Analysis of Wireless Networks With Massive Connectivity

Recent years have witnessed unprecedented growth in wireless networks in terms of both data traffic and number of connected devices. How to support this fast increasing demand for high data traffic and connectivity is a key consideration in the design of future wireless communication systems. With this motivation, in this thesis, we focus on the analysis of wireless networks with massive connectivity. In the first part of the thesis, we seek to improve the energy efficiency (EE) of single-cell massive multiple-input multiple-output (MIMO) networks with joint antenna selection and user scheduling. We propose a two-step iterative procedure to maximize the EE. In each iteration, bisection search and random selection are used first to determine a subset of antennas with the users selected before, and then identify the EE-optimal subset of users with the selected antennas via cross entropy algorithm. Subsequently, we focus on the joint uplink and downlink EE maximization, under a limitation on the number of available radio frequency (RF) chains. With the Jensen\u27s inequality and the power consumption model, the original problem is converted into a combinatorial optimization problem. Utilizing the learning-based stochastic gradient descent framework and the rare event simulation method, we propose an efficient learning-based stochastic gradient descent algorithm to solve the corresponding combinatorial optimization problem. In the second part of the thesis, we focus on the joint activity detection and channel estimation in cell-free massive MIMO systems with massive connectivity. At first, we conduct an asymptotic analysis of single measurement vector (SMV) based minimum mean square error (MMSE) estimation in cell-free massive MIMO systems with massive connectivity. We establish a decoupling principle of SMV based MMSE estimation for sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components. Subsequently, using the decoupling principle, likelihood ratio test and the optimal fusion rule, we obtain detection rules for the activity of users based on the received pilot signals at only one access point (AP), and also based on the cooperation of the received pilot signals from the entire set of APs for centralized and distributed detection. Moreover, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO systems. In the third part, we focus on the performance analysis of intelligent reflecting surface (IRS) assisted wireless networks. Initially, we investigate the MMSE channel estimation for IRS assisted wireless communication systems. Then, we study the sparse activity detection problem in IRS assisted wireless networks. Specifically, employing the generalized approximate message passing (GAMP) algorithm, we obtain the MMSE estimates of the equivalent effective channel coefficients from the base station (BS) to all users, and transform the received pilot signals into additive Gaussian noise corrupted versions of the equivalent effective channel coefficients. Likelihood ratio test is used to acquire decisions on the activity of each user based on the Gaussian noise corrupted equivalent effective channel coefficients, and the optimal fusion rule is used to obtain the final decisions on the activity of all users based on the previous decisions on the activity of each user and the corresponding reliabilities. Finally, we conduct an asymptotic analysis of maximizing the weighted sum rate by joint beamforming and power allocation under transmit power and quality-of-service (QoS) constraints in IRS assisted wireless networks

Distributed Detection in Wireless Sensor Networks under Multiplicative Fading via Generalized Score-tests

In this paper, we address the problem of distributed detection of a non-cooperative (unknown emitted signal) target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an (unknown) deterministic signal with attenuation depending on the unknown distance between the sensor and the target, multiplicative fading, and additive Gaussian noise. To model energy-constrained operations within Internet of Things (IoT), one-bit sensor measurement quantization is employed and two strategies for quantization are investigated. The Fusion Center (FC) receives sensor bits via noisy Binary Symmetric Channels (BSCs) and provides a more accurate global inference. Such a model leads to a test with nuisances (i.e. the target position xT) observable only under H1 hypothesis. Davies framework is exploited herein to design the generalized forms of Rao and Locally-Optimum Detection (LOD) tests. For our generalized Rao and LOD approaches, a heuristic approach for threshold-optimization is also proposed. Simulation results confirm the promising performance of our proposed approaches.acceptedVersio

Neural-network-aided automatic modulation classification

Automatic modulation classification (AMC) is a pattern matching problem which significantly impacts divers telecommunication systems, with significant applications in military and civilian contexts alike. Although its appearance in the literature is far from novel, recent developments in machine learning technologies have triggered an increased interest in this area of research. In the first part of this thesis, an AMC system is studied where, in addition to the typical point-to-point setup of one receiver and one transmitter, a second transmitter is also present, which is considered an interfering device. A convolutional neural network (CNN) is used for classification. In addition to studying the effect of interference strength, we propose a modification attempting to leverage some of the debilitating results of interference, and also study the effect of signal quantisation upon classification performance. Consequently, we assess a cooperative setting of AMC, namely one where the receiver features multiple antennas, and receives different versions of the same signal from the single-antenna transmitter. Through the combination of data from different antennas, it is evidenced that this cooperative approach leads to notable performance improvements over the established baseline. Finally, the cooperative scenario is expanded to a more complicated setting, where a realistic geographic distribution of four receiving nodes is modelled, and furthermore, the decision-making mechanism with regard to the identity of a signal resides in a fusion centre independent of the receivers, connected to them over finite-bandwidth backhaul links. In addition to the common concerns over classification accuracy and inference time, data reduction methods of various types (including “trained” lossy compression) are implemented with the objective of minimising the data load placed upon the backhaul links.Open Acces

One-bit Compressed Sensing in the Presence of Noise

Many modern real-world systems generate large amounts of high-dimensional data stressing the available computing and signal processing systems. In resource-constrained settings, it is desirable to process, store and transmit as little amount of data as possible. It has been shown that one can obtain acceptable performance for tasks such as inference and reconstruction using fewer bits of data by exploiting low-dimensional structures on data such as sparsity. This dissertation investigates the signal acquisition paradigm known as one-bit compressed sensing (one-bit CS) for signal reconstruction and parameter estimation. We first consider the problem of joint sparse support estimation with one-bit measurements in a distributed setting. Each node observes sparse signals with the same but unknown support. The goal is to minimize the probability of error of support estimation. First, we study the performance of maximum likelihood (ML) estimation of the support set from one-bit compressed measurements when all these measurements are available at the fusion center. We provide a lower bound on the number of one-bit measurements required per node for vanishing probability of error. Though the ML estimator is optimal, its computational complexity increases exponentially with the signal dimension. So, we propose computationally tractable algorithms in a centralized setting. Further, we extend these algorithms to a decentralized setting where each node can communicate only with its one-hop neighbors. The proposed method shows excellent estimation performance even in the presence of noise. In the second part of the dissertation, we investigate the problem of sparse signal reconstruction from noisy one-bit compressed measurements using a signal that is statistically dependent on the compressed signal as an aid. We refer to this signal as side-information. We consider a generalized measurement model of one-bit CS where noise is assumed to be added at two stages of the measurement process- a) before quantizationand b) after quantization. We model the noise before quantization as additive white Gaussian noise and the noise after quantization as a sign-flip noise generated from a Bernoulli distribution. We assume that the SI at the receiver is noisy. The noise in the SI can be either in the support or in the amplitude, or both. This nature of the noise in SI suggests that the noise has a sparse structure. We use additive independent and identically distributed Laplacian noise to model such sparse nature of the noise. In this setup, we develop tractable algorithms that approximate the minimum mean square error (MMSE) estimator of the signal. We consider the following three different SI-based scenarios: 1. The side-information is assumed to be a noisy version of the signal. The noise is independent of the signal and follows the Laplacian distribution. We do not assume any temporal dependence in the signal.2. The signal exhibits temporal dependencies between signals at the current time instant and the previous time instant. The temporal dependence is modeled using the birth-death-drift (BDD) model. The side-information is a noisy version of the previous time instant signal, which is statistically dependent on the signal as defined by the BDD model. 3. The SI available at the receiver is heterogeneous. The signal and side-information are from different modalities and may not share joint sparse representation. We assume that the SI and the sparse signal are dependent and use the Copula function to model the dependence. In each of these scenarios, we develop generalized approximate message passing-based algorithms to approximate the minimum mean square error estimate. Numerical results show the effectiveness of the proposed algorithm. In the final part of the dissertation, we propose two one-bit compressed sensing reconstruction algorithms that use a deep neural network as a prior on the signal. In the first algorithm, we use a trained Generative model such as Generative Adversarial Networks and Variational Autoencoders as a prior. This trained network is used to reconstruct the compressed signal from one-bit measurements by searching over its range. We provide theoretical guarantees on the reconstruction accuracy and sample complexity of the presented algorithm. In the second algorithm, we investigate an untrained neural network architecture so that it acts as a good prior on natural signals such as images and audio. We formulate an optimization problem to reconstruct the signal from one-bit measurements using this untrained network. We demonstrate the superior performance of the proposed algorithms through numerical results. Further, in contrast to competing model-based algorithms, we demonstrate that the proposed algorithms estimate both direction and magnitude of the compressed signal from one-bit measurements

Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks

Distributed inference arising in sensor networks has been an interesting and promising discipline in recent years. The goal of this dissertation is to investigate several issues related to distributed inference in sensor networks, emphasizing parameter estimation and target tracking with resource-constrainted networks. To reduce the transmissions between sensors and the fusion center thereby saving bandwidth and energy consumption in sensor networks, a novel methodology, where each local sensor performs a censoring procedure based on the normalized innovation square (NIS), is proposed for the sequential Bayesian estimation problem in this dissertation. In this methodology, each sensor sends only the informative measurements and the fusion center fuses both missing measurements and received ones to yield more accurate inference. The new methodology is derived for both linear and nonlinear dynamic systems, and both scalar and vector measurements. The relationship between the censoring rule based on NIS and the one based on Kullback-Leibler (KL) divergence is investigated. A probabilistic transmission model over multiple access channels (MACs) is investigated. With this model, a relationship between the sensor management and compressive sensing problems is established, based on which, the sensor management problem becomes a constrained optimization problem, where the goal is to determine the optimal values of probabilities that each sensor should transmit with such that the determinant of the Fisher information matrix (FIM) at any given time step is maximized. The performance of the proposed compressive sensing based sensor management methodology in terms of accuracy of inference is investigated. For the Bayesian parameter estimation problem, a framework is proposed where quantized observations from local sensors are not directly fused at the fusion center, instead, an additive noise is injected independently to each quantized observation. The injected noise performs as a low-pass filter in the characteristic function (CF) domain, and therefore, is capable of recoverving the original analog data if certain conditions are satisfied. The optimal estimator based on the new framework is derived, so is the performance bound in terms of Fisher information. Moreover, a sub-optimal estimator, namely, linear minimum mean square error estimator (LMMSE) is derived, due to the fact that the proposed framework theoretically justifies the additive noise modeling of the quantization process. The bit allocation problem based on the framework is also investigated. A source localization problem in a large-scale sensor network is explored. The maximum-likelihood (ML) estimator based on the quantized data from local sensors and its performance bound in terms of Cram\\u27{e}r-Rao lower bound (CRLB) are derived. Since the number of sensors is large, the law of large numbers (LLN) is utilized to obtain a closed-form version of the performance bound, which clearly shows the dependence of the bound on the sensor density, $i.e.,$ the Fisher information is a linearly increasing function of the sensor density. Error incurred by the LLN approximation is also theoretically analyzed. Furthermore, the design of sub-optimal local sensor quantizers based on the closed-form solution is proposed. The problem of on-line performance evaluation for state estimation of a moving target is studied. In particular, a compact and efficient recursive conditional Posterior Cram\\u27{e}r-Rao lower bound (PCRLB) is proposed. This bound provides theoretical justification for a heuristic one proposed by other researchers in this area. Theoretical complexity analysis is provided to show the efficiency of the proposed bound, compared to the existing bound

Some contributions on MIMO radar

Motivated by recent advances in Multiple Input Multiple Output (MIMO) wireless communications, this dissertation aims at exploring the potential of MIMO approaches in the radar context. In communications, MIMO systems combat the fading effects of the multi-path channel with spatial diversity. Further, the scattering environment can be used by such systems to achieve spatial multiplexing. In radar, a complex target consisting of several scatterers takes the place of the multi-path channel of the communication problem. A target\u27s radar cross section (RCS), which determines the amount of returned power, greatly varies with the considered aspect. Those variations significantly impair the detection and estimation performance of conventional radar employing closely spaced arrays on transmit and receive sides. In contrast, by widely separating the transmit and receive elements, MIMO radar systems observe a target simultaneously from different aspects resulting in spatial diversity. This diversity overcomes the fluctuations in received power. Similar to the multiplexing gain in communications, the simultaneous observation of a target from several perspectives enables resolving its features with an accuracy beyond the one supported by the bandwidth. The dissertation studies the MIMO concept in radar in the following manner. First, angle of arrival estimation is explored for a system applying transmit diversity on the transmit side. Due to the target\u27s RCS fluctuations, the notion of ergodic and outage Cramer Rao bounds is introduced. Both bounds are compared with simulation results revealing the diversity potentials of MIMO radar. Afterwards, the detection of targets in white Gaussian noise is discussed including geometric considerations due to the wide separation between the system elements. The detection performance of MIMO radar is then compared to the one achieved by conventional phased array radar systems. The discussion is extended to include returns from homogeneous clutter. A Doppler processing based moving target detector for MIMO radar is developed in this context. Based on this detector, the moving target detection capabilities of MIMO radar are evaluated and compared to the ones of phased array and multi-static radar systems. It is shown, that MIMO radar is capable of reliably detecting targets moving in an arbitrary direction. The advantage of using several transmitters is illustrated and the constant false alarm rate (CFAR) property of adaptive MIMO moving target detectors is demonstrated. Finally, the high resolution capabilities of MIMO radar are explored. As noted above, the several individual scatterers constituting a target result in its fluctuating RCS. The high resolution mode is aimed at resolving those scatterers. With Cramer Rao bounds and simulation results, it is explored how observing a single isotropic scatterer from several aspects enhances the accuracy of estimating the location of this scatterer. In this context a new, two-dimensional ambiguity function is introduced. This ambiguity function is used to illustrate that several scatterers can be resolved within a conventional resolution cell defined by the bandwidth. The effect of different system parameters on this ambiguity function is discussed