Supporting More Active Users for Massive Access via Data-assisted Activity Detection

TL;DR

This paper introduces a data-assisted activity detection framework for massive machine-type communication in 5G networks, significantly improving user detection accuracy and supporting approximately 20% more active users by leveraging joint data and activity detection techniques.

Contribution

It proposes a novel integrated approach combining activity detection and data decoding to reduce errors and enhance the capacity of massive access systems.

Findings

Increases activity detection accuracy.

Supports approximately 20% more active users.

Effective in reducing false alarms and missed detections.

Abstract

Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scalable access for a large number of devices. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. Particularly, the two key tasks in massive access systems, namely, user activity detection and data detection, were handled separately in most existing studies, which ignored the common sparsity pattern in the received pilot and data signal. Moreover, error detection and correction in the payload data provide additional mechanisms for performance improvement. In this paper, we propose a data-assisted activity detection framework, which aims at supporting more active users by reducing the activity detection error, consisting of false alarm and missed detection errors. Specifically, after an initial activity detection step based on the pilot symbols, the false alarm users are filtered by applying energy detection for the data symbols; once data symbols of some active users have been successfully decoded, their effect in activity detection will be resolved via successive pilot interference cancellation, which reduces the missed detection error. Simulation results show that the proposed algorithm effectively increases the activity detection accuracy, and it is able to support $\sim 20\%$ more active users compared to a conventional method in some sample scenarios.