A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO

Employing low resolution analog-to-digital converters in massive multiple-input multiple-output (MIMO) has many advantages in terms of total power consumption, cost and feasibility of such systems. However, such advantages come together with significant challenges in channel estimation and data detection due to the severe quantization noise present. In this study, we propose a novel iterative receiver for quantized uplink single carrier MIMO (SC-MIMO) utilizing an efficient message passing algorithm based on the Bussgang decomposition and Ungerboeck factorization, which avoids the use of a complex whitening filter. A reduced state sequence estimator with bidirectional decision feedback is also derived, achieving remarkable complexity reduction compared to the existing receivers for quantized SC-MIMO in the literature, without any requirement on the sparsity of the transmission channel. Moreover, the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO under frequency-selective channel, which do not require any cyclic-prefix overhead, is also derived. We observe that the proposed receiver has significant performance gains with respect to the existing receivers in the literature under imperfect channel state information.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Asymptotic Task-Based Quantization with Application to Massive MIMO

Quantizers take part in nearly every digital signal processing system which operates on physical signals. They are commonly designed to accurately represent the underlying signal, regardless of the specific task to be performed on the quantized data. In systems working with high-dimensional signals, such as massive multiple-input multiple-output (MIMO) systems, it is beneficial to utilize low-resolution quantizers, due to cost, power, and memory constraints. In this work we study quantization of high-dimensional inputs, aiming at improving performance under resolution constraints by accounting for the system task in the quantizers design. We focus on the task of recovering a desired signal statistically related to the high-dimensional input, and analyze two quantization approaches: We first consider vector quantization, which is typically computationally infeasible, and characterize the optimal performance achievable with this approach. Next, we focus on practical systems which utilize hardware-limited scalar uniform analog-to-digital converters (ADCs), and design a task-based quantizer under this model. The resulting system accounts for the task by linearly combining the observed signal into a lower dimension prior to quantization. We then apply our proposed technique to channel estimation in massive MIMO networks. Our results demonstrate that a system utilizing low-resolution scalar ADCs can approach the optimal channel estimation performance by properly accounting for the task in the system design

Variational channel estimation with tempering: An artificial intelligence algorithm for wireless intelligent networks

This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Network

Quasi-Synchronous Random Access for Massive MIMO-Based LEO Satellite Constellations

Low earth orbit (LEO) satellite constellation-enabled communication networks are expected to be an important part of many Internet of Things (IoT) deployments due to their unique advantage of providing seamless global coverage. In this paper, we investigate the random access problem in massive multiple-input multiple-output-based LEO satellite systems, where the multi-satellite cooperative processing mechanism is considered. Specifically, at edge satellite nodes, we conceive a training sequence padded multi-carrier system to overcome the issue of imperfect synchronization, where the training sequence is utilized to detect the devices' activity and estimate their channels. Considering the inherent sparsity of terrestrial-satellite links and the sporadic traffic feature of IoT terminals, we utilize the orthogonal approximate message passing-multiple measurement vector algorithm to estimate the delay coefficients and user terminal activity. To further utilize the structure of the receive array, a two-dimensional estimation of signal parameters via rotational invariance technique is performed for enhancing channel estimation. Finally, at the central server node, we propose a majority voting scheme to enhance activity detection by aggregating backhaul information from multiple satellites. Moreover, multi-satellite cooperative linear data detection and multi-satellite cooperative Bayesian dequantization data detection are proposed to cope with perfect and quantized backhaul, respectively. Simulation results verify the effectiveness of our proposed schemes in terms of channel estimation, activity detection, and data detection for quasi-synchronous random access in satellite systems.Comment: 38 pages, 16 figures. This paper has been accepted by IEEE JSAC SI on 3GPP Technologies: 5G-Advanced and Beyond. Copyright may be transferred without notice, after which this version may no longer be accessibl

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