Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals
Centre for Telecommunications Studies, Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学电信研究中心）；University of York（约克大学）；University of São Paulo（圣保罗大学）
英文摘要：
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.
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