The demand for access to information when and where you need has motivated the transition of wireless communications from a fixed infrastructure based cellular communications technology to a more pervasive adhoc wireless networking technology. Challenges still remain in wireless adhoc networks in terms of meeting higher capacity demands, improved reliability and longer connectivity before it becomes a viable widespread commercial technology. Present day wireless mesh networking uses node-to-node serial multi-hop communication to convey information from source to destination in the network. The performance of such a network depends on finding the best possible route between the source and destination nodes. However the end-to-end performance can only be as good as the weakest link within a chosen route. Unlike wired networks, the quality of point-to-point links in a wireless mesh network is subject to random fluctuations. This adversely affects the performance resulting in poor throughput and poor energy efficiency. In recent years, a new paradigm for communication called cooperative communications has been proposed for which initial information theoretic studies have shown the potential for improvements in capacity over traditional multi-hop wireless networks. Cooperative communication involves exploiting the broadcast nature of the wireless medium to form virtual antenna arrays out of independent single-antenna network nodes for transmission. In this research we explore the fundamental performance limits of cooperative communication under more practical operating scenarios. Specifically we provide a framework for computing the outage and ergodic capacities of non identical distributed MIMO links, study the effect of time synchronization error on system performance, analyze the end-to-end average bit error rate (ABER) performance under imperfect relaying, and study range extension and energy efficiency offered by the system when compared to a traditional system. / Ph. D.

A critical factor affecting the future prospects of wireless networks for wide-scale deployment is network capacity: the end users wish to have their communication experience over wireless networks to be comparable or similar to that for wireline networks. An effective approach to increase network capacity is to increase spectrum efficiency. Such an approach can be achieved by the use of multiple antenna systems (also known as multiple-input multiple-output (MIMO) technology). The benefits of substantial improvements in capacity at no cost of additional spectrum and power have positioned MIMO as one of the breakthrough technologies in modern wireless communications. As expected, research activities on applying MIMO to a variety of wireless networks have soared in recent years. However, compared with the simple point-to-point MIMO channel, which is relatively well-understood nowadays, network design and performance optimization for MIMO-based wireless networks is considerably more challenging. Many fundamental problems remain unsolved. Due to the complex characteristics of MIMO physical layer technology, it is not only desirable but also necessary to consider models and constraints at multiple layers (e.g., physical, link, and network) jointly. The formulations of these cross-layer problems for MIMO wireless networks, however, are usually mathematically challenging. In this dissertation, we aim to develop some novel algorithmic design and optimization techniques that provide optimal or near-optimal solutions. Based on network structure, this dissertation is organized into two parts. In the first part, we focus on single-hop MIMO wireless networks, while in the second part, we focus on multi-hop MIMO networks. The main results and contributions of this dissertation are summarized as follows. Single-hop MIMO Networks. In the first part of this dissertation, we study three different optimization problems for single-hop MIMO networks. The first problem addresses weighted proportional fair (WPF) scheduling associated with MIMO broadcast channels (Chapter 2). For the WPF scheduling problem in MIMO broadcast channels, we develop two algorithms that can efficiently determine the optimal dirty paper encoding order and power allocation to achieve an optimal WPF performance. To our knowledge, our work is the first that provides solutions to the WPF scheduling problem in MIMO broadcast channels. Our next problem concerns single-hop MIMO ad hoc networks (Chapter 3), which are quite different from the MIMO broadcast channels studied in the previous chapter. Single-hop MIMO ad hoc networks can be simply described as "multiple one-to-one," as compared with MIMO broadcast channels, which are "one-to-many." Performance optimization for such networks is known to be challenging due to the non-convex mathematical structure. Indeed, these networks can be viewed as the general case of interference channels in network information theory context, for which the capacity region remains unknown even under the two-user case. In this chapter, we treat the co-channel interference in the network as noise. We consider the maximum weighted sum rate problem under the single-carrier setting. We propose a global optimization approach that combines branch-and-bound (BB) and the reformulation-linearization technique (RLT). This technique is guaranteed to find a global optimal solution Multi-hop MIMO Networks. In addition to managing resources such as power and scheduling in single-hop networks, routing and end-to-end session rate control need to be considered in multi-hop MIMO networks. Thus, performance optimization problems in multi-hop MIMO networks are more interesting and yet challenging. In Chapter 4, we first consider the problem of jointly optimizing power and bandwidth allocation at each node and multihop/multipath routing in a multi-hop MIMO network that employs orthogonal channels. We show that this problem has some special structure that admits a decomposition into a set of subproblems in its dual domain. Based on this finding, we propose both centralized and distributed optimization algorithms to solve this problem optimally. In Chapter 5, we relax the orthogonal channel assumption. More specifically, we exploit the advantage of "dirty paper coding" (DPC) to allow multiple links originated from the same node to share the same channel media simultaneously. However, the formulation of cross-layer optimization problem with DPC has a non-convex structure and an exponentially large search space inherent in enumerating DPC's encoding orders. To address these difficulties, we propose an approach to reformulate and convexify the original problem. Based on the reformulated problem, we design an efficient solution procedure by exploiting decomposable dual structure. One thing in common in Chapters 4 and 5 is that we adopt the classical matrix-based MIMO channel models at the physical layer. Although this approach has its merit, the complex matrix operations in the classical MIMO models may pose a barrier for researchers in networking research community to gain fundamental understanding on MIMO networks. To bridge this gap between communications and networking communities, in Chapter 6, we propose a simple, accurate, and tractable model to enable the networking community to carry out cross-layer research for multi-hop MIMO networks. At the physical layer, we develop an accurate and simple model for MIMO channel capacity computation that captures the essence of spatial multiplexing and transmit power limit without involving complex matrix operations and the water-filling algorithm. At the link layer, we devise a space-time scheduling scheme called order-based interference cancellation (OBIC) that significantly advances the existing zero-forcing beamforming (ZFBF) to handle interference in a multi-hop network setting. The proposed OBIC scheme employs simple algebraic computation on matrix dimensions to simplify ZFBF in a multi-hop network. Finally, we apply both the new physical and link layer models to study a cross-layer optimization problem for a multi-hop MIMO network. / Ph. D.

This thesis investigates the application of an iterative leakage-based precoding algorithm to practical multiuser-MIMO systems. We consider the effect of practical impairments including imperfect channel state information, transmit antenna correlation, and time-varying channels. Solutions are derived which improve performance of the algorithm with imperfect channel state information at the transmitter by leveraging knowledge of the second-order statistics of the error. From this work we draw a number of conclusions on how imperfect channel state information may impact the system design including the importance of interference suppression at the receiver and the selection of the number of co-scheduled users. We also demonstrate an efficient approach to improve the convergence of the algorithm when using interference-rejection-combining receivers. Finally, we conduct simulations of an LTE-A system employing the improved algorithm to show its utility for modern communication systems. / Master of Science

The design in this work provides an easy and cost-efficient way of performing an FPGA implementation of a specific algorithm through use of a custom hardware design language and communication synthesis. The framework is designed to optimize performance with matrix-type mathematical operations. The largest matrices used in this process are 4x4 matrices. The primary example modeled in this work is MIMO decoding. Making this possible are 16 functional unit containers within the framework, with generalized interfaces, which can hold custom user hardware and IP cores. This framework, which is controlled by a microsequencer, is centered on a matrix-based memory structure comprised of 64 individual dual-ported memory blocks. The microsequencer uses an instruction word that can control every element of the architecture during a single clock cycle. Routing to and from the memory structure uses an optimized form of a crossbar switch with predefined routing paths supporting any combination of input/output pairs needed by the algorithm. A goal at the start of the design was to achieve a clock speed of over 100 MHz; a clock speed of 183 MHz has been achieved. This design is capable of performing a 4x4 matrix inversion within 335 clock cycles, or 1,829 ns. The power efficiency of the design is measured at 17.15 MFLOPS/W. / Master of Science

3-dimensional (3D) massive multiple-input-multiple-output (MIMO)/full dimensional (FD) MIMO and application of artificial intelligence are two main driving forces for next generation wireless systems. This dissertation focuses on aspects of channel estimation and precoding for 3D massive MIMO systems and application of deep reinforcement learning (DRL) for MIMO broadcast beam synthesis. To be specific, downlink (DL) precoding and power allocation strategies are identified for a time-division-duplex (TDD) multi-cell multi-user massive FD-MIMO network. Utilizing channel reciprocity, DL channel state information (CSI) feedback is eliminated and the DL multi-user MIMO precoding is linked to the uplink (UL) direction of arrival (DoA) estimation through estimation of signal parameters via rotational invariance technique (ESPRIT). Assuming non-orthogonal/non-ideal spreading sequences of the UL pilots, the performance of the UL DoA estimation is analytically characterized and the characterized DoA estimation error is incorporated into the corresponding DL precoding and power allocation strategy. Simulation results verify the accuracy of our analytical characterization of the DoA estimation and demonstrate that the introduced multi-user MIMO precoding and power allocation strategy outperforms existing zero-forcing based massive MIMO strategies. In 3D massive MIMO systems, especially in TDD mode, a base station (BS) relies on the uplink sounding signals from mobile stations to obtain the spatial information for downlink MIMO processing. Accordingly, multi-dimensional parameter estimation of MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this work, we also study the joint estimation of elevation and azimuth angles as well as the delay parameters for 3D massive MIMO orthogonal frequency division multiplexing (OFDM) systems under a parametric channel modeling. We introduce a matrix-based joint parameter estimation method, and analytically characterize its performance for massive MIMO OFDM systems. Results show that antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance, and the characterized MSEs match well with the simulated ones. Also, the joint parametric channel estimation outperforms the MMSEbased channel estimation in terms of the correlation between the estimated channel and the real channel. Beamforming in MIMO systems is one of the key technologies for modern wireless communication. Creating wide common beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' movement over time. In this dissertation, we present a MIMO broadcast beam optimization framework using deep reinforcement learning. Our proposed solution can autonomously and dynamically adapt the MIMO broadcast beam parameters based on user' distribution in the network. Extensive simulation results show that the introduced algorithm can achieve the optimal coverage, and converge to the oracle solution for both single cell and multiple cell environment and for both periodic and Markov mobility patterns. / Doctor of Philosophy / Multiple-input-multiple-output (MIMO) is a technology where a transmitter with multiple antennas communicates with one or multipe receivers having multiple antennas. 3- dimensional (3D) massive MIMO is a recently developed technology where a base station (BS) or cell tower with a large number of antennas placed in a two dimensional array communicates with hundreds of user terminals simultaneously. 3D massive MIMO/full dimensional (FD) MIMO and application of artificial intelligence are two main driving forces for next generation wireless systems. This dissertation focuses on aspects of channel estimation and precoding for 3D massive MIMO systems and application of deep reinforcement learning (DRL) for MIMO broadcast beam synthesis. To be specific, downlink (DL) precoding and power allocation strategies are identified for a time-division-duplex (TDD) multi-cell multi-user massive FD-MIMO network. Utilizing channel reciprocity, DL channel state information (CSI) feedback is eliminated and the DL multi-user MIMO precoding is linked to the uplink (UL) direction of arrival (DoA) estimation through estimation of signal parameters via rotational invariance technique (ESPRIT). Assuming non-orthogonal/non-ideal spreading sequences of the UL pilots, the performance of the UL DoA estimation is analytically characterized and the characterized DoA estimation error is incorporated into the corresponding DL precoding and power allocation strategy. Simulation results verify the accuracy of our analytical characterization of the DoA estimation and demonstrate that the introduced multi-user MIMO precoding and power allocation strategy outperforms existing zero-forcing based massive MIMO strategies. In 3D massive MIMO systems, especially in TDD mode, a BS relies on the uplink sounding signals from mobile stations to obtain the spatial information for downlink MIMO processing. Accordingly, multi-dimensional parameter estimation of MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this work, we also study the joint estimation of elevation and azimuth angles as well as the delay parameters for 3D massive MIMO orthogonal frequency division multiplexing (OFDM) systems under a parametric channel modeling. We introduce a matrix-based joint parameter estimation method, and analytically characterize its performance for massive MIMO OFDM systems. Results show that antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance, and the characterized MSEs match well with the simulated ones. Also, the joint parametric channel estimation outperforms the MMSE-based channel estimation in terms of the correlation between the estimated channel and the real channel. Beamforming in MIMO systems is one of the key technologies for modern wireless communication. Creating wide common beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' movement over time. In this dissertation, we present a MIMO broadcast beam optimization framework using deep reinforcement learning. Our proposed solution can autonomously and dynamically adapt the MIMO broadcast beam parameters based on user' distribution in the network. Extensive simulation results show that the introduced algorithm can achieve the optimal coverage, and converge to the oracle solution for both single cell and multiple cell environment and for both periodic and Markov mobility patterns.

Echo state network (ESN) is a specific neural network structure composed of high dimensional nonlinear dynamics and learned readout weights. This thesis considers applying ESN for symbol detection in multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. A new ESN structure, namely, windowed echo state networks (WESN) is introduced to further improve the symbol detection performance. Theoretical analysis justifies WESN has an enhanced short-term memory (STM) compared with the standard ESN such that WESN can offer better computing ability. Additionally, the bandwidth spent as the training set is the same as the demodulation reference signals defined in 3GPP LTE/LTE-Advanced systems for the ESN/WESN based symbol detection. Meanwhile, a unified training framework is developed for both comb and scattered pilot patterns. Complexity analysis demonstrates the advantages of ESN/WESN based symbol detector compared to conventional symbol detectors such as linear minimum mean square error (LMMSE) and sphere decoder when the system is employed with a large number of OFDM sub-carriers. Numerical evaluations show that ESN/WESN has an improvement of symbol detection performance as opposed to conventional methods in both low SNR regime and power amplifier (PA) nonlinear regime. Finally, it demonstrates that WESN can generate a better symbol detection result over ESN. / Artificial neural networks (ANN) are widely used in recognition tasks such as recommendation systems, robotics path planning, self-driving, video tracking, image classifications, etc. To further explore the applications of ANN, this thesis considers using a specific ANN, echo state network (ESN) for a wireless communications task: MIMO-OFDM symbol detection. Furthermore, it proposed an enhanced version of the standard ESN, namely, windowed echo state network (WESN). Theoretical analyses on the short term memory (STM) of ESN and WESN show that the later one has a longer STM. Besides, the training set size of this ESN/WESN based method is chosen the same as the pilot symbols used in conventional communications systems. The algorithm complexity analysis demonstrates the ESN/WESN based method performs with lower complexity compared with conventional methods, such as linear mean square error (LMMSE) and sphere decoding. Comprehensive simulations examine how the symbol detection performance can be improved by using ESN and its variant WESN when the transmission link is non-ideal.

High transmission data rate, spectral efficiency, and reliability are necessary for future wireless communications systems. In a multipath-rich wireless channel, deploying multiple antennas at both the transmitter and receiver achieves high data rate, without increasing the total transmission power or bandwidth. When perfect knowledge of the wireless channel conditions is available at the receiver, the capacity has been shown to grow linearly with the number of antennas. However, the channel conditions must be estimated since perfect channel knowledge is never known a priori. In practice, the channel estimation procedure can be aided by transmitting pilot symbols that are known at the receiver. System performance depends on the quality of channel estimate, and the number of pilot symbols. It is desirable to limit the number of transmitted pilot symbols because pilot symbols reduce spectral efficiency. This thesis analyzes the system performance of coded multiple-input multiple-output (MIMO) systems for the quasi-static fading channel. The assumption that perfect channel knowledge is available at the receiver must be removed, in order to more accurately examine the system performance. Emphasis is placed on developing channel estimation strategies for an iterative Vertical Bell-Labs Layered Space Time (V-BLAST) architecture. The channel estimate can be sequentially improved between successive iterations of the iterative V-BLAST algorithm. For both the coded and uncoded systems, at high signal to noise ratio only a minimum number of pilot symbols per transmit antenna are required to achieve perfect channel knowledge performance. / Master of Science

Orthogonal Frequency Division Multiplexing (OFDM) is fast gaining ground as a preferred modulation technique for short range wireless data application such as 802.11a/g, 802.15.3a and 802.16. Recently, use of multiple transmit and receive antenna for improving spectral efficiency in a wireless system has received much interest. IEEE 802.11 has set up the Work Group 802.11n to develop a standard for enhanced rate 802.11 based on OFDM using Multi Input/Multiple Output (MIMO) techniques. The most dominant proposal is the use of singular value decomposition based MIMO methods to achieve the high data rate. The selection of modulation and coding rates plays a significant role in the overall throughput of the system, more so in cases where the traffic between the transmitter and the receiver consists of short bursts and the user location is not fixed. The performance of a given modulation and coding technique depends on the channel condition. Closed form or bounding solutions exists for various modulation and coding techniques. But these techniques are not suitable for real time application where the channel is dynamic. The approach taken in this thesis is to decouple frequency selective MIMO OFDM channel into orthogonal spatial and frequency domains channels using Fast Fourier Transforms and Singular Value Decomposition. The channels can be viewed as parallel flat fading channels for which the expected BER rate can be computed. A SNR-BER table is used to efficiently compute the performance efficiently. An effective SNR is computed using the table and compared with rate threshold to select a suitable rate. Improvements of 15 dB and above are shown the link budget while using a four transmit four receive MIMO system. Proposed 802.11n TGn Sync physical layer standard is used to evaluate the performance. The performance in case of one of the systems being a legacy 802.11a/g nodes is also looked into. Gains up to 7 dB are shown in the link budget. / Master of Science

Increasing costs for space shuttle missions translate to smaller, lighter, and more flexible satellites that maintain or improve current dynamic requirements. This is especially true for optical systems and surfaces. Lightweight, inflatable structures, otherwise known as gossamer structures, are smaller, lighter, and more flexible than current satellite technology. Unfortunately, little research has been performed investigating cost effective and feasible methods of dynamic analysis and control of these structures due to their inherent, non-linear dynamic properties. Gossamer spacecraft have the potential of introducing lenses and membrane arrays in orbit on the order of 25 m in diameter. With such huge structures in space, imaging resolution and communication transmissibility will correspondingly increase in orders of magnitude. A daunting problem facing gossamer spacecraft is their highly flexible nature. Previous attempts at ground testing have produced only localized deformation of the structure's skin rather than excitation of the global (entire structure's) modes. Unfortunately, the global modes are necessary for model parameter verification. The motivation of this research is to find an effective and repeatable methodology for obtaining the dynamic response characteristics of a flexible, inflatable structure. By obtaining the dynamic response characteristics, a suitable control technique may be developed to effectively control the structure's vibration. Smart materials can be used for both active dynamic analysis as well as active control. In particular, piezoelectric materials, which demonstrate electro-mechanical coupling, are able to sense vibration and consequently can be integrated into a control scheme to reduce such vibration. Using smart materials to develop a vibration analysis and control algorithm for a gossamer space structure will fulfill the current requirements of space satellite systems. Smart materials will help spawn the next generation of space satellite technology. / Master of Science

The demands for greater capacity and lower transmitted power have historically motivated research in spatial diversity systems. Diversity techniques have been implemented in many current systems and have been shown to reduce the transmit power required to maintain acceptable system performance. Traditionally spatial diversity is based on the transmission and reception of a single stream of symbols through independent and spatially separated propagation channels. In more recent developments, space-time coding and array processing techniques use diversity concepts to resolve multiple independent streams of data and increase the potential data-rate. This new space-time research investigates the unprecedented ability to simultaneously transmit separate data streams from many closely-spaced antennas on a common carrier frequency. The effectiveness of these multi-element arrays in communication systems has been found to depend on antenna design and specific characteristics of the propagation channels. This thesis describes an effort to characterize an indoor office environment with respect to these applications. Theoretical analyses have demonstrated a relationship between the theoretical capacity of multi-element array systems with the cross-correlation of spatially separated channels. Historical measurements have also shown that in the presence of Rayleigh fading, antenna spacing may be used to control the level of correlation between propagation channels and maximize the diversity gain, or potential system capacity of a space-time system. Both the design of the antenna arrays and characteristics of the propagation environment influence a system's potential capacity. This thesis describes the construction of a measurement system and the use of this system to evaluate the capacity gains of multi-element arrays in a wireless communication system. The presented system is capable of measuring the channel gains between a number of transmitter and receiver antenna elements and calculating both the cross-correlation between channel gains and the theoretical system capacity. After a discussion of previous research, the measurement system and subsequent measurement results are described. / Master of Science