학술논문
Energy-Efficient Distributed Recursive Least Squares Learning with Coarsely Quantized Signals
Document Type
Conference
Source
2020 54th Asilomar Conference on Signals, Systems, and Computers Signals, Systems, and Computers, 2020 54th Asilomar Conference on. :1533-1537 Nov, 2020
Subject
Language
ISSN
2576-2303
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.