279 research outputs found
Estimation of Sparse MIMO Channels with Common Support
We consider the problem of estimating sparse communication channels in the
MIMO context. In small to medium bandwidth communications, as in the current
standards for OFDM and CDMA communication systems (with bandwidth up to 20
MHz), such channels are individually sparse and at the same time share a common
support set. Since the underlying physical channels are inherently
continuous-time, we propose a parametric sparse estimation technique based on
finite rate of innovation (FRI) principles. Parametric estimation is especially
relevant to MIMO communications as it allows for a robust estimation and
concise description of the channels. The core of the algorithm is a
generalization of conventional spectral estimation methods to multiple input
signals with common support. We show the application of our technique for
channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink
(Walsh-Hadamard coded schemes). In the presence of additive white Gaussian
noise, theoretical lower bounds on the estimation of SCS channel parameters in
Rayleigh fading conditions are derived. Finally, an analytical spatial channel
model is derived, and simulations on this model in the OFDM setting show the
symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR)
compared to standard non-parametric methods - e.g. lowpass interpolation.Comment: 12 pages / 7 figures. Submitted to IEEE Transactions on Communicatio
Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems
Channel estimation for the downlink of frequency division duplex (FDD)
massive MIMO systems is well known to generate a large overhead as the amount
of training generally scales with the number of transmit antennas in a MIMO
system. In this paper, we consider the solution of extrapolating the channel
frequency response from uplink pilot estimates to the downlink frequency band,
which completely removes the training overhead. We first show that conventional
estimators fail to achieve reasonable accuracy. We propose instead to use
high-resolution channel estimation. We derive theoretical lower bounds (LB) for
the mean squared error (MSE) of the extrapolated channel. Assuming that the
paths are well separated, the LB is simplified in an expression that gives
considerable physical insight. It is then shown that the MSE is inversely
proportional to the number of receive antennas while the extrapolation
performance penalty scales with the square of the ratio of the frequency offset
and the training bandwidth. The channel extrapolation performance is validated
through numeric simulations and experimental measurements taken in an anechoic
chamber. Our main conclusion is that channel extrapolation is a viable solution
for FDD massive MIMO systems if accurate system calibration is performed and
favorable propagation conditions are present.Comment: arXiv admin note: substantial text overlap with arXiv:1902.0684
Channel Prediction for Mobile MIMO Wireless Communication Systems
Temporal variation and frequency selectivity of wireless channels constitute
a major drawback to the attainment of high gains in capacity
and reliability offered by multiple antennas at the transmitter and receiver
of a mobile communication system. Limited feedback and adaptive transmission
schemes such as adaptive modulation and coding, antenna selection,
power allocation and scheduling have the potential to provide the platform
of attaining the high transmission rate, capacity and QoS requirements in
current and future wireless communication systems. Theses schemes require
both the transmitter and receiver to have accurate knowledge of Channel
State Information (CSI). In Time Division Duplex (TDD) systems, CSI at
the transmitter can be obtained using channel reciprocity. In Frequency Division
Duplex (FDD) systems, however, CSI is typically estimated at the
receiver and fed back to the transmitter via a low-rate feedback link. Due to
the inherent time delays in estimation, processing and feedback, the CSI obtained
from the receiver may become outdated before its actual usage at the
transmitter. This results in significant performance loss, especially in high
mobility environments. There is therefore a need to extrapolate the varying
channel into the future, far enough to account for the delay and mitigate the
performance degradation.
The research in this thesis investigates parametric modeling and prediction
of mobile MIMO channels for both narrowband and wideband systems.
The focus is on schemes that utilize the additional spatial information offered
by multiple sampling of the wave-field in multi-antenna systems to
aid channel prediction. The research has led to the development of several
algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient
methods for the extrapolation of narrowband MIMO channels are proposed.
Various extensions were also developed. These include methods for wideband
channels, transmission using polarized antenna arrays, and mobile-to-mobile
systems.
Performance bounds on the estimation and prediction error are vital when
evaluating channel estimation and prediction schemes. For this purpose, analytical
expressions for bound on the estimation and prediction of polarized
and non-polarized MIMO channels are derived. Using the vector formulation
of the Cramer Rao bound for function of parameters, readily interpretable
closed-form expressions for the prediction error bounds were found for cases
with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The
derived performance bounds are very simple and so provide insight into system
design.
The performance of the proposed algorithms was evaluated using standardized
channel models. The effects of the temporal variation of multipath
parameters on prediction is studied and methods for jointly tracking the
channel parameters are developed. The algorithms presented can be utilized
to enhance the performance of limited feedback and adaptive MIMO
transmission schemes
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
Sixth-generation (6G) mobile communication networks are expected to have
dense infrastructures, large-dimensional channels, cost-effective hardware,
diversified positioning methods, and enhanced intelligence. Such trends bring
both new challenges and opportunities for the practical design of 6G. On one
hand, acquiring channel state information (CSI) in real time for all wireless
links becomes quite challenging in 6G. On the other hand, there would be
numerous data sources in 6G containing high-quality location-tagged channel
data, making it possible to better learn the local wireless environment. By
exploiting such new opportunities and for tackling the CSI acquisition
challenge, there is a promising paradigm shift from the conventional
environment-unaware communications to the new environment-aware communications
based on the novel approach of channel knowledge map (CKM). This article aims
to provide a comprehensive tutorial overview on environment-aware
communications enabled by CKM to fully harness its benefits for 6G. First, the
basic concept of CKM is presented, and a comparison of CKM with various
existing channel inference techniques is discussed. Next, the main techniques
for CKM construction are discussed, including both the model-free and
model-assisted approaches. Furthermore, a general framework is presented for
the utilization of CKM to achieve environment-aware communications, followed by
some typical CKM-aided communication scenarios. Finally, important open
problems in CKM research are highlighted and potential solutions are discussed
to inspire future work
From Data Inferring to Physics Representing: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE
In this paper, we propose an innovative learning-based channel prediction
scheme so as to achieve higher prediction accuracy and reduce the requirements
of huge amount and strict sequential format of channel data. Inspired by the
idea of the neural ordinary differential equation (Neural ODE), we first prove
that the channel prediction problem can be modeled as an ODE problem with a
known initial value through analyzing the physical process of electromagnetic
wave propagation within a varying space. Then, we design a novel
physics-inspired spatial channel gradient network (SCGNet), which represents
the derivative process of channel varying as a special neural network and can
obtain the gradients at any relative displacement needed for the ODE solving.
With the SCGNet, the static channel at any location served by the base station
is accurately inferred through consecutive propagation and integration.
Finally, we design an efficient recurrent positioning algorithm based on some
prior knowledge of user mobility to obtain the velocity vector, and propose an
approximate Doppler compensation method to make up the instantaneous
angular-delay domain channel. Only discrete historical channel data is needed
for the training, whereas only a few fresh channel measurements is needed for
the prediction, which ensures the scheme's practicability
A Two-Stage 2D Channel Extrapolation Scheme for TDD 5G NR Systems
Recently, channel extrapolation has been widely investigated in frequency
division duplex (FDD) massive MIMO systems. However, in time division duplex
(TDD) fifth generation (5G) new radio (NR) systems, the channel extrapolation
problem also arises due to the hopping uplink pilot pattern, which has not been
fully researched yet. This paper addresses this gap by formulating a channel
extrapolation problem in TDD massive MIMO-OFDM systems for 5G NR, incorporating
imperfection factors. A novel two-stage two-dimensional (2D) channel
extrapolation scheme in both frequency and time domain is proposed, designed to
mitigate the negative effects of imperfection factors and ensure high-accuracy
channel estimation. Specifically, in the channel estimation stage, we propose a
novel multi-band and multi-timeslot based high-resolution parameter estimation
algorithm to achieve 2D channel extrapolation in the presence of imperfection
factors. Then, to avoid repeated multi-timeslot based channel estimation, a
channel tracking stage is designed during the subsequent time instants, in
which a sparse Markov channel model is formulated to capture the dynamic
sparsity of massive MIMO-OFDM channels under the influence of imperfection
factors. Next, an expectation-maximization (EM) based compressive channel
tracking algorithm is designed to jointly estimate unknown imperfection and
channel parameters by exploiting the high-resolution prior information of the
delay/angle parameters from the previous timeslots. Simulation results
underscore the superior performance of our proposed channel extrapolation
scheme over baselines
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