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.

中文翻译：

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具有偏差补偿和粗量化信号的分布式量化感知 RLS 学习


在这项工作中，我们提出了一种使用粗量化信号的物联网（IoT）网络的节能分布式学习框架。特别是，我们开发了一种分布式量化感知递归最小二乘（DQA-RLS）算法，该算法可以使用少量比特量化的信号以节能的方式学习参数，同时需要较低的计算成本。此外，我们开发了一种偏差补偿策略，以进一步提高所提出的 DQA-RLS 算法的性能。我们对所提出的 DQA-RLS 算法进行统计分析，并推导出用于预测均方偏差的解析表达式。然后提出了计算复杂性评估以及对所提出的和现有技术的功耗的研究。数值结果根据物联网设备在对等模式下运行的场景中的分布式参数估计任务的现有技术来评估 DQA-RLS 算法。