Type-Based Unsourced Multiple Access

TL;DR

This paper extends the type-based multiple access framework to unsourced scenarios, enabling devices to communicate quantized states to a receiver for effective type estimation, demonstrated via multi-target tracking.

Contribution

It introduces a generalized framework for unsourced multiple access that includes state quantization and type estimation, utilizing approximate message passing.

Findings

Type estimation is effective via approximate message passing.

Optimal quantization resolution balances distortion and communication error.

Framework applicable to multi-target position tracking.

Abstract

We generalize the type-based multiple access framework proposed by Mergen and Tong (2006) to the case of unsourced multiple access. In the proposed framework, each device tracks the state of a physical/digital process, quantizes this state, and communicates it to a common receiver through a shared channel in an uncoordinated manner. The receiver aims to estimate the type of the states, i.e., the set of states and their multiplicity in the sequence of states reported by all devices. We measure the type estimation error using the Wasserstein distance. Considering an example of multi-target position tracking, we show that type estimation can be performed effectively via approximate message passing. Furthermore, we determine the quantization resolution that minimizes the type estimation error by balancing quantization distortion and communication error.