Estimation of Sparse MIMO Channels with Common Support

Abstract

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.

Channel Acquisition for Massive MIMO-OFDM With Adjustable Phase Shift Pilots

“…Channel sparsity has also been exploited to mitigate pilot contamination in multi-cell massive MIMO [26], [27]. Note that compressive sensing has been applied to sparse channel acquisition in some recent works (see, e.g., [19], [22], [23], [28] and references therein), in which the corresponding pilot signals are usually assumed to be randomly generated. However, it is usually quite difficult to implement random pilot signals in practical systems [29].…”

Efficient Downlink Channel Estimation Scheme Based on Block-Structured Compressive Sensing for TDD Massive MU-MIMO Systems

“…It is suggested in [4] that two channel taps are resolvable if the time interval of arrival is larger than 1 10B where B is the bandwidth of signal. Therefore, it is obvious that the CIRs measured at different antennas share a common support [5] …”

“…This work is inspired by the recently proposed idea of common support in sparse channels [5], whereby the channel impulse response (CIR) between different transmit and receive antenna pairs exhibits sparse block structure. In addition, channel reciprocity in TDD mode enables us to use the path delays estimated from uplink training as an auxiliary information to improve the channel estimation performance in downlink.…”

Weighted compressive sensing based uplink channel estimation for time division duplex massive multi‐input multi‐output systems

“…After that we utilize the probabilities as a priori information in the subspace pursuit (SP) algorithm [17] [18], and propose the probability-weighted SP (PWSP) algorithm to improve the uplink massive MIMO channel estimation. Moreover, noting that the massive MIMO system may also share common support within one channel matrix [19]. Based on this assumption, an antenna collaborating method is exploited for the proposed channel estimation approach, so that the antennas could share information with their neighbouring antennas to strengthen their beliefs about the locations of the path delays, and thus to further improve the channel estimation performance.…”