A Tensor-BTD-based Modulation for Massive Unsourced Random Access

TL;DR

This paper introduces a tensor-based modulation scheme for massive unsourced random access, leveraging tensor decomposition and Grassmann manifold design to improve user separation and outperform existing methods.

Contribution

It presents a novel tensor-based modulation with a Grassmann manifold constellation design, providing theoretical guarantees for user separation in massive unsourced random access.

Findings

Outperforms state-of-the-art tensor-based modulation methods

Provides theoretical guarantees for active user separation

Demonstrates improved simulation results

Abstract

In this letter, we propose a novel tensor-based modulation scheme for massive unsourced random access. The proposed modulation can be deemed as a summation of third-order tensors, of which the factors are representatives of subspaces. A constellation design based on high-dimensional Grassmann manifold is presented for information encoding. The uniqueness of tensor decomposition provides theoretical guarantee for active user separation. Simulation results show that our proposed method outperforms the state-of-the-art tensor-based modulation.