Optimal Bit Loading for MIMO Systems with Decision Feedback Detection

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.

A systematic approach to decision feedback multiuser detection is introduced for the joint detection of symbols of K simultaneously transmitting users of a synchronous correlated waveform multiple-access (CWMA) channel with Gaussian noise. A new performance criterion called symmetric energy is defined which is a low-noise indicator of the joint error rate that at least one user is detected erroneously. Even the best linear detectors can perform poorly in terms of symmetric energy compared to the maximum-likelihood detector. A general class of decision feedback detectors (DFDs) is defined with O(K) implementational complexity per user. The symmetric energy of arbitrary DFD and bounds on their asymptotic effective energy (AEE) performance are obtained along with an exact bit-error rate and AEE analysis for the decorrelating DFD. The optimum DFD that maximizes symmetric energy is obtained. Each one of the K! optimum, decorrelating, and conventional DFDs, that correspond to the K! orders in which the users can be detected, are shown to outperform the linear optimum, decorrelating, and conventional detectors, respectively, in terms of symmetric energy. Moreover, algorithms are obtained for determining the choice of order of detection for the three DFDs which guarantee that they uniformly (user-wise) outperform their linear counterparts. In addition to optimality in symmetric energy, it is also shown that under certain conditions, the optimum DFD achieves the AEE performance of the exponentially complex maximum-likelihood detector for all users simultaneously. None of the results of this paper make the perfect feedback assumption. The implications of our work on power control for multiuser detection are also discussed.

Multiple-input multiple-output (MIMO) digital communication systems have received great attention due to their potential of increasing the overall system throughput. In such systems, MIMO decision feedback equalization (DFE) schemes are often used to mitigate intersymbol interference (ISI) resulting from channel multipath propagation. In this context, the existing computationally efficient methods for exact estimation of the DFE filters under minimum mean-square-error criteria (MMSE-DFE) rely on fast Cholesky decomposition and backsubstitution or Levinson techniques. These methods may still present several difficulties in implementation as the demand on higher transmission rates increases, and thus a simple solution is necessary. In this paper, new procedures for fast computation of the MIMO-MMSE-DFE are presented. The new algorithms are obtained from a simple observation, namely, that the optimal feedforward filter (FFF) is related to the well-known Kalman gain matrix, commonly encountered in fast recursive least squares adaptive algorithms-for which fast recursions exist and are readily applicable. Moreover, the feedback filter can be easily computed via stable fast MIMO convolution techniques. As a result, the proposed method is less complex, more structured, and can be as reliable in finite precision as known approaches in the literature.

In multiple-input multiple-output (MIMO) communication systems, complex matrix inversion is a very computationally demanding operation. Especially when the number of antennas increases, i.e., in a massive MIMO system, the complexity of matrix inversion becomes very high. Motivated by this observation, a new low complexity complex matrix inversion method, called SDF-SGR (Square root and Division Free Squared Givens' Rotations) based algorithm, is designed for MIMO channels. Square root operation is avoided in the whole algorithm, and division operation is replaced by shift operation during the Givens' rotations phase. Besides, since the scale factor z, involved in the traditional SGR, has little influence on the whole algorithm, it is removed in this SDF-SGR algorithm. Considering a 4×4 complex matrix inversion, the SDF-SGR based algorithm could reduce the multiplication operations by 13.07% compared with the traditional SGR algorithm, and the division operation is reduced by almost 52.94%.

For channel estimation in the relay-assisted multiple-input multiple-output and orthogonal frequency division multiplexing indoor visible light communication (VLC) system affected by light source, there is a problem that the received signal cannot be separated by simple filtering. To solve this problem, a channel estimation by PARATUCK2-PARAFAC-based noise compensation which introduces tensors to construct the suitable model of VLC is proposed. First, the transmitting pilots for channel estimation and noise estimation are constructed separately with the Hermitian symmetry structure. Then the tensor-based transmission model of the indoor VLC system is established, and the signals for channel and noise estimations are received. Finally, an asymptotic estimation for separating signals from different sources and noise is proposed by PARATUCK2-PARAFAC. Simulation results show that the proposed algorithm provides a more effective scheme for channel estimation comparing with the state-of-the-art algorithms.

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.

This paper considers large multiple-input multiple-output (MIMO) communication systems with linear precoding and linear minimum mean-squared error (LMMSE) equalization based on the iterative conjugate gradient (CG) algorithm. Convergence of the CG algorithm is fast when the eigenvalues of the received signal's covariance matrix are clustered, suggesting that mean-squared error and receiver complexity can be managed with judicious precoder design. In order to accelerate convergence of an iterative CG receiver, we incorporate constraints on two measures of eigenvalue clustering into the precoder design. Closed-form solutions to the optimal precoders are derived using majorization theory and convex optimization techniques. We show that if there are constraints on receiver complexity, the proposed precoders can improve performance for large MIMO systems operating over slowly time-varying fading channels.

Multiple-input multiple-output (MIMO) wireless communication systems can achieve higher throughput through interference alignment. For a small number of users, determining the maximum possible degrees of freedom as well as the feasibility of interference alignment in MIMO systems is well studied. However, the issues of scheduling in systems employing interference alignment and serving a large number of users have received little attention so far. In this paper, we study the problem of joint scheduling, interference alignment, and packet admission control in MIMO wireless systems with the goal of maximizing system throughput subject to stability constraints. We formulate a stochastic network optimization problem and propose a scheduling and interference alignment (SIA) algorithm. In each time slot, SIA schedules some users among many competing ones to transmit data, and determines encoding and decoding matrices for the selected users. Packet admission control is performed in each time slot. In addition, we propose a heuristic semi-distributed algorithm (SDSIA), which has a lower computational complexity than the SIA algorithm. Via simulation, we evaluate the performance of SIA and SDSIA for different algorithm parameters and different numbers of users. We also compare the performance of SDSIA with other approaches which do not simultaneously exploit interference alignment and scheduling and find that the combination of these two techniques increases the achievable data rate dramatically.

Traditional receiver algorithms developed for multiple-input multiple-output (MIMO) wireless communication systems are based on the assumption that the channel state information (CSI) is perfectly known at the receiver. However, in real environments the exact CSI is unavailable. In this paper, we address the problem of robustness of multiuser space-time block coded MIMO systems against imperfect CSI and develop a simple linear receiver which guarantees robustness against CSI errors with a certain selected probability. The proposed receiver is formulated via a probability-constrained optimization problem which is further relaxed using Chebyshev inequality to a much simpler second-order cone programming (SOCP) problem. Simulations demonstrate that the proposed receiver has a moderate performance degradation as compared to the exact solution to the original probability-constrained optimization problem, while offering much simpler implementation.

We present a hybrid soft detector that has a good performance/complexity trade-off for a multiple-input multiple-output (MIMO) wireless communication system with known channel information. The new soft detector combines the merits of a simple unstructured least-squares (LS)-based soft detector and a list sphere decoder (LSD)-based soft detector for data bit detection. The former is computationally much more efficient than the latter at the cost of poorer performance. The poor performance of the former occurs mainly when the channel matrix is ill-conditioned. Whenever this happens, we use the LSD-based soft detector in the hybrid soft detector; otherwise, we use the LS-based one. Moreover, we provide a tight radius for a sphere decoder, a hard detector, via using the output of an LS-based hard detector. These two hard detectors are needed to determine if LS or LSD should be used in the hybrid soft detector. As an application example, we consider doubling the maximum data rate of the IEEE 802.11a conformable wireless local area networks by a MIMO system with two transmit and two receive antennas. For this application, the new soft detector is about 10 times faster than the LSD-based one and is about 10 times slower than the LS-based one. Yet the packet error rate due to using the new soft detector is quite close to that of using the LSD-based one.

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.

An important transmitter adaptation technique used in multiple-input multiple-output (MIMO) communication systems is precoding based on the dominant right-singular vectors of the channel matrix. These vectors are typically estimated at the receiver, quantized, and fed-back to the transmitter via a low-rate feedback channel. We present a method for designing an efficient low-rate quantizer for the dominant right singular vectors of a MIMO channel matrix, which exploits the memory in the slow-varying channel fading process. A memory-based (recursive) quantizer is considered which quantizes the time-trajectory of points representing the channel subs-spaces on a Grassmannian manifold. The quantizer codebook is optimized with respect to an objective functions which can be directly related to MIMO channel capacity. The main contribution of this paper is the derivation of a stochastic gradient-based algorithm for codebook optimization. Numerical results obtained with channel simulations are presented, which demonstrate the MIMO channel capacity improvement achieved by using the codebooks optimized as proposed in this paper.

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 multiple-input multiple-output visible light communication (VLC) system based on disorder dispersion components is presented. Instead of monochromatic sources and large size photodetectors used in the traditional VLC systems, broadband sources with different spectra act as the transmitters and a compact imaging chip sensor accompanied by a disorder dispersion component and a calculating component serve as the receivers in the proposed system. This system has the merits of small size, more channels, simple structure, easy integration, and low cost. Simultaneously, the broadband sources are suitable to act as illumination sources for their white color. A regularized procedure is designed to solve a matrix equation for decoding the signals at the receivers. A proof-of-concept experiment using on–off keying modulation has been done to prove the feasibility of the design. The experimental results show that the signals decoded by the receivers fit well with those generated from the transmitters, but the bit error ratio is increased with the number of the signal channels. The experimental results can be further improved using a high-speed charge-coupled device, decreasing noises, and increasing the distance between the transmitters and the receivers.

Multiple-input multiple-output (MIMO) wireless communications systems have attracted a lot of attentions, because of their potential for high gain in channel capacity. Recently, attention has shifted to multiuser MIMO (MU-MIMO) systems. In the schemes for downlink MU-MIMO system, dirty paper coding (DPC) and block diagonalization (BD) algorithms can provide higher sum rate capacity of MU-MIMO systems; however, exhaustive user scheduling for them may cause prohibitive computation complexity. The greedy scheduling algorithms were proposed for DPC and for BD; and proportionally fair (PF) scheduling for MU-MIMO was also discussed in the previous works. However, the discussions in where mainly based on the system model with equal average signal to noise power ratio (SNR) users. In this paper, we focus on the user scheduling for the downlink of MU-MIMO systems with DPC transmission scheme, where each user is assumed to have only one receive antenna, which will simplify the analysis and is expected to give more insight of PF scheduling. We also assume each individual user has different average transmit SNR in the system. We modify the conventional user scheduling algorithm for DPC to modified Gram-Schmidt orthogonalization (M-GSO) based algorithm and extend it into PF scheduling. We also propose a modified PF metric corresponding to the unequal SNR users system model, that increases the sum rate capacity of MU-MIMO system.