Distributed Quantization for Sparse Time Sequences

TL;DR

This paper introduces a distributed quantization method leveraging secure group testing to efficiently represent sparse time sequences acquired via scalar ADCs, improving over traditional quantization and compressed sensing approaches.

Contribution

It proposes a novel distributed quantization scheme for sparse signals that exploits group testing theory, with practical implementation in multi-hop networks.

Findings

Outperforms conventional quantization and compressed sensing methods.

Provides a low-complexity routing and signal recovery policy.

Demonstrates significant numerical improvements in accuracy and efficiency.

Abstract

Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. Quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of analog signals are acquired individually and processed jointly. Such setups are referred to as distributed quantization. In this work, we propose a distributed quantization scheme for representing a set of sparse time sequences acquired using conventional scalar ADCs. Our approach utilizes tools from secure group testing theory to exploit the sparse nature of the acquired analog signals, obtaining a compact and accurate representation while operating in a distributed fashion. We then show how our technique can be implemented when the quantized signals are transmitted over a multi-hop communication network providing a low-complexity network policy for routing and signal recovery. Our numerical evaluations demonstrate that the proposed scheme notably outperforms conventional methods based on the combination of quantization and compressed sensing tools.