Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals

TL;DR

This paper introduces a distributed learning algorithm tailored for IoT networks that efficiently learns parameters using coarsely quantized signals, reducing energy consumption and computational costs.

Contribution

The paper proposes a novel distributed quantization-aware RLS algorithm specifically designed for energy-efficient IoT applications with low-bit quantized signals.

Findings

DQA-RLS outperforms existing methods in energy efficiency.

The algorithm achieves accurate parameter estimation with low-bit signals.

Numerical results validate the effectiveness of the proposed approach.

Abstract

In this work, we present an energy-efficient distributed learning framework using coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware recursive least squares (DQA-RLS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. Numerical results assess the DQA-RLS algorithm against existing techniques for a distributed parameter estimation task where IoT devices operate in a peer-to-peer mode.