Tensor-Based Modulation for Unsourced Massive Random Access

TL;DR

This paper proposes a tensor-based modulation scheme for unsourced massive random access, leveraging low-rank tensor structures and tensor decomposition to improve user separation and demapping in multi-user, multi-antenna channels.

Contribution

It introduces a novel tensor modulation method using Grassmannian sub-constellations for unsourced access, enabling better user separation and demapping in complex channels.

Findings

Performs well compared to state-of-the-art methods

Effective in block fading and multi-antenna scenarios

Utilizes low-rank tensor structures for user separation

Abstract

We introduce a modulation for unsourced massive random access whereby the transmitted symbols are rank-1 tensors constructed from Grassmannian sub-constellations. The use of a low-rank tensor structure, together with tensor decomposition in order to separate the users at the receiver, allows a convenient uncoupling between multi-user separation and single-user demapping. The proposed signaling scheme is designed for the block fading channel and multiple-antenna settings, and is shown to perform well in comparison to state-of-the-art unsourced approaches.