Estimating Sparse MIMO channels having Common Support

Barbotin, Yann; Hormati, Ali; Rangan, Sundeep; Vetterli, Martin

doi:10.1109/icassp.2011.5946268

Cited by 8 publications

(7 citation statements)

References 19 publications

(22 reference statements)

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“…We call the corresponding algorithm "Block-Prony TLS", listed under Algorithm 1. It solves the annihilating filter equation (8) in the total least-square (TLS) sense. The crucial step is the identification of what shall be the unidimensional null space of H (K+1) in a noiseless case.…”

Section: Proposition 2 For a Set Of Exact Scs Channels With K Distinmentioning

confidence: 99%

Estimation of Sparse MIMO Channels with Common Support

Barbotin

Hormati

Rangan

et al. 2012

IEEE Trans. Commun.

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Abstract-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.

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Section: Proposition 2 For a Set Of Exact Scs Channels With K Distinmentioning

confidence: 99%

Estimation of Sparse MIMO Channels with Common Support

Barbotin

Hormati

Rangan

et al. 2012

IEEE Trans. Commun.

Self Cite

181

150

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show abstract

“…Out of the many available sparse PDPs, Brazil‐A PDP [19] is chosen in this study. Furthermore, the sparse MIMO channels pertaining to each user are considered with an approximate sparse common support [20], while MIMO channels corresponding to different users have been considered with different

N_{t}

values. For the

k th

user, the number of non‐zero taps is

N_{normalt k}

.…”

Section: Multi‐user Mimo–ofdm System Modelmentioning

confidence: 99%

Large random matrix‐based channel estimation for massive MIMO‐OFDM uplink

Sure

Bhuma³

2018

IET Communications

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“…3(b). Consider the central antenna r C =1, its |N |=2 direct neighbors with (shared) partial estimates given in (24). The partial correlation and error covariance matrices associated with those estimates (shown underlined) along with that of central element are given in (29) and (30) respectively.…”

Section: Updatementioning

confidence: 99%

“…In particular, they suffer from huge complexity due to matrix inversion of very large dimensionality, making it impractical. Some methods to reduce the complexity of MMSE estimator in massive MIMO have also been proposed e.g., [19]- [24]. It is important to note that most of the existing methods make assumptions that are not always true.…”

Section: Introductionmentioning

confidence: 99%

Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems

Zaib

Masood

et al. 2016

IEEE Trans. Commun.

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Abstract-Massive MIMO communication systems, by virtue of utilizing very large number of antennas, have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. In this paper, we consider uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. With increased number of antennas, the channel estimation problem becomes very challenging as exceptionally large number of channel parameters have to be estimated. We propose an efficient distributed linear minimum mean square error (LMMSE) algorithm that can achieve near optimal channel estimates at very low complexity by exploiting the strong spatial correlations and symmetry of large antenna array elements. The proposed method involves solving a (fixed) reduced dimensional LMMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring antenna elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. We also analyse the effect of pilot contamination on the mean square error (MSE) performance of different channel estimation techniques. Unlike the conventional approaches, we use stochastic geometry to obtain analytical expression for interference variance (or power) across OFDM frequency tones and use it to derive the MSE expressions for different algorithms under both noise and pilot contaminated regimes. Simulation results validate our analysis and the near optimal MSE performance of proposed estimation algorithms.

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Estimating Sparse MIMO channels having Common Support

Cited by 8 publications

References 19 publications

Estimation of Sparse MIMO Channels with Common Support

Estimation of Sparse MIMO Channels with Common Support

Large random matrix‐based channel estimation for massive MIMO‐OFDM uplink

Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems

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