The scarce and overpopulated radio spectrum is going to present a major barrier to the growth and development of future wireless networks. As such, spectrum sharing seems to be inevitable to accommodate the exploding demand for high data rate applications. A major challenge to realizing the potential advantages of spectrum sharing is interference management. This thesis deals with interference management techniques in noncooperative networks. In specific, interference alignment is used as a powerful technique for interference management. We use the degrees of freedom (DoF) as the figure of merit to evaluate the performance improvement due to the interference management schemes. This dissertation is organized in two parts. In the first part, we consider the K-user multiple input multiple output (MIMO) Gaussian interference channel (IC) with M antennas at each transmitter and N antennas at each receiver. This channel models the interaction between K transmitter-receiver pairs sharing the same spectrum for data communication. It is assumed that the channel coefficients are constant and are available at all nodes prior to data transmission. A new cooperative upper-bound on the DoF of this channel is developed which outperforms the known bounds. Also, a new achievable transmission scheme is provided based on the idea of interference alignment. It is shown that the achievable DoF meets the upper-bound when the number of users is greater than a certain threshold, and thus it reveals the channel DoF. In the second part, we consider communication over MIMO interference and X channels in a fast fading environment. It is assumed that the transmitters obtain the channel state information (CSI) after a finite delay which is greater than the coherence time of the channel. In other words, the CSI at the transmitters becomes outdated prior to being exploited for the current transmission. New transmission schemes are proposed which exploit the knowledge of the past CSI at the transmitters to retrospectively align interference in the subsequent channel uses. The proposed transmission schemes offer DoF gain compared to having no CSI at transmitters. The achievable DoF results are the best known results for these channels. Simple cooperative upper-bounds are developed to prove the tightness of our achievable results for some network configurations.

For wireless communication systems, due to broadcasting nature of wireless medium, how to keep eavesdroppers from wiretapping messages is worth investigated. In addition to encryption techniques applied in application layer, physical layer secrecy techniques have been studied in literature. Under the premise that eavesdropper cannot steal any information, physical layer secrecy focus on maximizing the capacity of legal transmission, and make it more reliable by using physical properties of wireless channel. This thesis considers an amplify-and-forward (AF) multiple-input multiple-output (MIMO) cooperative communication network with an untrusted relay (UR), and linear precoders are employed at source, relay, and destination. The relay here serves as a bridge of transmission 1 between the source and the destination. However, assume that the untrusted relay may wiretap information from the source without authorization. In order to prevent relay from wiretapping information, the destination generates artificial noise (AN) to interfere the relay, when the relay is receiving information from the source. Since AN is generated by the destination, the destination can eliminate AN by itself after receiving signal from the relay without corrupting signals of legal transmission. We propose precoder design for source, relay and destination to maximize secrecy capacity under the power constraint of three nodes. By utilizing singular value decomposition (SVD) of all channel matrices and Hadamard inequality, we simplify the optimization problem of precoding matrices to scalar optimization problem, and optimization can be accomplished recursively.

Design and analysis of random-like codes for various multiple-input and multiple-output communication systems are addressed in this work. Random-like codes have drawn significant interest because they offer capacity-achieving performance. We first consider the analysis and design of low-density parity-check (LDPC) codes for turbo multiuser detection in multipath CDMA channels. We develop techniques for computing the probability density function (pdf) of the extrinsic messages at the output of the soft-input soft-output (SISO) multiuser detectors as a function of the pdf of input extrinsic messages, user spreading codes, channel impulse responses, and signal-to-noise ratios. Using these techniques, we are able to accurately compute the thresholds for LDPC codes and design good irregular LDPC codes. We then apply the tools of density evolution with mixture Gaussian approximations to optimize irregular LDPC codes and to compute minimum operational signal-to-noise ratios for ergodic MIMO OFDM channels. In particular, the optimization is done for various MIMO OFDM system configurations which include different number of antennas, different channel models and different demodulation schemes. We also study the coding-spreading tradeoff in LDPC coded CDMA systems employing multiuser joint decoding. We solve the coding-spreading optimization based on the extrinsic information SNR evolution curves for the SISO multiuser detectors and the SISO LDPC decoders. Both single-cell and multi-cell scenarios will be considered. For each of these cases, we will characterize the extrinsic information for both finite-size systems and the so-called large systems where asymptotic performance results must be evoked. Finally, we consider the design optimization of irregular repeat accumulate (IRA) codes for MIMO communication systems employing iterative receivers. We present the density evolution-based procedure with Gaussian approximation for optimizing the IRA code ensemble. We adopt an approximation method based on linear programming to design an IRA code with the extrinsic information transfer (EXIT) chart matched to that of the soft MIMO demodulator.

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technology has been used widely in many wireless communication systems. Signals will be distorted when they are transmitted in wireless channels. For the reason that wireless channel is time or location variant, we have to estimate the channel impulse response and use the channel state information to compensate the channel distortion. In order to estimate the state of the channel, let the known training symbols put in front of the data symbols and use training symbols to estimate channel response. A typical channel estimate for MIMO OFDM systems is treated as spatially uncorrelated. However in many realistic scenarios, the channel tends to be spatially correlated. Indeed, we have no prior knowledge of the channel spatial correlation. So consider the spatial correlation, the channel can estimate accurately. And it is important that how to combine spatial correlation and channel estimation to reduce the estimation error. In the paper we propose a iterative channel spatial correlation and channel estimation algorithm. At first, channel spatial correlation estimation is obtained by synchronize symbols. The receiver uses the estimated channel to help the detection/decision of data symbol. And then the channel estimation treats the detected signals as known data to perform a next stage channel estimation iteratively. By utilizing the iterative channel estimation and signal detection process we can reduce the estimation error caused by channel spatial correlation estimation. The accuracy of the channel estimation can be improved by increasing the number of iteration process. Simulation results demonstrate the iterative spatial correlation and channel estimation algorithm can provide better mean-square-error performance.

In multicell coordinated networks where multiple base stations cooperate to jointly combat interference from adjacent cells and fading to receivers, one of the outstanding questions is what is the role of receive antenna and receiver processing. Multiple receive antennas not only enable additional degrees of freedom at each receiver to combat the other-cell interference but also can change the transmitter design because transmitter and receiver beamforming design is often closely coordinated. In this dissertation, we investigate the role of the multiple receive antennas in multicell cooperative systems under different interference conditions. We then present novel non-iterative and iterative coordinated beamforming and precoding algorithms with different receiver processing. We present comprehensive performance comparison of various multicell cooperative systems and explore the feasibility of achieving much higher throughput via hyper-densification of heterogeneous and small cell networks with mandatory multicell cooperation. / text

In most wireless communications research, the channel models considered experience less severe fading than the classic Rayleigh fading case. In this thesis, however, we investigate MIMO channels where the fading is more severe. In these environments, we show that the coefficient of variation of the channel amplitudes is a good predictor of the link mutual information, for a variety of models. We propose a novel channel model for severely fading channels based on the complex multivariate t distribution. For this model, we are able to compute exact results for the ergodic mutual information and approximations to the outage probabilities for the mutual information. Applications of this work include wireless sensors, RF tagging, land-mobile, indoor-mobile, ground-penetrating radar, and ionospheric radio links. Finally, we point out that the methodology can also be extended to evaluate the mutual information of a cellular MIMO link and the performance of various MIMO receivers in a cellular scenario. In these cellular applications, the channel itself is not severely fading but the multivariate t distribution can be applied to model the effects of intercellular interference.

The scarce and overpopulated radio spectrum is going to present a major barrier to the growth and development of future wireless networks. As such, spectrum sharing seems to be inevitable to accommodate the exploding demand for high data rate applications. A major challenge to realizing the potential advantages of spectrum sharing is interference management. This thesis deals with interference management techniques in noncooperative networks. In specific, interference alignment is used as a powerful technique for interference management. We use the degrees of freedom (DoF) as the figure of merit to evaluate the performance improvement due to the interference management schemes. This dissertation is organized in two parts. In the first part, we consider the K-user multiple input multiple output (MIMO) Gaussian interference channel (IC) with M antennas at each transmitter and N antennas at each receiver. This channel models the interaction between K transmitter-receiver pairs sharing the same spectrum for data communication. It is assumed that the channel coefficients are constant and are available at all nodes prior to data transmission. A new cooperative upper-bound on the DoF of this channel is developed which outperforms the known bounds. Also, a new achievable transmission scheme is provided based on the idea of interference alignment. It is shown that the achievable DoF meets the upper-bound when the number of users is greater than a certain threshold, and thus it reveals the channel DoF. In the second part, we consider communication over MIMO interference and X channels in a fast fading environment. It is assumed that the transmitters obtain the channel state information (CSI) after a finite delay which is greater than the coherence time of the channel. In other words, the CSI at the transmitters becomes outdated prior to being exploited for the current transmission. New transmission schemes are proposed which exploit the knowledge of the past CSI at the transmitters to retrospectively align interference in the subsequent channel uses. The proposed transmission schemes offer DoF gain compared to having no CSI at transmitters. The achievable DoF results are the best known results for these channels. Simple cooperative upper-bounds are developed to prove the tightness of our achievable results for some network configurations.

Research in wireless communications has been actively carried out in recent years. In order to enable a high data transmission rate, multiple-input multiple-output (MIMO) communications has been proposed and commonly adopted. Accurate channel identification and reliable data detection are major challenges in the implementation of a communications system operating over a wireless fading channel. These issues become even more challenging in MIMO systems since there are many more parameters involved in the estimation processes. This thesis, consisting of four major parts, focuses on applying convex optimization to solve design problems in both MIMO channel estimation and data detection. The first part proposes a novel orthogonal affine precoding technique for jointly optimal channel estimation and symbol detection in a general MIMO frequency-selective fading channel. Additionally, the optimal power allocation between the data and training signals is also analytically derived. The proposed technique is shown to perform much better than other affine precoding techniques in terms of detection error probability and computational complexity. The second part is concerned with the MIMO orthogonal frequency-division multiplexing (OFDM) systems. The superimposed training technique developed in the first part is applied and extended for MIMO-OFDM systems where all the involved transmitters and receivers are assumed to be uncorrelated. Analytical and numerical results confirm that the proposed design can efficiently identify the unknown wireless channel as well as effectively recover the data symbols, while conserving the transmission bandwidth. The third part considers training and precoding designs for OFDM under colored noise environment. The superiority of the proposed design over the previously-known design under colored noise is thoroughly demonstrated. The last part of the thesis develops the orthogonal affine precoder for spatially correlated MIMO-OFDM systems. The optimal superimposed training sequences are solved by tractable semi-definite programming. To have a better computational efficiency, two approximate design techniques are also presented. Furthermore, the non-redundancy precoder proposed in the third part is employed to combat channel correlation. As a result, the proposed designs are demonstrated to outperform other known designs in terms of channel estimation and data detection.

Visible light communication (VLC) is an emerging form of optical wireless communication that transmits data by modulating light in the visible spectrum. To meet the growing demand for wireless communication capacity from mobile devices, we investigate multiple-input multiple-output (MIMO) VLC to achieve multiplexing capacity gains and to allow multiple users to simultaneously transmit without disrupting each other. Previous approaches to receive VLC signals have either been unable to simultaneously receive multiple independent signals from multiple transmitters, unable to adapt to moving transmitters and receivers, or unable to sample the received signals fast enough for high-speed VLC. In this dissertation, we develop and evaluate two novel approaches to receive high-speed MIMO VLC signals from mobile transmitters that can be practically scaled to support additional transmitters. The first approach, Token-Based Pixel Selection (TBPS) exploits the redundancy and sparsity of high-resolution transmitter images in imaging VLC receivers to greatly increase the rate at which complementary metal-oxide semiconductor (CMOS) active pixel sensor (APS) image sensors can sample VLC signals though improved signal routing to enable such high-resolution image sensors to capture high-speed VLC signals. We further model the CMOS APS pixel as a linear shift-invariant system, investigate how it scales to support additional transmitters and higher resolutions, and investigate how noise can affect its performance. The second approach, a spatial light modulator (SLM)-based VLC receiver, uses an SLM to dynamically control the resulting wireless channel matrix to enable relatively few photodetectors to reliably receive from multiple transmitters despite their movements. As part of our analysis, we develop a MIMO VLC channel capacity model that accounts for the non-negativity and peak-power constraints of VLC systems to evaluate the performance of the SLM VLC receiver and to facilitate the optimization of the channel matrix through the SLM.

Abstract Advanced multiple-input multiple-output (MIMO) transceiver structures which utilize the knowledge of channel state information (CSI) at the transmitter side to optimize certain link parameters (e.g., throughput, fairness, spectral efficiency, etc.) under different constraints (e.g., maximum transmitted power, minimum quality of services (QoS), etc.) are considered in this thesis. Adaptive transmission schemes for point-to-point MIMO systems are considered first. A robust link adaptation method for time-division duplex systems employing MIMO-OFDM channel eigenmode based transmission is developed. A low complexity bit and power loading algorithm which requires low signaling overhead is proposed. Two algorithms for computing the sum-capacity of MIMO downlink channels with full CSI knowledge are derived. The first one is based on the iterative waterfilling method. The convergence of the algorithm is proved analytically and the computer simulations show that the algorithm converges faster than the earlier variants of sum power constrained iterative waterfilling algorithms. The second algorithm is based on the dual decomposition method. By tracking the instantaneous error in the inner loop, a faster version is developed. The problem of linear transceiver design in MIMO downlink channels is considered for a case when the full CSI of scheduled users only is available at the transmitter. General methods for joint power control and linear transmit and receive beamformers design are provided. The proposed algorithms can handle multiple antennas at the base station and at the mobile terminals with an arbitrary number of data streams per scheduled user. The optimization criteria are fairly general and include sum power minimization under the minimum signal-to-interference-plus-noise ratio (SINR) constraint per data stream, the balancing of SINR values among data streams, minimum SINR maximization, weighted sum-rate maximization, and weighted sum mean square error minimization. Besides the traditional sum power constraint on the transmit beamformers, multiple sum power constraints can be imposed on arbitrary subsets of the transmit antennas.This extends the applicability of the results to novel system architectures, such as cooperative base station transmission using distributed MIMO antennas. By imposing per antenna power constraints, issues related to the linearity of the power amplifiers can be handled as well. The original linear transceiver design problems are decomposed as a series of remarkably simpler optimization problems which can be efficiently solved by using standard convex optimization techniques. The advantage of this approach is that it can be easily extended to accommodate various supplementary constraints such as upper and/or lower bounds for the SINR values and guaranteed QoS for different subsets of users. The ability to handle transceiver optimization problems where a network-centric objective (e.g., aggregate throughput or transmitted power) is optimized subject to user-centric constraints (e.g., minimum QoS requirements) is an important feature which must be supported by future broadband communication systems.