Estimation of Doubly-Dispersive Channels in Linearly Precoded Multicarrier Systems Using Smoothness Regularization

TL;DR

This paper introduces a smoothness regularization-based channel estimation method for pulse-shaped multicarrier systems, improving accuracy and reducing overhead in doubly-dispersive channels, especially in OTFS modulation.

Contribution

It proposes a novel interpolation-based channel estimation scheme that avoids leakage effects by precoding only data symbols and placing pilots without precoding, enhancing estimation accuracy.

Findings

Achieves accurate channel estimation with less signaling overhead.

Effectively suppresses leakage effects in OTFS channel estimation.

Demonstrates improved performance over Wiener filtering methods.

Abstract

In this paper, we propose a novel channel estimation scheme for pulse-shaped multicarrier systems using smoothness regularization for ultra-reliable low-latency communication (URLLC). It can be applied to any multicarrier system with or without linear precoding to estimate challenging doubly-dispersive channels. A recently proposed modulation scheme using orthogonal precoding is orthogonal time-frequency and space modulation (OTFS). In OTFS, pilot and data symbols are placed in delay-Doppler (DD) domain and are jointly precoded to the time-frequency (TF) domain. On the one hand, such orthogonal precoding increases the achievable channel estimation accuracy and enables high TF diversity at the receiver. On the other hand, it introduces leakage effects which requires extensive leakage suppression when the piloting is jointly precoded with the data. To avoid this, we propose to precode the data symbols only, place pilot symbols without precoding into the TF domain, and estimate the channel coefficients by interpolating smooth functions from the pilot samples. Furthermore, we present a piloting scheme enabling a smooth control of the number and position of the pilot symbols. Our numerical results suggest that the proposed scheme provides accurate channel estimation with reduced signaling overhead compared to standard estimators using Wiener filtering in the discrete DD domain.