AI-Enhanced Pilot-Assisted Angle-of-Arrival Estimation for Wearable Devices in Rician Fading Channels: A Low-SNR Focused Method
Aldelemy, Ahmad Al-Dulaimi, A. El Sanousi, G.T.A. Siaw, Prince O. Ali, N.T. Dama, Y.
Aldelemy, Ahmad
Al-Dulaimi, A.
El Sanousi, G.T.A.
Siaw, Prince O.
Ali, N.T.
Dama, Y.
Publication Date
2025-11-18
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© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
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2025-11-09
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Abstract
Accurate 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.
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Aldelemy A, Al-Dulaimi A, El Sanousi GTA et al (2025) AI-Enhanced Pilot-Assisted Angle-of-Arrival Estimation for Wearable Devices in Rician Fading Channels: A Low-SNR Focused Method. IEEE Access. 13: 197381-197413.
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