AI-Enhanced Pilot-Assisted Angle-of-Arrival Estimation for Wearable Devices in Rician Fading Channels: A Low-SNR Focused Method

Abstract

YesAccurate angle-of-arrival (AoA) estimation is critical for precise localisation in wearable devices, particularly in challenging wireless environments such as Rician fading with low signal-to-noise ratios (SNRs). This paper proposes a pilot-assisted AoA estimation technique that integrates pseudo-random permutations and Walsh sequences within an OFDM-based transmission framework. The method preserves phase coherence and enhances spatial resolution by optimising pilot allocation and leveraging advanced signal processing. Comprehensive MATLAB simulations show high robustness: At −38dB (per-subcarrier, per-snapshot SNR), the ≈1.5∘ RMS is achieved by aggregating across L snapshots and multiple subcarriers (see Table 12 for K-factor scenarios), with sub-degree accuracy at moderate-to-high SNRs. Furthermore, a lightweight, one-dimensional (1D) convolutional neural network (CNN) reduces residual carrier-frequency offsets by over 30%, highlighting a promising synergy between classical signal processing and data-driven learning. Comparative analysis against state-of-the-art techniques and a discussion of computational complexity are provided, underscoring the suitability of the proposed method for next-generation wearable and IoT direction-finding applications.This work was supported in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/X039366/1; and in part by the HORIZON-MSCA-RISE, a Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) Initiative titled ‘‘FractuRe Orthopaedic Rehabilitation: Ubiquitous eHealth Solution (Robust),’’ under Project 101086492