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Energy-Efficient Distributed Learning With Coarsely Quantized Signals
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DOI：10.1109/LSP.2021.3051522.png)
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Abstract
En 中文
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.
Keywords:
Signal processing algorithms
Quantization (signal)
Power demand
Peer-to-peer computing
Internet of Things
Task analysis
Random variables
Distributed learning
energy-efficient signal processing
adaptive algorithms
coarse quantization
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