Key result
Unsupervised learning improves AoD estimation accuracy and cuts latency versus benchmarks in MISO downlink transmissions.
Why the study?
Conventional AoD estimation methods face challenges such as requiring a large number of real-time measurements for latency-critical applications and enormous data collection for training deep learning models.
An unsupervised learning framework for AoD estimation in MISO downlink transmissions improves accuracy and reduces latency and overhead.
With the emergence 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 a 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.
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Li et al. (2025) studied Angle of Departure (AoD) estimation in MISO downlink wireless transmissions. Unsupervised learning framework for DML and SML based AoD estimation vs. Various benchmarks was evaluated on Estimation accuracy, number of observations, estimation overhead, and latency. An unsupervised learning framework for AoD estimation in MISO downlink transmissions improved estimation accuracy and significantly reduced the required number of observations and latency compared to benchmarks.
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