26 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 Parametric and Non-Parametric MIMO-OFDM Channel Estimation Schemes
A parametric super resolution sparse Multi Input Multi Output (MIMO)-OFDM channel estimation technique in view of the Finite Rate of Innovation (FRI) theory has been proposed, whereby super-resolution assessments of delays in paths with arbitrary values can be accomplished. In the mean time, for wireless MIMO channels both the spatial and temporal correlations are made use of, to enhance the precision of the channel estimation. For outside communication situations, where wireless channels are meager in nature, path delays of distinctive transmit-receive antenna pairs share a similar sparse pattern because of the spatial correlation of MIMO channels. At the same time, the channel sparse pattern is almost unaltered amid several adjacent OFDM symbols because of the temporal correlation of MIMO channels. Exploiting these MIMO channel attributes simultaneously, the proposed technique performs better than existing highly developed techniques. Moreover, by joint processing of signals integrated with distinctive antennas, the pilot overhead can be decreased under the structure of the FRI theory.
DOI: 10.17762/ijritcc2321-8169.15074
Weighted Compressive Sensing Based Uplink Channel Estimation for TDD Massive MIMO Sytems
In this paper, the channel estimation problem for the uplink massive multi-input multi-output (MIMO) system is considered. Motivated by the observations that the channels in massive MIMO systems may exhibit sparsity and the channel support changes slowly over time, we propose one efficient channel estimation method under the framework of compressive sensing. By exploiting the channel impulse response (CIR) estimated from the previous OFDM symbol, we firstly estimate the probabilities that the elements in the current CIR are nonzero. Then, we propose the probability-weighted subspace pursuit (PWSP) algorithm exploiting these probability information to efficiently reconstruct the uplink massive MIMO channel. Moreover, noting that the massive MIMO systems also share a common support within one channel matrix due to the shared local scatterers in the physical propagation environment, an antenna collaborating method is exploited for the proposed method to further enhance the channel estimation performance. Simulation results show that compared to the existing compressive sensing methods, the proposed methods could achieve higher spectral efficiency as well as more reliable performance over time-varying channel