abstract: The ability to identify unoccupied resources in the radio spectrum is a key capability for opportunistic users in a cognitive radio environment. This paper draws upon and extends geometrically based ideas in statistical signal processing to develop estimators for the rank and the occupied subspace in a multi-user environment from multiple temporal samples of the signal received at a single antenna. These estimators enable identification of resources, such as the orthogonal complement of the occupied subspace, that may be exploitable by an opportunistic user. This concept is supported by simulations showing the estimation of the number of users in a simple CDMA system using a maximum a posteriori (MAP) estimate for the rank. It was found that with suitable parameters, such as high SNR, sufficient number of time epochs and codes of appropriate length, the number of users could be correctly estimated using the MAP estimator even when the noise variance is unknown. Additionally, the process of identifying the maximum likelihood estimate of the orthogonal projector onto the unoccupied subspace is discussed. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2014

The cognitive radio (CR) concept is expected to be adopted along with many technologies to meet the requirements of the next generation of wireless and mobile systems, the 5G. Consequently, it is important to determine the performance of the CR systems with respect to these requirements. In this thesis, after briefly describing the 5G requirements, we present three main directions in which we aim to enhance the CR performance. The first direction is the reliability. We study the achievable rate of a multiple-input multiple-output (MIMO) relay-assisted CR under two scenarios; an unmanned aerial vehicle (UAV) one-way relaying (OWR) and a fixed two-way relaying (TWR). We propose special linear precoding schemes that enable the secondary user (SU) to take advantage of the primary-free channel eigenmodes. We study the SU rate sensitivity to the relay power, the relay gain, the UAV altitude, the number of antennas and the line of sight availability. The second direction is the scalability. We first study a multiple access channel (MAC) with multiple SUs scenario. We propose a particular linear precoding and SUs selection scheme maximizing their sum-rate. We show that the proposed scheme provides a significant sum-rate improvement as the number of SUs increases. Secondly, we expand our scalability study to cognitive cellular networks. We propose a low-complexity algorithm for base station activation/deactivation and dynamic spectrum management maximizing the profits of primary and secondary networks subject to green constraints. We show that our proposed algorithms achieve performance close to those obtained with the exhaustive search method. The third direction is the energy efficiency (EE). We present a novel power allocation scheme based on maximizing the EE of both single-input and single-output (SISO) and MIMO systems. We solve a non-convex problem and derive explicit expressions of the corresponding optimal power. When the instantaneous channel is not available, we present a simple sub-optimal power that achieves a near-optimal EE. The simulations show that the sub-optimal solution is very close to the optimal one. In the MIMO case, we show that adopting more antennas is more energy efficient.

This thesis deals with wireless channels in uncorrelated block fading environment with Rayleigh distribution. All nodes are assumed to be oblivious to their forward channel gains; however, they have perfect information about their backward channel gains. We also assume a stringent decoding delay constraint of one fading block that makes the definition of ergodic (Shannon) capacity meaningless. In this thesis, we focus on two different systems. In each case, the throughput and expected-rate are analyzed. First, the point-to-point multiple-antenna channel is investigated in chapter 2. We prove that in multiple-input single-output (MISO) channels, the optimum transmission strategy maximizing the throughput is to use all available antennas and perform equal power allocation with uncorrelated signals. Furthermore, to increase the expected-rate, multilayer coding (the broadcast approach) is applied. Analogously, we establish that sending uncorrelated signals and performing equal power allocation across all available antennas at each layer is optimum. A closed form expression for the maximum continuous-layer expected-rate of MISO channels is also obtained. Moreover, we investigate multiple-input multiple-output (MIMO) channels, and formulate the maximum throughput in the asymptotically low and high SNR regimes and also asymptotically large number of transmit or receive antennas by obtaining the optimum transmit covariance matrix. Furthermore, a distributed antenna system, wherein two single-antenna transmitters want to transmit a common message to a single-antenna receiver, is considered. It is shown that this system has the same outage probability and hence, throughput and expected-rate, as a point-to-point 2x1 MISO channel. In chapter 3, the problem of dual-hop transmission from a single-antenna source to a single-antenna destination via two parallel full-duplex single-antenna relays under the above assumptions is investigated. The focus of this chapter is on simple, efficient, and practical relaying schemes to increase the throughput and expected-rate at the destination. For this purpose, various combinations of relaying protocols and multi-layer coding are proposed. For the decode-forward (DF) relaying, the maximum finite-layer expected-rate as well as two upper-bounds on the continuous-layer expected-rate are obtained. The main feature of the proposed DF scheme is that the layers being decoded at both relays are added coherently at the destination although each relay has no information about the number of layers being successfully decoded by the other relay. It is proved that the optimum coding scheme is transmitting uncorrelated signals via the relays. Next, the maximum expected-rate of ON/OFF based amplify-forward (AF) relaying is analytically formulated. For further performance improvement, a hybrid decode-amplify-forward (DAF) relaying strategy, adopting multi-layer coding at the source and relays, is proposed and its maximum throughput and finite-layer expected-rate are presented. Moreover, the maximum throughput and expected-rate in the compress-forward (CF) relaying adopting multi-layer coding, using optimal quantizers and Wyner-Ziv compression at the relays, are fully derived. All theoretical results are illustrated by numerical simulations. As it turns out from the results, when the ratio of the relay power to the source power is high, the CF relaying outperforms DAF (and hence outperforms both DF and AF relaying); otherwise, DAF scheme is superior.

This thesis deals with wireless channels in uncorrelated block fading environment with Rayleigh distribution. All nodes are assumed to be oblivious to their forward channel gains; however, they have perfect information about their backward channel gains. We also assume a stringent decoding delay constraint of one fading block that makes the definition of ergodic (Shannon) capacity meaningless. In this thesis, we focus on two different systems. In each case, the throughput and expected-rate are analyzed. First, the point-to-point multiple-antenna channel is investigated in chapter 2. We prove that in multiple-input single-output (MISO) channels, the optimum transmission strategy maximizing the throughput is to use all available antennas and perform equal power allocation with uncorrelated signals. Furthermore, to increase the expected-rate, multilayer coding (the broadcast approach) is applied. Analogously, we establish that sending uncorrelated signals and performing equal power allocation across all available antennas at each layer is optimum. A closed form expression for the maximum continuous-layer expected-rate of MISO channels is also obtained. Moreover, we investigate multiple-input multiple-output (MIMO) channels, and formulate the maximum throughput in the asymptotically low and high SNR regimes and also asymptotically large number of transmit or receive antennas by obtaining the optimum transmit covariance matrix. Furthermore, a distributed antenna system, wherein two single-antenna transmitters want to transmit a common message to a single-antenna receiver, is considered. It is shown that this system has the same outage probability and hence, throughput and expected-rate, as a point-to-point 2x1 MISO channel. In chapter 3, the problem of dual-hop transmission from a single-antenna source to a single-antenna destination via two parallel full-duplex single-antenna relays under the above assumptions is investigated. The focus of this chapter is on simple, efficient, and practical relaying schemes to increase the throughput and expected-rate at the destination. For this purpose, various combinations of relaying protocols and multi-layer coding are proposed. For the decode-forward (DF) relaying, the maximum finite-layer expected-rate as well as two upper-bounds on the continuous-layer expected-rate are obtained. The main feature of the proposed DF scheme is that the layers being decoded at both relays are added coherently at the destination although each relay has no information about the number of layers being successfully decoded by the other relay. It is proved that the optimum coding scheme is transmitting uncorrelated signals via the relays. Next, the maximum expected-rate of ON/OFF based amplify-forward (AF) relaying is analytically formulated. For further performance improvement, a hybrid decode-amplify-forward (DAF) relaying strategy, adopting multi-layer coding at the source and relays, is proposed and its maximum throughput and finite-layer expected-rate are presented. Moreover, the maximum throughput and expected-rate in the compress-forward (CF) relaying adopting multi-layer coding, using optimal quantizers and Wyner-Ziv compression at the relays, are fully derived. All theoretical results are illustrated by numerical simulations. As it turns out from the results, when the ratio of the relay power to the source power is high, the CF relaying outperforms DAF (and hence outperforms both DF and AF relaying); otherwise, DAF scheme is superior.

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.

Next-generation cellular systems will rely on millimeter wave (mmWave) bands to meet the increasing demand for wireless connectivity from end user equipment. Given large available bandwidth and small-sized antenna elements, mmWave frequencies can support high communication rates and facilitate the use of multiple-input-multiple-output (MIMO) techniques to increase the wireless capacity. However, the small wavelength of mmWave yields severe path loss and high channel uncertainty. Meanwhile, using a large number of antenna elements requires a high energy consumption and heavy communication overhead for MIMO transmissions and channel measurement. To facilitate efficient mmWave communications, in this dissertation, the challenges of energy efficiency and communication overhead are addressed. First, the use of unmanned aerial vehicle (UAV), intelligent signal reflector, and device-to-device (D2D) communications are investigated to improve the reliability and energy efficiency of mmWave communications in face of blockage. Next, to reduce the communication overhead, new channel modeling and user localization approaches are developed to facilitate MIMO channel estimation by providing prior knowledge of mmWave links. Using advance mathematical tools from machine learning (ML), game theory, and communication theory, this dissertation develops a suite of novel frameworks using which mmWave communication networks can be reliably deployed and operated in wireless cellular systems, UAV networks, and wearable device networks. For UAV-based wireless communications, a learning framework is developed to predict the cellular data traffic during congestion events, and a new framework for the on-demand deployment of UAVs is proposed to offload the excessive traffic from the ground base stations (BSs) to the UAVs. The results show that the proposed approach enables a dynamical and optimal deployment of UAVs that alleviates the cellular traffic congestion. Subsequently, a novel energy-efficient framework is developed to reflect mmWave signals from a BS towards mobile users using a UAV-carried intelligent reflector (IR). To optimize the location and reflection coefficient of the UAV-carried IR, a deep reinforcement learning (RL) approach is proposed to maximize the downlink transmission capacity. The results show that the RL-based approach significantly improves the downlink line-of-sight probability and increases the achievable data rate. Moreover, the channel estimation challenge for MIMO communications is addressed using a distributional RL approach, while optimizing an IR-aided downlink multi-user communication. The results show that the proposed method captures the statistic feature of MIMO channels, and significantly increases the downlink sum-rate. Moreover, in order to capture the characteristics of air-to-ground channels, a data-driven approach is developed, based on a distributed framework of generative adversarial networks, so that each UAV collects and shares mmWave channel state information (CSI) for cooperative channel modeling. The results show that the proposed algorithm enables an accurate channel modeling for mmWave MIMO communications over a large temporal-spatial domain. Furthermore, the CSI pattern is analyzed via semi-supervised ML tools to localize the wireless devices in the mmWave networks. Finally, to support D2D communications, a novel framework for mmWave multi-hop transmissions is investigated to improve the performance of the high-rate low-latency transmissions between wearable devices. In a nutshell, this dissertation provides analytical foundations on the ML-based performance optimization of mmWave communication systems, and the anticipated results provide rigorous guidelines for effective deployment of mmWave frequency bands into next-generation wireless systems (e.g., 6G). / Doctor of Philosophy / Different kinds of new smart devices are invented and deployed every year. Emerging smart city applications, including autonomous vehicles, virtual reality, drones, and Internet-of-things, will require the wireless communication system to support more data transmissions and connectivity. However, existing wireless network (e.g., 5G and Wi-Fi) operates at congested microwave frequency bands and cannot satisfy needs of these applications due to limited resources. Therefore, a different, very high frequency band at the millimeter wave (mmWave) spectrum becomes an inevitable choice to manage the exponential growth in wireless traffic for next-generation communication systems. With abundant bandwidth resources, mmWave frequencies can provide the high transmission rate and support the wireless connectivity for the massive number of devices in a smart city. Despite the advantages of communications at the mmWave bands, it is necessary to address the challenges related to high-frequency transmissions, such as low energy efficiency and unpredictable link states. To this end, this dissertation develops a set of novel network frameworks to facilitate the service deployment, performance analysis, and network optimization for mmWave communications. In particular, the proposed frameworks and efficient algorithms are tailored to the characteristics of mmWave propagation and satisfy the communication requirements of emerging smart city applications. Using advanced mathematical tools from machine learning, game theory, and wireless communications, this dissertation provides a comprehensive understanding of the communication performance over mmWave frequencies in the cellular systems, wireless local area networks, and drone networks. The anticipated results will promote the deployment of mmWave frequencies in next-generation communication systems.

The reconfigurable aperture (RECAP) is a reconfigurable antenna consisting of a dense array of electronically controlled elements, which can be manipulated to support many antenna functions within a single architecture. RECAPs are explored herein as an enabling technology for future software defined and cognitive radio architectures, as well as compact wireless devices supporting many bands and services. First, the concept of a parasitic RECAP is developed and analyzed for various communication applications. This begins with the analysis of existing RECAP topologies (e.g. planar and parasitic) using a hybrid method combining full wave simulations and network analysis. Next, a performance versus complexity analysis is performed to assess the use of a parasitic RECAP for the most critical communications functions: pattern synthesis, MIMO communications and physical-layer wireless security. To verify simulation results, a prototype parasitic RECAP is also built and deployed in real propagation environments. Given the potential of adaptive and reconfigurable architectures for providing enhanced security, an idealized reconfigurable antenna is analyzed, resulting in the concept of secure array synthesis. The objective is to find optimal array beamforming for secure communication in the presence of a passive eavesdropper in a static line-of-sight (LOS) channel. The method is then extended to the case of multipath propagation environments. The problem is solved by casting it into the form of a semi-definite program, which can be solved with convex optimization. The method is general and can be applied to an arbitrary array topology with or without antenna mutual-coupling. Due to complexity of the problem, initial attention has been restricted to idealized reconfigurable antennas (smart antennas), where excitation amplitude and phase at each element can be controlled independently. Lastly, reconfigurable antennas are investigated as a solution to support the emerging application of over-the-air (OTA) testing in a low-cost and compact way, resulting in the concept of the reconfigurable over-the-air chamber (ROTAC). First, an idealized two-dimensional ROTAC is analyzed, revealing that the fading distribution, spatial correlation, frequency selectivity, and multipath angular spectrum can be controlled by proper specification of the random loads. Later, a prototype of ROTAC is built to study the fading statistics and angular characteristics of the multipath fields inside a practical chamber.

In this thesis, different aspects of lattice-based precoding and decoding for the transmission of digital and analog data over MIMO fading channels are investigated: 1) Lattice-based precoding in MIMO broadcast systems: A new viewpoint for adopting the lattice reduction in communication over MIMO broadcast channels is introduced. Lattice basis reduction helps us to reduce the average transmitted energy by modifying the region which includes the constellation points. The new viewpoint helps us to generalize the idea of lattice-reduction-aided precoding for the case of unequal-rate transmission, and obtain analytic results for the asymptotic behavior of the symbol-error-rate for the lattice-reduction-aided precoding and the perturbation technique. Also, the outage probability for both cases of fixed-rate users and fixed sum-rate is analyzed. It is shown that the lattice-reduction-aided method, using LLL algorithm, achieves the optimum asymptotic slope of symbol-error-rate (called the precoding diversity). 2) Lattice-based decoding in MIMO multiaccess systems and MIMO point-to-point systems: Diversity order and diversity-multiplexing tradeoff are two important measures for the performance of communication systems over MIMO fading channels. For the case of MIMO multiaccess systems (with single-antenna transmitters) or MIMO point-to-point systems with V-BLAST transmission scheme, it is proved that lattice-reduction-aided decoding achieves the maximum receive diversity (which is equal to the number of receive antennas). Also, it is proved that the naive lattice decoding (which discards the out-of-region decoded points) achieves the maximum diversity in V-BLAST systems. On the other hand, the inherent drawbacks of the naive lattice decoding for general MIMO fading systems is investigated. It is shown that using the naive lattice decoding for MIMO systems has considerable deficiencies in terms of the diversity-multiplexing tradeoff. Unlike the case of maximum-likelihood decoding, in this case, even the perfect lattice space-time codes which have the non-vanishing determinant property can not achieve the optimal diversity-multiplexing tradeoff. 3) Lattice-based analog transmission over MIMO fading channels: The problem of finding a delay-limited schemes for sending an analog source over MIMO fading channels is investigated in this part. First, the problem of robust joint source-channel coding over an additive white Gaussian noise channel is investigated. A new scheme is proposed which achieves the optimal slope for the signal-to-distortion-ratio (SDR) curve (unlike the previous known coding schemes). Then, this idea is extended to MIMO channels to construct lattice-based codes for joint source-channel coding over MIMO channels. Also, similar to the diversity-multiplexing tradeoff, the asymptotic performance of MIMO joint source-channel coding schemes is characterized, and a concept called diversity-fidelity tradeoff is introduced in this thesis.

In this thesis, different aspects of lattice-based precoding and decoding for the transmission of digital and analog data over MIMO fading channels are investigated: 1) Lattice-based precoding in MIMO broadcast systems: A new viewpoint for adopting the lattice reduction in communication over MIMO broadcast channels is introduced. Lattice basis reduction helps us to reduce the average transmitted energy by modifying the region which includes the constellation points. The new viewpoint helps us to generalize the idea of lattice-reduction-aided precoding for the case of unequal-rate transmission, and obtain analytic results for the asymptotic behavior of the symbol-error-rate for the lattice-reduction-aided precoding and the perturbation technique. Also, the outage probability for both cases of fixed-rate users and fixed sum-rate is analyzed. It is shown that the lattice-reduction-aided method, using LLL algorithm, achieves the optimum asymptotic slope of symbol-error-rate (called the precoding diversity). 2) Lattice-based decoding in MIMO multiaccess systems and MIMO point-to-point systems: Diversity order and diversity-multiplexing tradeoff are two important measures for the performance of communication systems over MIMO fading channels. For the case of MIMO multiaccess systems (with single-antenna transmitters) or MIMO point-to-point systems with V-BLAST transmission scheme, it is proved that lattice-reduction-aided decoding achieves the maximum receive diversity (which is equal to the number of receive antennas). Also, it is proved that the naive lattice decoding (which discards the out-of-region decoded points) achieves the maximum diversity in V-BLAST systems. On the other hand, the inherent drawbacks of the naive lattice decoding for general MIMO fading systems is investigated. It is shown that using the naive lattice decoding for MIMO systems has considerable deficiencies in terms of the diversity-multiplexing tradeoff. Unlike the case of maximum-likelihood decoding, in this case, even the perfect lattice space-time codes which have the non-vanishing determinant property can not achieve the optimal diversity-multiplexing tradeoff. 3) Lattice-based analog transmission over MIMO fading channels: The problem of finding a delay-limited schemes for sending an analog source over MIMO fading channels is investigated in this part. First, the problem of robust joint source-channel coding over an additive white Gaussian noise channel is investigated. A new scheme is proposed which achieves the optimal slope for the signal-to-distortion-ratio (SDR) curve (unlike the previous known coding schemes). Then, this idea is extended to MIMO channels to construct lattice-based codes for joint source-channel coding over MIMO channels. Also, similar to the diversity-multiplexing tradeoff, the asymptotic performance of MIMO joint source-channel coding schemes is characterized, and a concept called diversity-fidelity tradeoff is introduced in this thesis.