356 research outputs found
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model
For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the
terahertz (THz)-band has been envisioned as one of the key enabling
technologies for the sixth generation networks. However, the acquisition of the
THz channel entails several unique challenges such as severe path loss and
beam-split. Prior works usually employ ultra-massive arrays and additional
hardware components comprised of time-delayers to compensate for these loses.
In order to provide a cost-effective solution, this paper introduces a
sparse-Bayesian-learning (SBL) technique for joint channel and beam-split
estimation. Specifically, we first model the beam-split as an array
perturbation inspired from array signal processing. Next, a low-complexity
approach is developed by exploiting the line-of-sight-dominant feature of THz
channel to reduce the computational complexity involved in the proposed SBL
technique for channel estimation (SBCE). Additionally, based on
federated-learning, we implement a model-free technique to the proposed
model-based SBCE solution. Further to that, we examine the near-field
considerations of THz channel, and introduce the range-dependent near-field
beam-split. The theoretical performance bounds, i.e., Cram\'er-Rao lower
bounds, are derived both for near- and far-field parameters, e.g., user
directions, beam-split and ranges. Numerical simulations demonstrate that SBCE
outperforms the existing approaches and exhibits lower hardware cost.Comment: Accepted Paper in IEEE Open Journal of Communications Societ
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 Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications
Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers
vast spatial degrees of freedom, has emerged as a potentially pivotal enabling
technology for the sixth generation (6G) of wireless mobile networks. With its
growing significance, both opportunities and challenges are concurrently
manifesting. This paper presents a comprehensive survey of research on XL-MIMO
wireless systems. In particular, we introduce four XL-MIMO hardware
architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array
(UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and
continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss
their characteristics and interrelationships. Following this, we examine exact
and approximate near-field channel models for XL-MIMO. Given the distinct
electromagnetic properties of near-field communications, we present a range of
channel models to demonstrate the benefits of XL-MIMO. We further motivate and
discuss low-complexity signal processing schemes to promote the practical
implementation of XL-MIMO. Furthermore, we explore the interplay between
XL-MIMO and other emergent 6G technologies. Finally, we outline several
compelling research directions for future XL-MIMO wireless communication
systems.Comment: 38 pages, 10 figure
Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?
Hybrid analog/digital MIMO architectures were recently proposed as an
alternative for fully-digitalprecoding in millimeter wave (mmWave) wireless
communication systems. This is motivated by the possible reduction in the
number of RF chains and analog-to-digital converters. In these architectures,
the analog processing network is usually based on variable phase shifters. In
this paper, we propose hybrid architectures based on switching networks to
reduce the complexity and the power consumption of the structures based on
phase shifters. We define a power consumption model and use it to evaluate the
energy efficiency of both structures. To estimate the complete MIMO channel, we
propose an open loop compressive channel estimation technique which is
independent of the hardware used in the analog processing stage. We analyze the
performance of the new estimation algorithm for hybrid architectures based on
phase shifters and switches. Using the estimated, we develop two algorithms for
the design of the hybrid combiner based on switches and analyze the achieved
spectral efficiency. Finally, we study the trade-offs between power
consumption, hardware complexity, and spectral efficiency for hybrid
architectures based on phase shifting networks and switching networks.
Numerical results show that architectures based on switches obtain equal or
better channel estimation performance to that obtained using phase shifters,
while reducing hardware complexity and power consumption. For equal power
consumption, all the hybrid architectures provide similar spectral
efficiencies.Comment: Submitted to IEEE Acces
Can Far-field Beam Training Be Deployed for Cross-field Beam Alignment in Terahertz UM-MIMO Communications?
Ultra-massive multiple-input multiple-output (UM-MIMO) is the enabler of
Terahertz (THz) communications in next-generation wireless networks. In THz
UM-MIMO systems, a new paradigm of cross-field communications spanning from
near-field to far-field is emerging, since the near-field range expands with
higher frequencies and larger array apertures. Precise beam alignment in
cross-field is critical but challenging. Specifically, unlike far-field beams
that rely only on the angle domain, the incorporation of dual-domain (angle and
distance) training significantly increases overhead. A natural question arises
of whether far-field beam training can be deployed for cross-field beam
alignment. In this paper, this question is answered, by demonstrating that the
far-field training enables sufficient signal-to-noise ratio (SNR) in both far-
and near-field scenarios, while exciting all channel dimensions. Based on that,
we propose a subarray-coordinated hierarchical (SCH) training with greatly
reduced overhead. To further obtain high-precision beam designs, we propose a
two-phase angle and distance beam estimator (TPBE). Extensive simulations
demonstrate the effectiveness of the proposed methods. Compared to near-field
exhaustive search, the SCH possesses 0.2\% training overhead. The TPBE achieves
0.01~degrees and 0.02~m estimation root-mean-squared errors for angle and
distance. Furthermore, with the estimated beam directions, a near-optimal SNR
with 0.11~dB deviation is attained after beam alignment
Atomic Norm decomposition for sparse model reconstruction applied to positioning and wireless communications
This thesis explores the recovery of sparse signals, arising in the wireless communication and radar system fields, via atomic norm decomposition. Particularly, we
focus on compressed sensing gridless methodologies, which avoid the always existing
error due to the discretization of a continuous space in on-grid methods. We define
the sparse signal by means of a linear combination of so called atoms defined in a
continuous parametrical atom set with infinite cardinality. Those atoms are fully
characterized by a multi-dimensional parameter containing very relevant information
about the application scenario itself. Also, the number of composite atoms is
much lower than the dimension of the problem, which yields sparsity. We address
a gridless optimization solution enforcing sparsity via atomic norm minimization to
extract the parameters that characterize the atom from an observed measurement
of the model, which enables model recovery. We also study a machine learning approach to estimate the number of composite atoms that construct the model, given
that in certain scenarios this number is unknown.
The applications studied in the thesis lay on the field of wireless communications,
particularly on MIMO mmWave channels, which due to their natural properties can
be modeled as sparse. We apply the proposed methods to positioning in automotive
pulse radar working in the mmWave range, where we extract relevant information
such as angle of arrival (AoA), distance and velocity from the received echoes of
objects or targets. Next we study the design of a hybrid precoder for mmWave
channels which allows the reduction of hardware cost in the system by minimizing
as much as possible the number of required RF chains. Last, we explore full channel
estimation by finding the angular parameters that model the channel. For all
the applications we provide a numerical analysis where we compare our proposed
method with state-of-the-art techniques, showing that our proposal outperforms the
alternative methods.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Pablo Martínez Olmos.- Vocal: David Luengo Garcí
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
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