Estimation of Sparse MIMO Channels with Common Support

TL;DR

This paper introduces a parametric sparse estimation technique for MIMO channels with common support, leveraging FRI principles to improve channel estimation accuracy in OFDM and CDMA systems.

Contribution

It generalizes spectral estimation methods for multiple signals with shared support, providing a robust and concise channel description in MIMO communications.

Findings

Reduces symbol error rate by up to 50% in simulations.

Derives theoretical bounds for channel parameter estimation.

Demonstrates effectiveness in OFDM and CDMA scenarios.

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.