1,507 research outputs found
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
Wavelet Based Semi-blind Channel Estimation For Multiband OFDM
This paper introduces an expectation-maximization (EM) algorithm within a
wavelet domain Bayesian framework for semi-blind channel estimation of
multiband OFDM based UWB communications. A prior distribution is chosen for the
wavelet coefficients of the unknown channel impulse response in order to model
a sparseness property of the wavelet representation. This prior yields, in
maximum a posteriori estimation, a thresholding rule within the EM algorithm.
We particularly focus on reducing the number of estimated parameters by
iteratively discarding ``unsignificant'' wavelet coefficients from the
estimation process. Simulation results using UWB channels issued from both
models and measurements show that under sparsity conditions, the proposed
algorithm outperforms pilot based channel estimation in terms of mean square
error and bit error rate and enhances the estimation accuracy with less
computational complexity than traditional semi-blind methods
Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study
In this paper, preamble-based least squares (LS) channel estimation in OFDM
systems of the QAM and offset QAM (OQAM) types is considered, in both the
frequency and the time domains. The construction of optimal (in the mean
squared error (MSE) sense) preambles is investigated, for both the cases of
full (all tones carrying pilot symbols) and sparse (a subset of pilot tones,
surrounded by nulls or data) preambles. The two OFDM systems are compared for
the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also
includes the power spent for CP transmission. OFDM/OQAM, with a sparse preamble
consisting of equipowered and equispaced pilots embedded in zeros, turns out to
perform at least as well as CP-OFDM. Simulations results are presented that
verify the analysis
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