论文标题
研究具有粗略量化信号的基于RLS的分布式学习的研究
Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals
论文作者
论文摘要
在这项工作中，我们使用用于物联网（IoT）网络的精心量化的信号提出了节能分布式学习框架。特别是，我们开发了分布式量化的递归最小二乘（DQA-RLS）算法，该算法可以使用几乎没有位的信号来以节能的方式学习参数，同时需要低计算成本。数值结果评估了针对现有技术的DQA-RLS算法，用于分布式参数估计任务，其中IoT设备以对等模式运行。
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.
