Unsourced Sparse Multiple Access foUnsourced Sparse Multiple Access for 6G Massive Communicationr 6G Massive Communication

TL;DR

This paper introduces USMA, a novel unsourced sparse multiple access framework for 6G massive communication, combining compressed sensing and SIDMA to significantly improve capacity and efficiency in IoT scenarios.

Contribution

The paper proposes a new USMA framework with a scalable design for 6G, integrating compressed sensing and SIDMA, achieving near-theoretical bounds and enhanced IoT performance.

Findings

USMA approaches theoretical capacity bounds within 1-1.5 dB.

USMA-based A-IoT achieves nearly 4x capacity and 6x efficiency over RFID.

Scalable design reduces memory and computation for IoT applications.

Abstract

Massive communication is one of key scenarios of 6G where two magnitude higher connection density would be required to serve diverse services. As a promising direction, unsourced multiple access has been proved to outperform significantly over orthogonal multiple access (OMA) or slotted-ALOHA in massive connections. In this paper we describe a design framework of unsourced sparse multiple access (USMA) that consists of two key modules: compressed sensing for preamble generation, and sparse interleaver division multiple access (SIDMA) for main packet transmission. Simulation results of general design of USMA show that the theoretical bound can be approached within 1~1.5 dB by using simple channel codes like convolutional. To illustrate the scalability of USMA, a customized design for ambient Internet of Things (A-IoT) is proposed, so that much less memory and computation are required. Simulations results of Rayleigh fading and realistic channel estimation show that USMA based A-IoT solution can deliver nearly 4 times capacity and 6 times efficiency for random access over traditional radio frequency identification (RFID) technology.