63 research outputs found
Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications
We consider the problem of channel estimation and joint active and passive
beamforming for reconfigurable intelligent surface (RIS) assisted millimeter
wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency
division multiplexing (OFDM) systems. We show that, with a well-designed
frame-based training protocol, the received pilot signal can be organized into
a low-rank third-order tensor that admits a canonical polyadic decomposition
(CPD). Based on this observation, we propose two CPD-based methods for
estimating the cascade channels associated with different subcarriers. The
proposed methods exploit the intrinsic low-rankness of the CPD formulation,
which is a result of the sparse scattering characteristics of mmWave channels,
and thus have the potential to achieve a significant training overhead
reduction. Specifically, our analysis shows that the proposed methods have a
sample complexity that scales quadratically with the sparsity of the cascade
channel. Also, by utilizing the singular value decomposition-like structure of
the effective channel, this paper develops a joint active and passive
beamforming method based on the estimated cascade channels. Simulation results
show that the proposed CPD-based channel estimation methods attain mean square
errors that are close to the Cramer-Rao bound (CRB) and present a clear
advantage over the compressed sensing-based method. In addition, the proposed
joint beamforming method can effectively utilize the estimated channel
parameters to achieve superior beamforming performance.Comment: arXiv admin note: text overlap with arXiv:2203.1616
Low-Rank Channel Estimation for Millimeter Wave and Terahertz Hybrid MIMO Systems
Massive multiple-input multiple-output (MIMO) is one of the fundamental technologies for 5G and beyond. The increased number of antenna elements at both the transmitter and the receiver translates into a large-dimension channel matrix. In addition, the power requirements for the massive MIMO systems are high, especially when fully digital transceivers are deployed. To address this challenge, hybrid analog-digital transceivers are considered a viable alternative. However, for hybrid systems, the number of observations during each channel use is reduced. The high dimensions of the channel matrix and the reduced number of observations make the channel estimation task challenging. Thus, channel estimation may require increased training overhead and higher computational complexity.
The need for high data rates is increasing rapidly, forcing a shift of wireless communication towards higher frequency bands such as millimeter Wave (mmWave) and terahertz (THz). The wireless channel at these bands is comprised of only a few dominant paths. This makes the channel sparse in the angular domain and the resulting channel matrix has a low rank. This thesis aims to provide channel estimation solutions benefiting from the low rankness and sparse nature of the channel. The motivation behind this thesis is to offer a desirable trade-off between training overhead and computational complexity while providing a desirable estimate of the channel
A survey on 5G massive MIMO Localization
Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field
Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications
Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave
(mmWave) and terahertz (THz) systems to achieve both coverage and capacity
enhancement, where the design of hybrid precoders, combiners, and the IRS
typically relies on channel state information. In this paper, we address the
problem of uplink wideband channel estimation for IRS aided multiuser
multiple-input single-output (MISO) systems with hybrid architectures.
Combining the structure of model driven and data driven deep learning
approaches, a hybrid driven learning architecture is devised for joint
estimation and learning the properties of the channels. For a passive IRS aided
system, we propose a residual learned approximate message passing as a model
driven network. A denoising and attention network in the data driven network is
used to jointly learn spatial and frequency features. Furthermore, we design a
flexible hybrid driven network in a hybrid passive and active IRS aided system.
Specifically, the depthwise separable convolution is applied to the data driven
network, leading to less network complexity and fewer parameters at the IRS
side. Numerical results indicate that in both systems, the proposed hybrid
driven channel estimation methods significantly outperform existing deep
learning-based schemes and effectively reduce the pilot overhead by about 60%
in IRS aided systems.Comment: 30 pages, 8 figures, submitted to IEEE transactions on wireless
communications on December 13, 202
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Millimeter wave MIMO communications : high-resolution angle acquisition and low-resolution time-frequency synchronization
Knowledge of the propagation channel is critical to exploit the full benefit of multiple-input multiple-output (MIMO) techniques in millimeter wave (mmWave) cellular systems. Obtaining accurate channel state information in mmWave systems, however, is challenging due to high estimation overhead, high computational complexity and on-grid setting. It is also desirable to reduce the analog-to-digital converters (ADCs) resolution at mmWave frequencies to reduce power consumption and implementation costs. The use of low-precision ADCs, though, brings new design challenges to practical cellular networks.
In the first part of this dissertation, we develop several new methods to estimate and track the mmWave channel's angle-of-departure and angle-of-arrival with high accuracy and low overhead. The key ingredient of the proposed strategies is custom designed beam pairs, from which there exists an invertible function of the angle to be estimated. We further extend the proposed algorithms to dual-polarized MIMO in wideband channels, and angle tracking design for fast-varying environments. We derive analytical angle estimation error performance of the proposed methods in single-path channels. We also use numerical examples to characterize the robustness of the proposed approaches to various transceiver settings and channel conditions.
In the second part of this dissertation, we focus on improving the low-resolution time-frequency synchronization performance for mmWave cellular systems. In our system model, the base station uses analog beams to send the synchronization signal with infinite-resolution digital-to-analog converters (DACs). The user equipment employs a fully digital front end to detect the synchronization signal with low-resolution ADCs. For low-resolution timing synchronization, we propose a new multi-beam probing based strategy, targeting at maximizing the minimum received synchronization signal-to-quantization-plus-noise ratio among all serving users. Regarding low-resolution frequency synchronization, we construct new sequences for carrier frequency offset (CFO) estimation and compensation. We use both analytical and numerical examples to show that the proposed sequences and the corresponding metrics used for retrieving the CFOs are robust to the quantization distortion.Electrical and Computer Engineerin
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