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

Multi-dimensional Channel Parameter Estimation for mmWave Cylindrical Arrays

University of Technology Sydney. Faculty of Engineering and Information Technology.Millimeter-wave (mmWave) large-scale antenna arrays, standardized for the fifth-generation (5G) communication networks, have the potential to estimate channel parameters with unprecedented accuracy, due to their high temporal resolution and excellent directivity. However, most existing techniques have very high complexities in hardware and software, and they cannot effectively exploit the properties of mmWave large-array systems for channel estimation. As a result, their application in 5G mmWave large array systems is limited in practice. This thesis develops new and efficient solutions to channel parameter estimation using large-scale mmWave uniform cylindrical arrays (UCyAs). The key contributions of this thesis are on the following four aspects: We first present a channel compression-based channel estimation method, which reduces the computational complexity substantially at a negligible cost of estimation accuracy. By capitalizing on the sparsity of mmWave channel, the method effectively filters out the useless signal components. As a result, the dimension of the element space of the received signals can be reduced. Next, we extend the channel estimation to the hybrid UCyA case, and design new hybrid beamformers. By exploiting the convergence property of the Bessel function, the designed beamformers can preserve the recurrence relationship of the received signals with a small number of radio frequency (RF) chains. We then arrange the received signals in a tensor form and propose a new tensor-based channel estimation algorithm. By suppressing the receiver noises in all dimensions (time, frequency, and space), the algorithm can achieve substantially higher estimation accuracy than existing matrix-based techniques. Finally, to reduce cost and power consumption while maintaining a high network access capability, we develop a novel nested hybrid UCyA and present the corresponding parameter estimation algorithm based on the second-order channel statistics. Simulation results show that by exploiting the sparse array technique to design the RF chain connection network, the angles of a large number of devices can be accurately estimated with much fewer RF chains than antennas. Overall, this thesis presents several applicable UCyA design schemes and proposes the efficient channel parameter estimation algorithms. The presented new UCyAs can significantly reduce the hardware cost of the system with a marginal accuracy loss, and the proposed algorithms are capable of accurately estimating the channel parameters with low computational complexities. By employing the presented UCyAs and implementing the proposed novel algorithms cohesively, the different communication and deployment requirements of a variety of mmWave communication scenarios can be met

Gradient Pursuit-Based Channel Estimation for MmWave Massive MIMO Systems with One-Bit ADCs

In this paper, channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs) is considered. In the mmWave band, the number of propagation paths is small, which results in sparse virtual channels. To estimate sparse virtual channels based on the maximum a posteriori (MAP) criterion, sparsity-constrained optimization comes into play. In general, optimizing objective functions with sparsity constraints is NP-hard because of their combinatorial complexity. Furthermore, the coarse quantization of one-bit ADCs makes channel estimation a challenging task. In the field of compressed sensing (CS), the gradient support pursuit (GraSP) and gradient hard thresholding pursuit (GraHTP) algorithms were proposed to approximately solve sparsity-constrained optimization problems iteratively by pursuing the gradient of the objective function via hard thresholding. The accuracy guarantee of these algorithms, however, breaks down when the objective function is ill-conditioned, which frequently occurs in the mmWave band. To prevent the breakdown of gradient pursuit-based algorithms, the band maximum selecting (BMS) technique, which is a hard thresholder selecting only the "band maxima," is applied to GraSP and GraHTP to propose the BMSGraSP and BMSGraHTP algorithms in this paper.Comment: to appear in PIMRC 2019, Istanbul, Turke

Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach

Channel estimation in reconfigurable intelligent surfaces (RIS)-aided systems is crucial for optimal configuration of the RIS and various downstream tasks such as user localization. In RIS-aided systems, channel estimation involves estimating two channels for the user-RIS (UE-RIS) and RIS-base station (RIS-BS) links. In the literature, two approaches are proposed: (i) cascaded channel estimation where the two channels are collapsed into a single one and estimated using training signals at the BS, and (ii) separate channel estimation that estimates each channel separately either in a passive or semi-passive RIS setting. In this work, we study the separate channel estimation problem in a fully passive RIS-aided millimeter-wave (mmWave) single-user single-input multiple-output (SIMO) communication system. First, we adopt a variational-inference (VI) approach to jointly estimate the UE-RIS and RIS-BS instantaneous channel state information (I-CSI). In particular, auxiliary posterior distributions of the I-CSI are learned through the maximization of the evidence lower bound. However, estimating the I-CSI for both links in every coherence block results in a high signaling overhead to control the RIS in scenarios with highly mobile users. Thus, we extend our first approach to estimate the slow-varying statistical CSI of the UE-RIS link overcoming the highly variant I-CSI. Precisely, our second method estimates the I-CSI of RIS-BS channel and the UE-RIS channel covariance matrix (CCM) directly from the uplink training signals in a fully passive RIS-aided system. The simulation results demonstrate that using maximum a posteriori channel estimation using the auxiliary posteriors can provide a capacity that approaches the capacity with perfect CSI

Bayesian Beamforming for Mobile Millimeter Wave Channel Tracking in the Presence of DOA Uncertainty

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.This work was in part supported by the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2020ZT012), Beijing Jiaotong University and China Railway Corporation (Contract No. N2019G028). This article was presented in part at the 2019 IEEE GLOBECOM’19. The associate editor coordinating the review of this article and approving it for publication was O. Oyman. (Corresponding author: Yan Yang.) Yan Yang is with the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, Chin

Near-Field Communications: A Comprehensive Survey

Multiple-antenna technologies are evolving towards large-scale aperture sizes, extremely high frequencies, and innovative antenna types. This evolution is giving rise to the emergence of near-field communications (NFC) in future wireless systems. Considerable attention has been directed towards this cutting-edge technology due to its potential to enhance the capacity of wireless networks by introducing increased spatial degrees of freedom (DoFs) in the range domain. Within this context, a comprehensive review of the state of the art on NFC is presented, with a specific focus on its 1) fundamental operating principles, 2) channel modeling, 3) performance analysis, 4) signal processing, and 5) integration with other emerging technologies. Specifically, 1) the basic principles of NFC are characterized from both physics and communications perspectives, unveiling its unique properties in contrast to far-field communications. 2) Based on these principles, deterministic and stochastic near-field channel models are investigated for spatially-discrete (SPD) and continuous-aperture (CAP) antenna arrays. 3) Rooted in these models, existing contributions on near-field performance analysis are reviewed in terms of DoFs/effective DoFs (EDoFs), power scaling law, and transmission rate. 4) Existing signal processing techniques for NFC are systematically surveyed, encompassing channel estimation, beamforming design, and low-complexity beam training. 5) Major issues and research opportunities associated with the integration of NFC and other emerging technologies are identified to facilitate NFC applications in next-generation networks. Promising directions are highlighted throughout the paper to inspire future research endeavors in the realm of NFC.Comment: 56 pages, 23figures; submit for possible journa