Sparse Channel Separation using Random Probes

TL;DR

This paper demonstrates that sparse channel responses can be efficiently separated using random probes and simultaneous activation of sources, leveraging compressive sensing to reduce probing time and complexity.

Contribution

It introduces a method for separating multiple sparse channel responses simultaneously with theoretical guarantees, reducing probing time compared to traditional sequential methods.

Findings

Channel responses can be separated even with short probe signals.

Theoretical lower bounds on probe length ensure stable separation.

Applicable to seismic imaging, MIMO communication, and coded aperture imaging.

Abstract

This paper considers the problem of estimating the channel response (or Green's function) between multiple source-receiver pairs. Typically, the channel responses are estimated one-at-a-time: a single source sends out a known probe signal, the receiver measures the probe signal convolved with the channel response, and the responses are recovered using deconvolution. In this paper, we show that if the channel responses are sparse and the probe signals are random, then we can significantly reduce the total amount of time required to probe the channels by activating all of the sources simultaneously. With all sources activated simultaneously, the receiver measures a superposition of all the channel responses convolved with the respective probe signals. Separating this cumulative response into individual channel responses can be posed as a linear inverse problem. We show that channel response separation is possible (and stable) even when the probing signals are relatively short in spite of the corresponding linear system of equations becoming severely underdetermined. We derive a theoretical lower bound on the length of the source signals that guarantees that this separation is possible with high probability. The bound is derived by putting the problem in the context of finding a sparse solution to an underdetermined system of equations, and then using mathematical tools from the theory of compressive sensing. Finally, we discuss some practical applications of these results, which include forward modeling for seismic imaging, channel equalization in multiple-input multiple-output communication, and increasing the field-of-view in an imaging system by using coded apertures.