Bandwidth Efficient Channel Estimation for Multiple-input Multiple-output (MIMO) Wireless Communication Systems: A Study of Semi-blind and Superimposed Schemes


Book Description

Superimposed pilots (SP) are another interesting alternative to reduce the impact of a pilot overhead without a significant increase in computational complexity. We present a study of the mean-squared error (MSE) and throughput performance of superimposed pilots (SP) for the estimation of a MIMO wireless channel. We illustrate a semi-blind scheme for SP based MIMO channel estimation, which improves performance over the traditional mean-estimator. A new result is presented for the worst-case capacity of a communication channel with correlated information symbols and noise. We also address the issue of optimal source-pilot power allocation for SP. In the end we consider the problem of estimation of a time-selective MIMO wireless channel using superimposed pilot (SP) symbols. We demonstrate a scheme for channel estimation based on a complex exponential basis expansion model (CEBEM) approximation of the time-selective wireless channel. We further reduce the MSE of estimation by employing an expectation-maximization (EM) based iterative estimation procedure.




Massive MIMO


Book Description

The last ten years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a high throughput to support applications such as voice, real-time video, movies, and games. Demands for wireless throughput and the number of wireless devices will always increase. In addition, there is a growing concern about energy consumption of wireless communication systems. Thus, future wireless systems have to satisfy three main requirements: i) having a high throughput; ii) simultaneously serving many users; and iii) having less energy consumption. Massive multiple-input multiple-output (MIMO) technology, where a base station (BS) equipped with very large number of antennas (collocated or distributed) serves many users in the same time-frequency resource, can meet the above requirements, and hence, it is a promising candidate technology for next generations of wireless systems. With massive antenna arrays at the BS, for most propagation environments, the channels become favorable, i.e., the channel vectors between the users and the BS are (nearly) pairwisely orthogonal, and hence, linear processing is nearly optimal. A huge throughput and energy efficiency can be achieved due to the multiplexing gain and the array gain. In particular, with a simple power control scheme, Massive MIMO can offer uniformly good service for all users. In this dissertation, we focus on the performance of Massive MIMO. The dissertation consists of two main parts: fundamentals and system designs of Massive MIMO. In the first part, we focus on fundamental limits of the system performance under practical constraints such as low complexity processing, limited length of each coherence interval, intercell interference, and finite-dimensional channels. We first study the potential for power savings of the Massive MIMO uplink with maximum-ratio combining (MRC), zero-forcing, and minimum mean-square error receivers, under perfect and imperfect channels. The energy and spectral efficiency tradeoff is investigated. Secondly, we consider a physical channel model where the angular domain is divided into a finite number of distinct directions. A lower bound on the capacity is derived, and the effect of pilot contamination in this finite-dimensional channel model is analyzed. Finally, some aspects of favorable propagation in Massive MIMO under Rayleigh fading and line-of-sight (LoS) channels are investigated. We show that both Rayleigh fading and LoS environments offer favorable propagation. In the second part, based on the fundamental analysis in the first part, we propose some system designs for Massive MIMO. The acquisition of channel state information (CSI) is very importantin Massive MIMO. Typically, the channels are estimated at the BS through uplink training. Owing to the limited length of the coherence interval, the system performance is limited by pilot contamination. To reduce the pilot contamination effect, we propose an eigenvalue-decomposition-based scheme to estimate the channel directly from the received data. The proposed scheme results in better performance compared with the conventional training schemes due to the reduced pilot contamination. Another important issue of CSI acquisition in Massive MIMO is how to acquire CSI at the users. To address this issue, we propose two channel estimation schemes at the users: i) a downlink "beamforming training" scheme, and ii) a method for blind estimation of the effective downlink channel gains. In both schemes, the channel estimation overhead is independent of the number of BS antennas. We also derive the optimal pilot and data powers as well as the training duration allocation to maximize the sum spectral efficiency of the Massive MIMO uplink with MRC receivers, for a given total energy budget spent in a coherence interval. Finally, applications of Massive MIMO in relay channels are proposed and analyzed. Specifically, we consider multipair relaying systems where many sources simultaneously communicate with many destinations in the same time-frequency resource with the help of a massive MIMO relay. A massive MIMO relay is equipped with many collocated or distributed antennas. We consider different duplexing modes (full-duplex and half-duplex) and different relaying protocols (amplify-and-forward, decode-and-forward, two-way relaying, and one-way relaying) at the relay. The potential benefits of massive MIMO technology in these relaying systems are explored in terms of spectral efficiency and power efficiency.




Understanding Communications Systems Principles—A Tutorial Approach


Book Description

Wireless communications and sensing systems are nowadays ubiquitous; cell phones and automotive radars typifying two of the most familiar examples. This book introduces the field by addressing its fundamental principles, proceeding from its very beginnings, up to today's emerging technologies related to the fifth-generation wireless systems (5G), Multi-Input Multiple Output (MIMO) connectivity, and Aerospace/Electronic Warfare Radar. The tone is tutorial. Problems are included at the end of each chapter to facilitate the understanding and assimilation of the material to electrical engineering undergraduate/graduate students and beginning and non-specialist professionals. Free temporary access to Keysight's SystemVue system simulation is provided to further enhance reader learning through hands-on tutorial exercises.Chapter 1 introduces wireless communications and sensing and in particular how curiosity-driven scientific research led to the foundation of the field. Chapter 2 presents a brief introduction to the building blocks that make up wireless systems. Chapter 3 focuses on developing an understanding of the performance parameters that characterize a wireless system. Chapter 4 deals with circuit topologies for modulation and detection. In chapter 5 we cover the fundamental transmitter and receiver systems architectures that enable the transmission of information at precise frequencies and their reception from among a rather large multitude of other signals present in space. Chapter 6 introduces 5G, its motivation, and its development and adoption challenges for providing unprecedented levels of highest speed wireless connectivity. Chapter 7 takes on the topic of MIMO, its justification and its various architectures. Chapter 8 addresses the topic of aerospace/electronic warfare radar and finally Chapter 9 presents three Tutorials utilizing the SystemVue simulation tool.




Multiple-Input Multiple-Output Channel Models


Book Description

A complete discussion of MIMO communications, from theory to real-world applications The emerging wireless technology Wideband Multiple-Input, Multiple-Output (MIMO) holds the promise of greater bandwidth efficiency and wireless link reliability. This technology is just now being implemented into hardware and working its way into wireless standards such as the ubiquitous 802.11g, as well as third- and fourth-generation cellular standards. Multiple-Input Multiple-Output Channel Models uniquely brings together the theoretical and practical aspects of MIMO communications, revealing how these systems use their multipath diversity to increase channel capacity. It gives the reader a clear understanding of the underlying propagation mechanisms in the wideband MIMO channel, which is fundamental to the development of communication algorithms, signaling strategies, and transceiver design for MIMO systems. MIMO channel models are important tools in understanding the potential gains of a MIMO system. This book discusses two types of wideband MIMO models in detail: correlative channel models—specifically the Kronecker, Weichselberger, and structured models—and cluster models, including Saleh-Valenzuela, European Cooperation in the field of Scientific and Technical Research (COST) 273, and Random Cluster models. From simple to complex, the reader will understand the models' mechanisms and the reasons behind the parameters. Next, channel sounding is explained in detail, presenting the theory behind a few channel sounding techniques used to sound narrowband and wideband channels. The technique of digital matched filtering is then examined and, using real-life data, is shown to provide very accurate estimates of channel gains. The book concludes with a performance analysis of the structured and Kronecker models. Multiple-Input Multiple-Output Channel Models is the first book to apply tensor calculus to the problem of wideband MIMO channel modeling. Each chapter features a list of important references, including core literary references, Matlab implementations of key models, and the location of databases that can be used to help in the development of new models or communication algorithms. Engineers who are working in the development of telecommunications systems will find this resource invaluable, as will researchers and students at the graduate or post-graduate level.




Signal-perturbation-free Semi-blind Channel Estimation for MIMO-OFDM Systems


Book Description

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) has been considered as a strong candidate for the beyond 3G (B3G) wireless communication systems, due to its high data-rate wireless transmission performance. It is well known that the advantages promised by MIMO-OFDM systems rely on the precise knowledge of the channel state information (CSI). In real wireless environments, however, the channel condition is unknown. Therefore, channel estimation is of crucial importance in MIMO-OFDM systems. Semi-blind channel estimation as a combination of the training-based or pilot-assisted method and the pure blind approach is considered to be a feasible solution for practical wireless systems due to its better estimation accuracy as well as spectral efficiency. In this thesis, we address the semi-blind channel estimation issue of MIMO-OFDM systems with an objective to develop very efficient channel estimation approaches. In the first part of the dissertation, several nulling-based semi-blind approaches are presented for the estimation of time-domain MIMO-OFDM channels. By incorporating a blind constraint that is derived from MIMO linear prediction (LP) into a training-based least-square method, a semi-blind solution for the time-domain channel estimation is first obtained. It is revealed through a perturbation analysis that the semi-blind solution is not subject to signal perturbation and therefore is superior to pure blind estimation methods. The LP-based semi-blind method is then extended for the channel estimation of MIMO-OFDM systems with pulse-shaping. By exploiting the pulse-shaping filter in the transmitter and the matched filter in the receiver, a very efficient semi-blind approach is developed for the estimation of sampling duration based multipath channels. A frequency-domain correlation matrix estimation algorithm is also presented to facilitate the computation of time-domain second-order statistics required in the LP-based method. The nulling-based semi-blind estimation issue of sparse MIMO-OFDM channels is also addressed. By disclosing and using a relationship between the positions of the most significant taps (MST) of the sparse channel and the lags of nonzero correlation matrices of the received signal, a novel estimation approach consisting of the MST detection and the sparse channel estimation, both in a semi-blind fashion, is developed. An intensive simulation study of all the proposed nulling-based methods with comparison to some existing techniques is conducted, showing a significant superiority of the new methodologies. The second part of the dissertation is dedicated to the development of two signal-perturbation-free (SPF) semi-blind channel estimation algorithms based on a novel transmit scheme that bears partial information of the second-order statistics of the transmitted signal to receiver. It is proved that the new transmit scheme can completely cancel the signal perturbation error in the noise-free case, thereby improving largely the estimation accuracy of correlation matrix for channel estimation in noisy conditions. It is also shown that the overhead caused by the transmission of the 8PF data is negligible as compared to that of regular pilot signals. By using the proposed transmit scheme, a whitening rotation (WR)-based algorithm is first developed for frequency-domain MIMO-OFDM channel estimation. It is shown through both theoretical analysis and simulation study that the new WR-based algorithm significantly outperforms the conventional WR-based method and the nulling-based semi-blind method. By using MIMO linear prediction, the new WR-based algorithm utilizing the 8PF transmit scheme is then extended for time-domain MIMO-OFDM channel estimation. Computer simulations show that the proposed signal-perturbation-free LP-based semi-blind solution performs much better than the LP semi-blind method without using the proposed transmit scheme, the LS method as well as the nulling-based semi-blind method in terms of the MSE of the channel estimate.




Near-Capacity Multi-Functional MIMO Systems


Book Description

Providing an all-encompassing self-contained treatment of Near-Capacity Multi-Functional MIMO Systems , the book starts by categorizing the family of Multiple-Input Multiple-Output (MIMO) schemes as diversity techniques, multiplexing schemes, multiple access arrangements and beam-forming techniques. Sophisticated coherent and low-complexity non-coherent MIMO receivers dispensing with channel estimation are considered in both classic and cooperation-aided scenarios. It is demonstrated that in the presence of correlated shadow-fading, cooperation-assisted systems may be expected to outperform their non-cooperative counterparts. The book contains a 100-page chapter on the unified treatment of all block codes in the context of high-flexibility, cutting-edge irregular Linear Dispersion Codes (LDC), which approach the MIMO-capacity. The majority of the book’s solutions are in the optimum sphere-packing frame-work. Sophisticated amalgam of five year’s near-capacity MIMO research Detailed examination of wireless landscape, including the fields of channel coding, spacetime coding and turbo detection techniques Novel tool of Extrinsic Information Transfer Charts (EXIT) used to address recent developments Material presented logically, allowing advanced readers to turn directly to any specific chapter of interest One of the only books to cover these subjects, giving equal weighting to each




Optimizing Massive MIMO


Book Description

The past decades have seen a rapid growth of mobile data traffic,both in terms of connected devices and data rate. To satisfy the evergrowing data traffic demand in wireless communication systems, thecurrent cellular systems have to be redesigned to increase both spectralefficiency and energy efficiency. Massive MIMO(Multiple-Input-Multiple-Output) is one solution that satisfy bothrequirements. In massive MIMO systems, hundreds of antennas areemployed at the base station to provide service to many users at thesame time and frequency. This enables the system to serve the userswith uniformly good quality of service simultaneously, with low-costhardware and without using extra bandwidth and energy. To achievethis, proper resource allocation is needed. Among the availableresources, transmit power beamforming are the most important degrees offreedom to control the spectral efficiency and energy efficiency. Dueto the use of excessive number of antennas and low-end hardware at thebase station, new aspects of power allocation and beamforming compared to currentsystems arises. In the first part of the thesis, new uplink power allocation schemes that based on long term channel statistics isproposed. Since quality of the channel estimates is crucial in massive MIMO, in addition to data power allocation, joint power allocationthat includes the pilot power as additional variable should be considered. Therefore a new framework for power allocation thatmatches practical systems is developed, as the methods developed in the literature cannot be applied directly to massive MIMO systems. Simulation results confirm the advantages brought by the the proposed new framework. In the second part, we introduces a new approach to solve the joint precoding and power allocation for different objective in downlink scenarios by a combination of random matrix theory and optimization theory. The new approach results in a simplified problem that, though non-convex, obeys a simple separable structure. Simulation results showed that the proposed scheme provides large gains over heuristic solutions when the number of users in the cell is large, which is suitable for applying in massive MIMO systems. In the third part we investigate the effects of using low-end amplifiers at the basestations. The non-linear behavior of power consumption in these amplifiers changes the power consumption model at the basestation, thereby changes the power allocation and beamforming design. Different scenarios are investigated and resultsshow that a certain number of antennas can be turned off in some scenarios. In the last part we consider the use of non-orthogonal-multiple-access (NOMA) inside massive MIMO systems in practical scenarios where channel state information (CSI) is acquired through pilot signaling. Achievable rate analysis is carried out for different pilot signaling schemes including both uplink and downlink pilots. Numerical results show that when downlink CSI is available at the users, our proposed NOMA scheme outperforms orthogonal schemes. However with more groups of users present in the cell, it is preferable to use multi-user beamforming in stead of NOMA.




Power Control for Multi-Cell Massive MIMO


Book Description

The cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increases in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the frequency bandwidth for cellular communication is limited, significant effort was dedicated to improve the utilization of the available spectrum and increase the system performance via new technologies. For example, 3G and 4G networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is a necessity for new cellular network technologies to accommodate the ever-growing data traffic demand. 5G is behind the corner to deal with the tremendous data traffic requirements that will appear in cellular networks in the next decade. Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. As an outcome of using Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to properly allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods to deliver high spectral and energy efficiency of Massive MIMO networks. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric. In the first part of this thesis, we investigate reusing the resources of a Massive MIMO system, for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO systems by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario in comparison with conventional Massive MIMO systems to limit the interference that is caused between the cellular network and the D2D communication, thereby enabling their coexistence. In this part, we propose a novel pilot transmission scheme for D2D users to limit the interference to the channel estimation phase of cellular users in comparison with the case of sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO systems. This method aims at assuring that D2D communication enhances the SE of the network in comparison with conventional Massive MIMO systems. In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO systems. The new power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, which are based on the network-wide max-min and proportional fairness performance metrics. We first explain the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve this optimization problem, we prove that it can be rewritten in a convex form and then be solved using standard optimization solvers.




A Study on Blind and Semiblind Detection for MIMO Systems and ARQ Retransmissions


Book Description

We investigate blind and semi-blind channel estimation and detection techniques for multiple-input multiple-output (MIMO) communication systems. MIMO is an emerging technology that provides significant capacity and throughput gain over single-input single-output (SISO) systems without increase in bandwidth and power. To achieve these gains, typically, it requires pilot assisted channel estimation at the cost of bandwidth and power efficiency. Blind and semi-blind channel estimation or direct data detection techniques provide viable alternatives. We begin with blind zero-forcing (ZF) detection of Space-Time Block (STB) coded signal for MIMO systems over flat-fading channels for single user systems. We propose linear programming-based blind ZF equalization algorithm. Our method is globally convergent. We exploit redundancy and the implicit structure of space-time block codes to cast the problem into a low complexity linear programming. Unlikeseveral known methods, the proposed technique is applicable to full-rate space-time block codes such as the popular Alamouti orthogonal code and the quasi-orthogonal space time code. For orthogonal space-time codes, our LP algorithm achieves near maximum likelihood detection of orthogonal codes without channel knowledge and does not impose any conditions on the number of receive antennas. We extend this work to blind detection of STB coded data for co-channel synchronous multiuser MIMO system. We propose a globally convergent algorithm for the blind detection of co-channel multiuser signals under synchronous orthogonal space-time block coded modulation. We exploit the algebraic structure of the orthogonal space-time block codes and the Quadrature Amplitude Modulation (QAM) signal constellation to derive a quadratic programming (QP) algorithm that achieves desired convergence without the aid of training pilots. Focusing on the high rate space-time block codes in a synchronous co-channel multiuser system, we propose a successive processing strategy based on the proposed blind zero forcing (ZF) detector which has low computational complexity and fast convergence. Furthermore, our QP receiver for multiuser detection imposes less stringent conditions on the number of receive antennas of the multi-input-multi-output (MIMO) system. After investigating blind detection of STB coded signals for single and multiuser MIMO system, we focus on bandwidth and power efficient Hybrid Automatic Repeat reQuest (HARQ) at the physical layer level. The concept of HARQ is adapted in Long Term Evolution (LTE) and WiMAX standard and both standards consider full retransmission of the original copy of the original information. The physical layer performs joint detection called diversity combining. We propose retransmission of partial copy of original message and omit pilot symbols during retransmission. Our receiver jointly estimates channel of the both transmissions in semi-blind fashion. The joint detection of two transmissions improves overall performance of the receiver. We demonstrate bandwidth savings without significant performance loss and mild increase in complexity.




Massive MIMO Networks


Book Description

Massive MIMO Networks is the first book on the subject to cover the spatial channel correlation and consider rigorous signal processing design essential for the complete understanding by the students, practicing engineers and researchers working on modern day communication systems.