108 research outputs found
Sparsity in the Delay-Doppler Domain for Measured 60 GHz Vehicle-to-Infrastructure Communication Channels
We report results from millimeter wave vehicle-to-infrastructure (V2I)
channel measurements conducted on Sept. 25, 2018 in an urban street
environment, down-town Vienna, Austria. Measurements of a frequency-division
multiplexed multiple-input single-output channel have been acquired with a
time-domain channel sounder at 60 GHz with a bandwidth of 100 MHz and a
frequency resolution of 5 MHz. Two horn antennas were used on a moving
transmitter vehicle: one horn emitted a beam towards the horizon and the second
horn emitted an elevated beam at 15-degrees up-tilt. This configuration was
chosen to assess the impact of beam elevation on V2I communication channel
characteristics: propagation loss and sparsity of the local scattering function
in the delay-Doppler domain. The measurement results within urban speed limits
show high sparsity in the delay-Doppler domain.Comment: submitted to IEEE International Conference on Communication
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
Variable Earns Profit: Improved Adaptive Channel Estimation using Sparse VSS-NLMS Algorithms
Accurate channel estimation is essential for broadband wireless
communications. As wireless channels often exhibit sparse structure, the
adaptive sparse channel estimation algorithms based on normalized least mean
square (NLMS) have been proposed, e.g., the zero-attracting NLMS (ZA-NLMS)
algorithm and reweighted zero-attracting NLMS (RZA-NLMS). In these NLMS-based
algorithms, the step size used to iteratively update the channel estimate is a
critical parameter to control the estimation accuracy and the convergence speed
(so the computational cost). However, invariable step-size (ISS) is usually
used in conventional algorithms, which leads to provide performance loss or/and
low convergence speed as well as high computational cost. To solve these
problems, based on the observation that large step size is preferred for fast
convergence while small step size is preferred for accurate estimation, we
propose to replace the ISS by variable step size (VSS) in conventional
NLMS-based algorithms to improve the adaptive sparse channel estimation in
terms of bit error rate (BER) and mean square error (MSE) metrics. The proposed
VSS-ZA-NLMS and VSS-RZA-NLMS algorithms adopt VSS, which can be adaptive to the
estimation error in each iteration, i.e., large step size is used in the case
of large estimation error to accelerate the convergence speed, while small step
size is used when the estimation error is small to improve the steady-state
estimation accuracy. Simulation results are provided to validate the
effectiveness of the proposed scheme.Comment: 6 pages, 9 figures, submitted for ICC201
Joint Channel Estimation Algorithm via Weighted Homotopy for Massive MIMO OFDM System
Massive (or large-scale) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is widely acknowledged as a key technology for future communication. One main challenge to implement this system in practice is the high dimensional channel estimation, where the large number of channel matrix entries requires prohibitively high computational complexity. To solve this problem efficiently, a channel estimation approach using few number of pilots is necessary. In this paper, we propose a weighted Homotopy based channel estimation approach which utilizes the sparse nature in MIMO channels to achieve a decent channel estimation performance with much less pilot overhead. Moreover, inspired by the fact that MIMO channels are observed to have approximately common support in a neighborhood, an information exchange strategy based on the proposed approach is developed to further improve the estimation accuracy and reduce the required number of pilots through joint channel estimation. Compared with the traditional sparse channel estimation methods, the proposed approach can achieve more than 2dB gain in terms of mean square error (MSE) with the same number of pilots, or achieve the same performance with much less pilots
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