With the emerging of simultaneous localization and communication (SLAC), it becomes more and more attractive to perform angle of departure (AoD) estimation at the receiving Internet of Thing (IoT) user end for improved positioning accuracy, flexibility and enhanced user privacy. To address challenges like large number of real-time measurements required for latency-critical applications and enormous data collection for training deep learning models in conventional AoD estimation methods, we propose in this letter an unsupervised learning framework, which unifies training for both deterministic maximum likelihood (DML) and stochastic maximum likelihood (SML) based AoD estimation in multiple-input single-output (MISO) downlink (DL) wireless transmissions. Specifically, under the line-of-sight (LoS) assumption, we incorporate both the received signals and pilot-sequence information, as per its availability at the DL user, into the input of the deep learning model, and adopt a common neural network architecture compatible with input data in both DML and SML cases. Extensive numerical results validate that the proposed unsupervised learning based AoD estimation not only improves estimation accuracy, but also significantly reduces required number of observations, thereby reducing both estimation overhead and latency compared to various benchmarks.

翻译：随着同步定位与通信（SLAC）技术的兴起，在接收端的物联网（IoT）用户终端执行离角（AoD）估计以提高定位精度、灵活性并增强用户隐私保护，正变得越来越具有吸引力。针对传统AoD估计方法中存在的挑战——例如时延敏感应用需要大量实时测量，以及训练深度学习模型所需的海量数据收集——本文提出一种无监督学习框架，该框架统一了多输入单输出（MISO）下行链路（DL）无线传输中基于确定性最大似然（DML）和随机最大似然（SML）的AoD估计训练。具体而言，在视距（LoS）传输假设下，我们将接收信号以及导频序列信息（依据其在下行链路用户端的可用性）共同作为深度学习模型的输入，并采用一种兼容DML与SML两种情况下输入数据的通用神经网络架构。大量数值结果表明，与多种基准方法相比，所提出的基于无监督学习的AoD估计方法不仅提高了估计精度，而且显著减少了所需的观测次数，从而降低了估计开销与时延。