Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals

Abstract

In this work, we present an energy-efficient distributed learning framework using low-resolution ADCs and coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. We also carry out a statistical analysis of the proposed DQA-LMS algorithm that includes a stability condition. Simulations assess the DQA-LMS algorithm against existing techniques for a distributed parameter estimation task where IoT devices operate in a peer-to-peer mode and demonstrate the effectiveness of the DQA-LMS algorithm.

5 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:2012.10939

References in corpus (7)

• Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems
• Throughput Analysis of Massive MIMO Uplink with Low-Resolution ADCs
• Distributed Learning in Wireless Sensor Networks
• Low Power Analog-to-Digital Conversion in Millimeter Wave Systems: Impact of Resolution and Bandwidth on Performance
• Branch-and-Bound Precoding for Multiuser MIMO Systems with 1-Bit Quantization
• Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents
• Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals

Cited by in corpus (3)

• Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals
• Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals
• Study of Block Diagonalization Precoding and Power Allocation for Multiple-Antenna Systems with Coarsely Quantized Signals