Distributed Quantization-Aware RLS Learning With Bias Compensation and Coarsely Quantized Signals

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...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2022-01, Vol.70, p.3441-3455
Main Authors: Danaee, Alireza, Lamare, Rodrigo C. de, Nascimento, Vitor Heloiz
Format: Article
Language:English
Subjects:
adaptive algorithms
Adaptive systems
Algorithms
Bias
coarse quantization
Compensation
Computing costs
Distributed learning
Energy efficiency
energy-efficient signal processing
Internet of Things
Learning
Measurement
Parameter estimation
Peer-to-peer computing
Power consumption
Power demand
Sensors
Signal processing algorithms
Statistical analysis
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Summary: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. Moreover, we develop a bias compensation strategy to further improve the performance of the proposed DQA-RLS algorithm. We carry out a statistical analysis of the proposed DQA-RLS algorithm and derive analytical expressions for predicting the mean-square deviation. A computational complexity evaluation and a study of the power consumption of the proposed and existing techniques are then presented. Numerical results assess the DQA-RLS algorithm against existing techniques for a distributed parameter estimation task in a scenario where IoT devices operate in peer-to-peer mode.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2022.3185898