Channel Protection: Random Coding Meets Sparse Channels

TL;DR

This paper introduces a novel method combining random coding and sparsity exploitation to improve signal recovery in multipath channels, especially when the channel varies rapidly and traditional estimation is unreliable.

Contribution

It proposes a new approach that uses random encoding and sparse channel assumptions to simultaneously estimate the channel and recover the signal.

Findings

Reliable recovery when the channel is sufficiently sparse

Effective simultaneous channel estimation and signal recovery

Improved robustness over traditional equalization methods

Abstract

Multipath interference is an ubiquitous phenomenon in modern communication systems. The conventional way to compensate for this effect is to equalize the channel by estimating its impulse response by transmitting a set of training symbols. The primary drawback to this type of approach is that it can be unreliable if the channel is changing rapidly. In this paper, we show that randomly encoding the signal can protect it against channel uncertainty when the channel is sparse. Before transmission, the signal is mapped into a slightly longer codeword using a random matrix. From the received signal, we are able to simultaneously estimate the channel and recover the transmitted signal. We discuss two schemes for the recovery. Both of them exploit the sparsity of the underlying channel. We show that if the channel impulse response is sufficiently sparse, the transmitted signal can be recovered reliably.