Computationally Efficient Norm-Based Capacity-Maximising MIMO Joint and Decoupled Antenna Selection Algorithms

In this work, a new antenna selection algorithm is proposed for multiple-input multiple-output (MIMO) wireless communication systems. It is assumed that the channel state information (CSI) is exactly known at the receiver, and hence the antenna selection is considered only at the receiver. The goal of antenna selection is to find a tradeoff between the maximization of the channel capacity and the minimization of the pair-wise error probability (PEP). It turns out that the scheme of antenna selection plays an important part when dealing with high antenna correlation. It is shown that our proposed scheme for a system with two-transmit antennas and five-receive antennas offers better PEP performance than the algorithm based on the maximization capacity, but has little capacity loss. The simulation results indicate that our proposed scheme can jointly meet the capacity maximization and the PEP minimization.

Knowledge of the propagation channel makes it possible for multiple-input multiple-output (MIMO) links to achieve higher system capacities by means of beamforming on the stronger channel eigenvectors. Assigning dedicated low bit rate feedback links is an efficient and practical method of providing transmitters with the required channel state information. One of the key problems in limited feedback precoding is the design of finite indexed codebooks known a priori to both the transmitter and receiver.Previous design methods include signal-to-noise ratio maximization through Grassmannian line packing, distortion minimization via vector quantization and antenna selection algorithms. In this paper we propose a new method that utilizes the dominant right singular vector of the MIMO channel to construct quantized unitary precoders. We show that although sub-optimum, this method can produce full diversity order codebooks. Using Monte-Carlo simulations, we compare the performance of the this method with optimum Grassmannian precoders and also antenna selection and subset selection 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.

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.

Recently it has been shown that it is possible to improve the performance of multiple-input multiple-output (MIMO) systems by employing a larger number of antennas than actually used and selecting the optimal subset based on the channel state information. Existing antenna selection algorithms assume perfect channel knowledge and optimize criteria such as Shannon capacity or various bounds on error rate. This paper examines MIMO antenna selection algorithms where the set of possible solutions is large and only a noisy estimate of the channel is available. In the same spirit as traditional adaptive filtering algorithms, we propose simulation based discrete stochastic optimization algorithms to adaptively select a better antenna subset using criteria such as maximum mutual information, bounds on error rate, etc. These discrete stochastic approximation algorithms are ideally suited to minimize the error rate since computing a closed form expression for the error rate is intractable. We also consider scenarios of time-varying channels for which the antenna selection algorithms can track the time-varying optimal antenna configuration. We present several numerical examples to show the fast convergence of these algorithms under various performance criteria, and also demonstrate their tracking capabilities.

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.

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.

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.

Multiple-input multiple-output (MIMO) wireless systems use antenna arrays at both the transmitter and receiver to provide communication links with substantial diversity and capacity. Spatial multiplexing is a common space-time modulation technique for MIMO communication systems where independent information streams are sent over different transmit antennas. Unfortunately, spatial multiplexing is sensitive to ill-conditioning of the channel matrix. Precoding can improve the resilience of spatial multiplexing at the expense of full channel knowledge at the transmitter-which is often not realistic. This correspondence proposes a quantized precoding system where the optimal precoder is chosen from a finite codebook known to both receiver and transmitter. The index of the optimal precoder is conveyed from the receiver to the transmitter over a low-delay feedback link. Criteria are presented for selecting the optimal precoding matrix based on the error rate and mutual information for different receiver designs. Codebook design criteria are proposed for each selection criterion by minimizing a bound on the average distortion assuming a Rayleigh-fading matrix channel. The design criteria are shown to be equivalent to packing subspaces in the Grassmann manifold using the projection two-norm and Fubini-Study distances. Simulation results show that the proposed system outperforms antenna subset selection and performs close to optimal unitary precoding with a minimal amount of feedback.

In wireless communication systems, the spatial multiplexing mode of Multiple Input Multiple Output (MIMO) is known to provide significant gain in spectral efficiency in the high Signal to Noise Ratio (SNR) region. Between the two methods, Singular Value Decomposition (SVD) and Bell labs Layered Space Time (BLAST), for implementing spatial multiplexing, the SVD scheme provides much higher spectral efficiency but it is much more complex to implement than the BLAST scheme. The aim of this work is to present an algorithm for antenna selection, which works together with BLAST method and compare the performance with that of SVD method. The comparison is done considering linear receiver architecture, and that the receiver has noisy channel estimates. The channel model used for performance evaluation is as per International Telecommunication Union (ITU)-specifications. It is shown in this work that, the proposed algorithm used with BLAST is very simple to implement and performs at par with SVD.

Accurate channel state information (CSI) is a prerequisite for diversity and multiplexing gains in multiple-input multiple-output (MIMO) wireless systems. More refined CSI can be attained by bootstrapping channel estimation techniques. We propose adaptive filtering-based iterative channel estimators with the incorporation of an iterative receiver over a flat-fading MIMO wireless link. The performance of three different algorithms with the aid of hard decision feedback is evaluated and compared through simulations. We show that the least-mean-square algorithm is a reasonable choice among the other considered algorithms in terms of performance and feasibility.

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.

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.

Reconfigurable intelligent surface (RIS) is capable of improving the wireless system performance by steering the reflected signal in the desired direction. One of the major challenges is that both the transceiver and RIS have to be jointly optimized, where the optimization problems have to be reformulated for different system models and scenarios. To circumvent this challenge, new low-complexity antenna selection (AS) algorithms for transceiver-agnostic RIS configuration are proposed. Given a multiple-input multiple-output (MIMO) channel, the proposed RIS-AS opts for accurately aligning the RIS both with the transmit antenna (TA) and receive antenna (RA) for the sake of maximizing the MIMO channel’s overall output power. The proposed RIS-AS only has to configure the RIS alone, i.e. without iterations with the transceiver optimization. As a result, the proposed RIS-AS has the compelling benefit that they are generically applicable, regardless of the specific transceiver architecture. Our simulation results confirm that the proposed RIS-AS is capable of supporting any MIMO configuration, regardless of their closed/open-loop, single-/ full-RF and multiplexing-/diversity-oriented setups.

We present a new hybrid multiple input multiple output (MIMO) system combining spatial multiplexing and transmit diversity at different levels. The inner core of the transmit processing is responsible for space-time coding with variable rate-diversity control. The outer core is composed of two (coarse) precoding matrices acting as transmit antenna and mode selection matrices. Exploiting the structure of these selection matrices, a capacity maximizing selection criterion is proposed to optimize the hybrid transmission mode jointly with the transmit antenna mapping. Simulation results show that the proposed hybrid system offers significant capacity gains over its open-loop counterpart and outperforms pure antenna selection and pure mode selection based systems.