Convolutional Sparse Coding based Channel Estimation for OTFS-SCMA in Uplink

TL;DR

This paper introduces a convolutional sparse coding-based channel estimation method for OTFS-SCMA in uplink scenarios, reducing pilot overhead while maintaining high accuracy in high mobility and massive connectivity environments.

Contribution

It proposes a novel CSC-based channel estimation technique for OTFS-SCMA that significantly reduces pilot overhead without sacrificing estimation accuracy.

Findings

Efficient channel estimation with minimal pilot overhead.

Improved BER, NMSE, and spectral efficiency.

Effective in high mobility and multi-user scenarios.

Abstract

Orthogonal time frequency space (OTFS) has emerged as the most sought-after modulation technique in a high mobility scenario. Sparse code multiple access (SCMA) is an attractive code-domain non-orthogonal multiple access (NOMA) technique. Recently a code-domain NOMA approach for OTFS, named OTFS-SCMA, is proposed. OTFS-SCMA is a promising framework that meets the demands of high mobility and massive connectivity. This paper presents a channel estimation technique based on the convolutional sparse coding (CSC) approach for OTFS-SCMA in the uplink. The channel estimation task is formulated as a CSC problem following a careful rearrangement of the OTFS input-output relation. We use an embedded pilot-aided sparse-pilot structure that enjoys the features of both OTFS and SCMA. The existing channel estimation techniques for OTFS in multi-user scenarios for uplink demand extremely high overhead for pilot and guard symbols, proportional to the number of users. The proposed method maintains a minimal overhead equivalent to a single user without compromising on the estimation error. The results show that the proposed channel estimation algorithm is very efficient in bit error rate (BER), normalized mean square error (NMSE), and spectral efficiency (SE).