Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals
This work addresses the problem of energy-efficient learning for IoT devices operating in peer-to-peer mode, which is important for extending device battery life.
This paper introduces a distributed quantization-aware recursive least squares (DQA-RLS) algorithm for energy-efficient learning in IoT networks. It enables parameter learning using coarsely quantized signals with few bits while maintaining low computational cost.
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.