Adaptive Filtering-Based Iterative Channel Estimation for MIMO Wireless Communications

Multiple transmit-receive antenna based wireless communication system is a well investigated area in the literature. However, within the scope of author’s knowledge there are few analytical frameworks which facilitate communication system design engineers to select the required transmit-receive antenna combination for a given quality of service (QoS) in terms of Bit Error Rate (BER) or Symbol Error Rate (SER). Most of such frameworks for Multiple-Input Multiple-Output (MIMO) wireless systems in the existing literature are lacking tightness between theoretical approximation and actual simulation results. The aim of investigation in this study is to provide theoretical expectation of receiver performance with MIMO wireless communication system within a given channel. In this study, new analytical performance bound frameworks with tighter lower bounds have been presented for different receivers for MIMO systems. On the basis of the presented analytical framework, a simulation platform has been established. The channel state information as well as expected quality of service is assumed to be known for simplicity. Rayleigh fading channel has been considered within simulation platform. Simulation results show that the proposed frameworks provide degree of tightness up to 100 times at Signal to Noise Ratio (SNR) 20 dB for (2x2) MIMO system in comparison to the existing frameworks. Few other MIMO antenna combinations have been considered in the evaluation process of the proposed framework.

Multipath fading is one of the primary factors for degrading the performance in a wireless network. Information theoretic and past research suggest the use various diversity techniques to combat fading in wireless networks. Antenna diversity, a form of diversity technique, when incorporated in a wireless transceiver increases the system capacity and is one of the effective methods to combat fading in wireless systems. Also, recent research by Laneman et.al., Sendonaris et.al. suggests that cooperation among users in a wireless networks is an effective approach for a better signal reception in multipath fading environments. The diversity gains obtained by cooperation among the users of a wireless network is termed as cooperative diversity . Although, prior research in cooperative diversity considers users equipped with single antenna, in practical scenarios users may be able to accommodate multiple antennas due to the recent advanced research in semiconductor industry. Hence, the primary purpose of this thesis is to design, simulate and analyze an end-end performance of multi-antenna wireless systems employing cooperative multi antenna relay nodes so as to exploit the cooperative diversity and antenna diversity simultaneously in a wireless networks. Three main contributions to the area of cooperative multiple-input multiple-output (MIMO) wireless systems is presented in this thesis. First, we perform information theoretic analysis to study the impact of antenna arrays on cooperative wireless networks and propose the best possible distribution of antenna arrays among the three terminals of a simple three terminal cooperative relay network. Second, we design, simulate, and analyze a cooperative multiple-input multiple-output (MIMO) wireless systems employing orthogonal space-time block codes as proposed by Alamouti in 1998 with a decode-and-forward (DF) relay terminal. We implement a maximal ratio combining receiver that provides almost twice the diversity gain with respect to point-point multiple input multiple output link. Finally, we implement a practical receiver for cooperative reception using multiple antennas at all nodes based on Bell-Labs Layered Space Time architecture (BLAST). We incorporate a practical adaptive decode-and-forward (DF) relaying technique for reliable signal retransmission for both Alamouti space-time coding and the BLAST schemes. Results presented in terms of bit error rates and throughput show that remarkable performance gains are achievable by combining the concepts drawn from space-time coding, cooperative relaying and array processing.

The use of multiple antennas at both ends of a wireless link such as multiple-input multiple-output (MIMO) systems are now expected to hold the potential to drastically improve the spectral efficiency and link reliability in the future wireless communications. This is due to the fact that a MIMO channel can offer a capacity gain over a traditional single-input single-output (SISO) channel. These spectral efficiency gains often require accurate knowledge of the channel at the receiver, and some times at the transmitter as well. In this paper, we present the concept and discuss some ongoing research directions on the channel capacity of a MIMO wireless system. Preliminary simulations show linear increasing results in spectral efficiency of MIMO with an arbitrary number of transmit or receive antennas.

This paper investigates the capacity of multiple-input multiple-output (MIMO) wireless systems when instantaneous channel state information (CSI) is available at both the transmitter and the receiver in a line-of-sight Rician fading environment. Specifically, an infinite series representation for the ergodic capacity of uncorrelated Rician fading MIMO channels is derived, assuming both transmitter and receiver CSI and a specular component of arbitrary rank. The ergodic capacity and its associated outage probability are expressed in terms of a cutoff level capturing the optimal eigen-mode power and rate adaptation. Moreover, an equation from which the cutoff value can be solved for numerically is derived for arbitrary numbers of transmit and receive antennas.

Recently, technological developments have enhanced, the use of Millimeter-wave (mm-Wave) Multiple Input Multiple Output (MIMO) system in various communication applications and wireless sensor networks as channel estimation efficiency can be immensely improved with the help of this technological developments in Millimeter-wave MIMO system and wireless sensor network as well. Moreover, they can improve quality of communication services to a great extent. However, cell interference in Millimeter-wave (mm-Wave) MIMO system can produce a massive impact on spectral efficiency. Therefore, a Routing Enabled Channel Estimation (RECE) technique is presented in this article to minimize interference between cells. The proposed Channel Estimation technique improves channel capacity as well as spectral efficiency. Moreover, Normalized Mean Square Error (NMSE) is minimized heavily using proposed RECE technique. Here, main aim of this article is to reduce cell interference and channel estimation inside a cell by using route selection, beam selection, and spatial frequency estimation. Here, different scenarios and parameters are considered to evaluate performance efficiency of proposed RECE technique in terms of spectral efficiency, NMSE and SNR and compared against varied traditional channel estimation techniques. Moreover, it is clearly evident from performance results that the proposed channel estimation technique performs better than the other two methods.

This paper considers a robust FIR precoder design problem for frequency-selective multiple-input multiple-output (MIMO) wireless systems under transmit power constraints with imperfect channel knowledge at the transmitter. The robust design problem requires finding the optimal power constrained FIR precoder for the worst-case channel model within a neighborhood of the estimated channel. The neighborhood is formed by placing a bound on Kullback-Leibler (KL) divergence between the actual and estimated channels. We show that the optimal power constrained FIR precoder designed for the estimated channel under the assumption of perfect channel knowledge is robust when channel uncertainties are measured by the KL divergence metric.

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.

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.

After a few decades’ evolution of wireless communication systems, to ensure reliable highspeed communication over unreliable wireless channels is still one of the major challenges facing researchers and engineers. The use of multiple antennas at transmitter and receiver, known as multiple-input multiple-output (MIMO) communications, is one promising technology delivering desired wireless services. The main goal of this thesis is to study two important issues in wireless MIMO communication systems: receiver design for coded MIMO systems, and diversity-multiplexing tradeoff analysis in general fading channels. In the first part of this thesis, we decompose the receiver design problem into two subproblems: MIMO channel estimation and MIMO detection. For the MIMO channel estimation, we develop an expectation-maximization (EM) based semi-blind channel and noise covariance matrix estimation algorithm for space-time coding systems under spatially correlated noise. By incorporating the proposed channel estimator into the iterative receiver structure, both the channel estimation and the error-control decoding are improved significantly. We also derive the modified Cramer-Rao bounds (MCRB) for the unknown parameters as the channel estimation performance metric, and demonstrate that the proposed channel estimation algorithm can achieve the MCRB after several iterations. For the MIMO detection, we propose a novel lowcomplexity MIMO detection algorithm, which has only cubic order computational complexity, but with near-optimal performance. For a 4×4 turbo-coded system, we show that the proposed detector had the same performance as the maximum a posteriori (MAP) detector for BPSK modulation, and 0.1 dB advantage over the approximated MAP detector (list sphere decoding algorithm) for 16-QAM modulation at BER = 10−4. In the second part of this thesis, we derive the optimal diversity-multiplexing tradeoff for

This paper discusses radiation pattern selection in multiple-input multiple-output (MIMO) wireless systems. Each receive antenna can be reconfigured to P distinct radiation patterns. We select radiation patterns in order to maximize instantaneous channel capacity. And we prove that for arbitrary correlation the maximum achievable diversity order of the pattern selection MIMO (PS-MIMO) system equals to the rank of the total correlation matrix of its full system so that the link reliability can be significantly improved. We propose a fast selection algorithm as the suboptimal solution for channel capacity maximization. For PS-MIMO systems over spatially correlated channels, we propose to use only a part of available radiation patterns based on the estimation of the correlation before performing the pattern search. Simulations are used to validate the theoretical results and illustrate the performances of proposed algorithms.

Information theoretic limits for random key generation in multiple-input multiple-output (MIMO) wireless systems exhibiting a reciprocal channel response are investigated experimentally with a new three-node MIMO measurement campaign. As background, simple expressions are presented for the number of available key bits, as well as the number of bits that are secure from a close eavesdropper. Two methods for generating secret keys are analyzed in the context of MIMO channels and their mismatch rate and efficiency are derived. A new wideband indoor MIMO measurement campaign in the 2.51- to 2.59-GHz band is presented, whose purpose is to study the number of available key bits in both line-of-sight and nonline-of-sight environments. Application of the key generation methods to measured propagation channels indicates key generation rates that can be obtained in practice for four-element arrays.

In modern wireless communication systems, accurate characterization of wireless propagation channels remains a significant challenge. In multiple-input multiple-output (MIMO) wireless systems, a double-directional channel can be achieved by utilizing multipath components' spatial and temporal properties (MPCs). Grouping the MPC can simplify the parameters as a trade-off between complexity and accuracy. This paper implements k-Deep Auto Encoder (k-DAE), AE+k-means, and K Power Means (KPM) clustering approaches and compares their performance in clustering wireless propagation multipaths in indoor and outdoor scenarios. The results show that AE+k-means performs better than k-DAE in indoor scenarios by 25.48%, while k-DAE performs 24.60 % better in outdoor scenarios. The KPM algorithm performs best in all indoor scenarios among the three algorithms, with a significant increase of 4.38% and 11.062% to AE+k-means and k-DAE, respectively. However, both k-DAE and AE+k-means have quite similar performance in outdoor scenarios. The study also highlights the first use of autoencoders in clustering the MPCs. The results indicate that k-DAE can be used as an alternative clustering method in channel modeling. Future works envisioned applying the approach to other wireless channel models.

This paper describes investigations into the antenna mutual coupling (MC) effect on channel estimation and capacity of a multiple-input multiple-output (MIMO) wireless communication system. The presented investigations close the gap existing in the previous works which assessed the effect of mutual coupling on MIMO capacity under the assumption of availability of perfect channel state information (CSI) at the receiver. The new approach assumes that the perfect CSI is not available due to channel estimation errors. The investigations are carried out for different spacing between array antenna elements producing a varying effect of mutual coupling on the channel estimation and the resulting MIMO channel capacity.

Multiple Input Multiple Output(MIMO) has been in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel with modified precoder and decoder has been considered when the channel state information (CSI) is considered partial. Due to delay in acquiring transmitted information at the receiver end, the time selective fading wireless channel often induces incomplete or partial CSI. The dynamic CSI model has also been implemented consisting channel mean and covariance which leads to extracting of channel estimates and error covariance which then further applied with the modified precoder and decoder utilizing both the parameters indicating the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partially or fully blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI which has been manipulated according to the precoder and decoder conditions. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new approach which maximizes the capacity of spatial channel model with modified precoder and decoder has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach, where by utilizing the detected symbols from the previous iteration, multiuser/MIMO channel estimation and symbol detection is improved.

Spatial modulation (SM) and space shift keying (SSK) represent transmission methods for low-complexity implementation of multiple-input multiple-output (MIMO) wireless systems in which antenna indices are employed for data transmission. In this paper, we focus our attention on the secrecy behavior of SSK and SM. Using an information-theoretic framework, we derive expressions for the mutual information and consequently compute achievable secrecy rates for SSK and SM via numerical evaluations. We also characterize the secrecy behavior of SSK by showing the effects of increasing the number of antennas at the transmitter as well as the number of antennas at the legitimate receiver and the eavesdropper. We further evaluate the secrecy rates achieved by SM with different sizes of the underlying signal constellation and compare the secrecy performance of this scheme with those of general MIMO and SIMO systems. The proposed framework unveils that SM is capable of achieving higher secrecy rates than the conventional single-antenna transmission schemes. However, it underperfoms compared to a general MIMO system in terms of the achievable secrecy rates.