As various of wireless techniques have been proposed to achieve fast and efficient data communication, it’s becoming increasingly important to protect wireless communications from being undermined by adversaries. A secure and reliable wireless physical layer design is essential and critical to build a solid foundation for upper layer applications. This dissertation present two works that explore the physical layer features to secure wireless communications towards the data confidentiality and user authentication. The first work builds a reliable wireless communication system to enforce the location restricted service access control. In particular, the work proposes a novel technique named pinpoint waveforming to deliver the services to users at eligible locations only. The second work develops a secure far proximity identification approach that can determine whether a remote device is far away, thus preventing potential spoofing attacks in long-haul wireless communications. This dissertation lastly describes some future work efforts, designing a light-weight encryption scheme to facilitate sensitive data encryption for applications which cannot support expensive cryptography encryption operations such as IoT devices.

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A wide range of contemporary technologies leveraging ubiquitous mobile phones have addressed the challenge of transportation mode recognition, which involves identifying how users move about, such as walking, cycling, driving a car, or taking a bus. This problem has found applications in various areas, including smart city transportation, carbon footprint calculation, and context-aware mobile assistants. Previous research has primarily focused on recognizing mobility modes using GPS and motion sensor data from smartphones. However, these approaches often necessitate the installation of specialized mobile applications on users’ devices to collect sensor data, resulting in power inefficiency and privacy concerns. In this study, we tackle these issues by presenting a user-independent system capable of distinguishing four forms of locomotion—walking, bus, car, and train—solely based on mobile data (4G) from smartphones. Our system was developed using data collected in three diverse locations (Mekkah, Jeddah, KAUST) in the Kingdom of Saudi Arabia. The underlying concept is to correlate phone speed with features extracted from Channel State Information (CSI), which includes information about Physical Cell ID, received signal strength, and other relevant data. The feature extraction process involves utilizing sliding windows over both the time and frequency domains. By employing statistical classification and boosting techniques, we achieved remarkable F-scores of 85%, 95%, 88%, and 70% for the car, bus, walking, and train modes, respectively. Moreover, we conducted an analysis of the handover rate in a one-tier network and compared the analytical results with real data. This investigation provided novel insights into the influence of transportation modes on handover rate, revealing the correlation between different modes of mobility and network connectivity. This work sets the stage for the development of more efficient and privacy-friendly solutions in transportation mode recognition and network optimization.

<p> We consider the design of the precoders for a multi-input multi-output (MIMO) communication system equipped with a decision feedback equalizer (DFE) receiver. For such design problems, perfect knowledge of the channel state information (CSI) at both the transmitter and the receiver is usually required. However, in the environment of wireless communications, it is often difficult to provide sufficiently timely and accurate feedback of CSI from the receiver to the transmitter for such designs to be practically viable.</p> <p> In this thesis, we consider the optimum precoder designs for a wireless communication link having M transmitter antennas and N receiver antennas (M < N), in which the channels are assumed to be flat fading and may be correlated. We assume that full knowledge of CSI is available at the receiver. At the transmitter, however, only the first- and second-order statistics of the channels are available. Our first goal is to come up with an efficient design of the optimal precoder for such a MIMO system by minimizing the average arithmetic mean-squared error (MSE) of zero-forcing (ZF) decision feedback detection subject to a constraint on the total transmission power. Applying some of the properties of the matrix parameters, this non-convex optimization problem can be transformed into a convex geometrical programming problem which can then be efficiently solved using an interior point method. The performance of the MIMO system equipped with this optimum precoder and a ZF-DFE has also been found to be comparable, and in some cases, superior to that of V-BLAST which necessitates optimally ordered successive interference cancellation based on the largest post-detection signal-to-noise ratio (SNR). In terms of trade-off between performance and implementation simplicity, the proposed system is certainly an attractive alternative.</p> <p> In addition, we also utilize these important properties of our system parameters to investigate an "inverse problem" of our first design. That is, we design another precoding matrix by minimizing the total transmission power of the MIMO communication system subject to a constraint on the average MSE. Also, a closed-form solution is derived when the channels are uncorrelated while simulation results for the minimum power precoder designs is given at the end of this thesis.</p> / Thesis / Master of Applied Science (MASc)

Over the last few decades, wireless networks have morphed from traditional cellular/wireless local area networks (WLAN), into a wide range of applications, such as the Internet-of-Things (IoT), vehicular-to-everything (V2X), and smart grid communication networks. This transition has been facilitated by research and development efforts in academia and industry, which has resulted in the standardization of fifth-generation (5G) wireless networks. To meet the performance requirements of these diverse use-cases, 5G networks demand higher performance in terms of data rate, latency, security, and reliability, etc. At the physical layer, these performance enhancements are achieved by (a) optimizing spectrum utilization shared amongst multiple technologies (termed as spectrum sharing), and (b) leveraging advanced spatial signal processing techniques using large antenna arrays (termed as massive MIMO). In this dissertation, we focus on enhancing the performance of next-generation vehicular communication and spectrum sharing systems. In the first contribution, we present a novel pilot configuration design and adaptation mechanism for cellular vehicular-to-everything (C-V2X) networks. Drawing inspiration from 4G and 5G standards, the proposed approach is based on limited feedback of indices from a codebook comprised of quantized channel statistics information. We demonstrate significant rate improvements using our proposed approach in terrestrial and air-to-ground (A2G) vehicular channels. In the second contribution, we demonstrate the occurrence of cellular link adaptation failure due to channel state information (CSI) contamination, because of coexisting pulsed radar signals that act as non-pilot interference. To mitigate this problem, we propose a low-complexity semi-blind SINR estimation scheme that is robust and accurate in a wide range of interference and noise conditions. We also propose a novel dual CSI feedback mechanism for cellular systems and demonstrate significant improvements in throughput, block error rate, and latency, when sharing spectrum with a pulsed radar. In the third contribution, we develop fundamental insights on underlay radar-massive MIMO spectrum sharing, using mathematical tools from stochastic geometry. We consider a multi-antenna radar system, sharing spectrum with a network of massive MIMO base stations distributed as a homogeneous Poisson Point Process (PPP) outside a circular exclusion zone centered around the radar. We propose a tractable analytical framework, and characterize the impact of worst-case downlink cellular interference on radar performance, as a function of key system parameters. The analytical formulation enables network designers to systematically isolate and evaluate the impact of each parameter on the worst-case radar performance and complements industry-standard simulation methodologies by establishing a baseline performance for each set of system parameters, for current and future radar-cellular spectrum sharing deployments. Finally, we highlight directions for future work to advance the research presented in this dissertation and discuss its broader impacts across the wireless industry, and policy-making. / Doctor of Philosophy / The impact of today's technologies has been magnified by wireless networks, due to the standardization and deployment of fifth-generation (5G) cellular networks. 5G promises faster data speeds, lower latency and higher user security, among other desirable features. This has made it capable of meeting the performance requirements of key infrastructure such as smart grid and mission-critical networks, and novel consumer applications such as smart home appliances, smart vehicles, and augmented/virtual reality. In part, these capabilities have been achieved by (a) better spectrum utilization among various wireless technologies (called spectrum sharing), and (b) serving multiple users on the same resource using large multi-antenna systems (called massive MIMO). In this dissertation, we make three contributions that enhance the performance of vehicular communications and spectrum sharing systems. In the first contribution, we present a novel scheme wherein a vehicular communication link adapts to the channel conditions by controlling the resource overhead in real-time, to improve spectral utilization of data resources. The proposed scheme enhances those of current 4G and 5G networks, which are based on limited feedback of quantized channel statistics, fed back from the receiver to the transmitter. In the second contribution, we show that conventional link adaptation methods fail when 4G/5G networks share spectrum with pulsed radars. To mitigate this problem, we develop a comprehensive signal processing framework, consisting of a hybrid SINR estimation method that is robust and accurate in a wide range of interference and noise conditions. Concurrently, we also propose a scheme to pass additional information that captures the channel conditions in the presence of radar interference, and analyze its performance in detail. In the third contribution, we focus on characterizing the impact of 5G cellular interference on a radar system in shared spectrum, using mathematical tools from stochastic geometry. We model the worst-case interference scenario, and study the impact of the system parameters on the worst-case radar performance. In summary, this dissertation advances the state-of-the-art in vehicular communications and spectrum sharing, through (a) novel contributions in protocol design and (b) development of mathematical tools for performance characterization.

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In a MIMO system, a transmitter with perfect knowledge of the underlying channel state information (CSI) can achieve a higher channel capacity compared to transmission without CSI. When reciprocity of the wireless channel does not hold, the identification and utilization of partial CSI at the transmitter are important issues. This thesis is focused on partial CSI acquisition and utilization techniques for MIMO channels. We propose a feedback algorithm for tracking the dominant channel subspaces for MIMO systems in a continuously time-varying environment. We exploit the correlation between channel states of adjacent time instants and quantize the variation of channel states. Specifically, we model a subspace as one point in a Grassmann manifold, treat the variations in principal right singular subspaces of the channel matrices as a piecewise-geodesic process in the Grassmann manifold, and quantize the velocity matrix of the geodesic. We design a complexity-constrained MIMO OFDM system where the transmitter has knowledge of channel correlations. The transmitter is constrained to perform at most one inverse Discrete Fourier Transform per OFDM symbol on the average. We show that in the MISO case, time domain beamforming can be used to do two-dimensional eigen-beamforming. For the MIMO case, we derive design criteria for the transmitter beamforming and receiver combining weighting vectors and show some suboptimal solutions. The feedback channel may have uncertainties such as unexpected delay or error. We consider channel mean feedback with an unknown delay and propose a broadcast approach that is able to adapt to the quality of the feedback. Having considered CSI feedback problems where the receiver tries to convey its attained CSI to the transmitter, we turn to noncoherent coding design for fast fading channels, where the receiver does not have reliable CSI. We propose a data-dependent superimposed training scheme to improve the performance of training based codes. The transmitter is equipped with multiple training sequences and dynamically selects a training sequence for each data sequence to minimize channel estimation error. The set of training sequences are optimized to minimize pairwise error probability between codewords.

Previous studies have demonstrated that many existing communication systems can be formulated within a unified multirate filterbank transceiver model. A redundant block transmission system implemented via this unified multirate filterbank transceiver model is usually known as a multiple-input-multiple-output (MIMO) system in literature. This dissertation devises an optimum linear block-based precoder and the corresponding equalizer for MIMO systems over perfect reconstruction (PR) channels by exploiting the proposed oblique projection framework. Particularly, two main criteria of interest in a digital communication link with limited transmission power are investigated, namely, average bit error rate (BER) minimization and mutual information rate maximization. The study framework is developed as follows. For a block-based precoder, a received signal model is formulated for the two redundancy schemes, viz., trailing-zeros (TZ) and cyclic-prefix (CP). By exploiting the property of oblique projection, a cascaded equalizer for block transmission systems (i.e., MIMO systems) is proposed and implemented with a scheme, in which the inter-block interference (IBI) is completely eliminated by the oblique projection and followed by a matrix degree of freedom for inter-symbol interference (ISI) equalization. With the available channel state information at the transmitter side, the matrix for ISI equalization of the cascaded equalizer is utilized to design an optimum linear block-based precoder, such that the BER is minimized (or the mutual information rate is maximized), subject to the ISI-free and the transmission power constraints. Accordingly, the cascaded equalizer with the ISI-free constraint yields a cascaded ZF equalizer. Theoretical derivations and simulation results confirm that the proposed framework not only retains identical BER and information rate performances to previous works for cases with sufficient redundancy, but also allows their results to be extended to the cases of insufficient redundancy.

The need for reliable, high-throughput, mobile wireless communication technologies has never been greater as increases in the demand for on-the-go access to information, entertainment, and other electronic services continues. Two such technologies, which are at the forefront of current research efforts, are orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) systems, their union being known simply as MIMO-OFDM. The successful performance of these technologies depends upon the availability of accurate information concerning the wireless communication channel. In this dissertation, several issues related to quality of this channel state information (CSI) are studied. Specifically, the first part of this dissertation considers the design of optimal pilot signals for OFDM systems. The optimization is addressed via lower bounds on the estimation error variance, which bounds are given by formulations of the Cram'{e}r-Rao bound (CRB). The second part of this dissertation uses the CRB once again, this time as a tool for evaluating the potential performance of MIMO-OFDM channel estimation and prediction. Bounds are found for several parametric time-varying wideband MIMO-OFDM channel models, and numerical evaluations of these bounds are used to illuminate several interesting features regarding the estimation and prediction of MIMO-OFDM channels. The final part of this dissertation considers the problem of MIMO multiplexing using SVD-based methods when only imperfect CSI is available. For this purpose, general per-MIMO-subchannel signal and interference-plus-noise power expressions are derived to quantify the effects of CSI imperfections, and these expressions are then used to find robust MIMO-SVD power and bit allocations which maintain good overall performance in spite of imperfect CSI.

Dans la première partie de la thèse, on se concentre sur la conception d'un système de communication par satellite complet se basant sur la construction de faisceaux adaptatifs aux terminaux mobiles. Comparé à la construction classique de faisceaux fixes, le système à faisceaux adaptatifs peut considérablement améliorer la capacité du système en termes du nombre de STs desservies et de l'efficacité énergétique. Pour la conception du système à faisceaux adaptatifs, les informations sur l'état de canal (CSI) sont essentielles. Vu que le temps de propagation est trop long par rapport au temps de cohérence du canal, le CSI instantané est déjà périmé lorsqu'il est reçu pour la construction des faisceaux. Cependant, une partie de l'information du canal, plus particulièrement, les vecteurs de directivité ont une variation assez lente. On utilise cette connaissance partielle du CSI pour concevoir le système à base de faisceaux adaptatifs. Afin d'estimer les vecteurs de directivité, on propose un algorithme basé sur un critère de minimisation de l'erreur quadratique. Puis, basées sur l'estimation des vecteurs de directivité, on présente deux approches heuristiques pour la conception des faisceaux. En outre, on propose également deux approches qui reposent sur l'estimation de la directivité pour la détection des STs et la résolution possible des collisions sur le canal d'accès aléatoire au satellite. Comme la performance du système SDMA dépend fortement des positions spatiales des STs co-existants, on propose deux algorithmes de faible complexité pour l'attribution des fréquences dans le système de communication par satellite / In the first part of the thesis, we focus on the design of a complete satellite communication system adopting adaptive beamforming with mobile satellite terminals. Compared with conventional fixed beamforming, adaptive beamforming can signi_cantly improve the capacity of a satellite system in terms of served satellite terminals (ST) and power e_ciency. For the design of an adaptive beamforming system, channel state information (CSI) is critical. Since the propagation delay is too long compared to the coherence time of the channel, the instantaneous CSI is already stale when processed for beamforming. However, some parts of the channel, more speci_cally, directivity vectors change quite slowly. We utilize this partial knowledge of CSI to design an adaptive beamforming system. In order to estimate the directivity vectors, we propose an algorithm based on a least square error criterion. Then, based on the estimation of directivity vectors, we propose two heuristics approaches to the design of adaptive beamforming. Additionally, we also propose two approaches, based on directivity estimation for the detection of transmitting terminals and the possible resolution of collisions in the random access channel of the satellite system. Since SDMA system performance depends strongly on the spatial locations of co-existing terminals, we also propose two low complexity algorithms for frequency allocation in a satellite communication system. Finally, we simulate a complete satellite system, including a random access channel and a connection-oriented channel. We analyze the system performance and compare it to conventional fixed beamforming systems