Type-Based Unsourced Multiple Access Over Fading Channels in Distributed MIMO With Application to Multi-Target Localization

2026-03-02

AI Summary

This work addresses the challenge of enabling type-based passive multiple access—where the empirical distribution of user messages is estimated rather than individual identities—in distributed MIMO systems operating over fading channels without channel state information. To this end, the authors propose a joint communication and sensing framework that employs location-dependent codebook partitioning to mitigate path loss disparities, designs a multi-source approximate message passing (Multi-AMP) algorithm amenable to both centralized and distributed decoding, and introduces a quantized target location reporting mechanism. In multi-target localization scenarios, a cost function incorporating both localization error and missed detection penalties is formulated to evaluate the efficiency of resource allocation between communication and sensing. Experimental results demonstrate that the proposed approach effectively balances sensing accuracy and communication overhead, revealing how performance varies with the number of quantization bits and the allocation ratio of shared resources.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

We consider the problem of type estimation over unsourced multiple access fading channels in distributed multiple-input multiple-output (D-MIMO) systems. Unlike classical unsourced multiple access, type-based unsourced multiple access (TUMA) aims to estimate the type, i.e., the empirical distribution of transmitted messages. We extend our prior work on TUMA over additive white Gaussian channels to fading scenarios in which neither the transmitters nor the receiver have channel state information. To mitigate the impact of path-loss variability, we employ location-based codebook partitioning: users with similar large-scale fading coefficients use the same codebook. The decoder is built on the multisource approximate message passing algorithm proposed by Cakmak et al. (2025), and supports both centralized and distributed implementations. As an application, we demonstrate how TUMA enables efficient communication in a multi-target localization setting, where distributed sensors report to a D-MIMO receiver quantized target positions. We propose a performance cost function that combines localization errors with a misdetection penalty, and use it to characterize how performance depends on the fraction of resources assigned to sensing vs. communication, as well as on the number of bits used to quantize the positions of the targets.

Problem

• type estimation
• unsourced multiple access
• fading channels
• distributed MIMO
• multi-target localization

Innovation

• Type-Based Unsourced Multiple Access
• Distributed MIMO
• Fading Channels
• Approximate Message Passing
• Multi-Target Localization