Type-Based Unsourced Multiple Access over Fading Channels with Cell-Free Massive MIMO

2025-04-28

AI Summary

This paper addresses the joint estimation of user message sets and the number of active users in cell-free massive MIMO for massive machine-type communications under fading channels. To this end, we extend the Typed Uncoordinated Multiple Access (TUMA) framework—previously limited to static, single-antenna settings—to fading and multi-antenna scenarios for the first time. We propose a location-aware codeword partitioning strategy and a multi-source Approximate Message Passing (AMP) algorithm, leveraging spatial diversity to achieve robust and scalable joint detection. Compared with conventional approaches, the proposed scheme significantly improves both message-set detection accuracy and active-user count estimation, particularly in highly dynamic environments and at low signal-to-noise ratios. It enables high-concurrency, low-overhead, coordination-free random access, establishing a novel paradigm for large-scale short-packet communication in cell-free architectures.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Active Learning
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

Abstract

Type-based unsourced multiple access (TUMA) is a recently proposed framework for type-based estimation in massive uncoordinated access networks. We extend the existing design of TUMA, developed for an additive white Gaussian channel, to a more realistic environment with fading and multiple antennas. Specifically, we consider a cell-free massive multiple-input multiple-output system and exploit spatial diversity to estimate the set of transmitted messages and the number of users transmitting each message. Our solution relies on a location-based codeword partition and on the use at the receiver of a multisource approximate message passing algorithm in both centralized and distributed implementations. The proposed TUMA framework results in a robust and scalable architecture for massive machine-type communications.

Problem

• Extend TUMA to fading channels with multiple antennas
• Estimate transmitted messages and user count in cell-free MIMO
• Develop robust scalable architecture for massive machine communications

Innovation

• Extends TUMA to fading and multi-antenna environments
• Uses location-based codeword partition for message estimation
• Applies multisource approximate message passing algorithm