is on developing efficient and effective tap selection techniques for non-uniformly spaced Minimum Mean Squared Error (MMSE) equalization and applying it to UWB systems to achieve high performance with reduced receiver complexity. In this dissertation, a new class of tap selection techniques based on Order Recursive Least Squares (ORLS) and Matching Pursuit (MP) algorithms is proposed for UWB multipath channel equalization in the presence of Inter Symbol Interference (ISI) and unknown co-channel interference. The proposed tap selection techniques are directly implemented based on training symbol sequence under Least Squares (LS) criterion without the need for explicit channel estimation. In addition, Diagonal Loading (DL) technique is incorporated into the tap selection and MMSE equalization processes to insure system robustness given limited number of training symbols in the case of practical implementation. The proposed tap selection based equalizer is shown to outperform the conventional uniformly spaced linear equalizer and is with reduced computational complexity. The LS based technique proposed for equalizer tap selection is then extended to receive antenna selection for high data rate UWB systems with multiple receive antenna elements to achieve improved performance in the presence of Narrow Band Interference (NBI). Based on the LS criterion, antenna selection techniques for Multiple Input Multiple Output (MIMO) systems are also explored in our research work. Novel transmit and receive antenna selection techniques are proposed for MIMO spatial multiplexing systems with linear receivers under LS criterion. Unlike conventional approaches, xii

Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neural Inference System (ANFIS) controller and compare it with a traditional PID controller. and evaluated by objective function as integral time absolute error (ITAE). ANFIS is used to train fuzzy logic systems according to the hybrid learning algorithm. The training involves the fuzzy logic parameters through simulating the validation data to represent a model to know the correctness and effectiveness of the system. It is optimizes the system performance in real time, however, to avoid potential problems such as easy local optimality. In the proposed approach stability is guaranteed as the initial steady-state scheme. ITAE is combined with ANFIS to minimize the steady-state transient time responses between the high-order initial pattern and unit amplitude response. The proposed ANFIS self-tuning controller is evaluated by comparing with the conventional PID. MATLAB simulink is used to illustrate the results and demonstrate the possibility of adopting ANFIS controller. The simulation results showed that the performance of ANFIS controller is better than the PID controller in terms of settling time, undershoot and overshoot time.

In this thesis we investigate the effects of the physical constraints such as antenna aperture size, antenna geometry and non-isotropic scattering distribution parameters (angle of arrival/departure and angular spread) on the performance of coherent and non-coherent space-time coded wireless communication systems. First, we derive analytical expressions for the exact pairwise error probability (PEP) and PEP upper-bound of coherent and non-coherent space-time coded systems operating over spatially correlated fading channels using a moment-generating function-based approach. These analytical expressions account for antenna spacing, antenna geometries and scattering distribution models. Using these new PEP expressions, the degree of the effect of antenna spacing, antenna geometry and angular spread is quantified on the diversity advantage (robustness) given by a space-time code. It is shown that the number of antennas that can be employed in a fixed antenna aperture without diminishing the diversity advantage of a space-time code is determined by the size of the antenna aperture, antenna geometry and the richness of the scattering environment. In realistic channel environments the performance of space-time coded multipleinput multiple output (MIMO) systems is significantly reduced due to non-ideal antenna placement and non-isotropic scattering. In this thesis, by exploiting the spatial dimension of a MIMO channel we introduce the novel use of linear spatial precoding (or power-loading) based on fixed and known parameters of MIMO channels to ameliorate the effects of non-ideal antenna placement on the performance of coherent and non-coherent space-time codes. The spatial precoder virtually arranges the antennas into an optimal configuration so that the spatial correlation between all antenna elements is minimum. With this design, the precoder is fixed for fixed antenna placement and the transmitter does not require any feedback of channel state information (partial or full) from the receiver. We also derive precoding schemes to exploit non-isotropic scattering distribution parameters of the scattering channel to improve the performance of space-time codes applied on MIMO systems in non-isotropic scattering environments. However, these schemes

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.

In this paper, the detection-aided ordering schemes are proposed for linear minimum mean-squared-error (LMMSE) iterative soft interference cancellation (ISIC) in multiple-input multiple-output (MIMO) systems. Unlike the conventional LMMSE-ISIC ordering schemes that utilize the channel state information (CSI) only, the proposed ordering schemes utilize the receive signal vector and CSI for the ordering procedure. Then, for each candidate symbol, the sum of the likelihoods except the largest likelihood is calculated to estimate the reliability of the candidate symbol, where the likelihoods are calculated by the LMMSE or LMMSE-ISIC detection-aided ordering procedure. Thus, the proposed ordering schemes can provide a significantly more accurate ordering result than the conventional ordering schemes. As the detection-aided ordering schemes, non-iterative and iterative ordering schemes are proposed, and the constrained iterative ordering scheme is also proposed to resolve the high computational complexity of the original iterative ordering scheme. Numerical simulation results verify that the proposed detection-aided ordering schemes outperform the conventional ordering schemes in terms of convergence speed and error performance.