LMMSE-based interference mitigation method for compressive signal

Abstract

Compressive sensing (CS) is a viable source of an innovative 5G system, what's more, it's an effective technology to deal with the data redundancy problem of massive machine-to-machine communication (MMC), since it enables the recovery of sparse and approximately sparse signals with significantly fewer samples than demanded by Nyquist-Shannon sampling theory. Interference in signal will lead a series problem to signal processing, which will be a recovery error proportional to the interference energy in CS. This paper tries to mitigate interference by proposing a compressive interference pre-filter based on linear minimal mean square error (LMMSE). The main contributions are that two practical estimation methods that based on autocorrelation function and auto-covariance function respectively, are applied to estimate the LMMSE-based filtering matrix. The estimation and pre-filter algorithms can be practically integrated into the compressive signal processing framework with improved robust property to noise and interference.