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Platform-Aware Channel Knowledge Mapping via Mutual Antenna Pattern Learning in 3D Wireless Links

Mushfiqur Rahman, Ismail Guvenc, Jason A. Abrahamson, Arupjyoti Bhuyan

Abstract

This letter proposes a platform-aware framework to characterize wireless links by empirically modeling the `near-platform' scattering and reflections induced by the hardware mounting structures of both endpoints. We model the link characteristics as a novel mutual antenna pattern: a joint function of the angle of arrival (AoA) and angle of departure (AoD). We demonstrate that while individual platform-aware patterns are mathematically unidentifiable from power measurements, the coupled mutual pattern can be effectively estimated in a least-squares sense. Our framework is evaluated using noisy measurement data, revealing that as few as 10 measurements per joint-angular bin are sufficient. The proposed methodology is validated through cross-validation of experimental subsets, demonstrating that the learned mutual radiation pattern reduces path loss estimation errors by up to 10 dB compared to traditional models using isolated anechoic chamber antenna gains.

Platform-Aware Channel Knowledge Mapping via Mutual Antenna Pattern Learning in 3D Wireless Links

Abstract

This letter proposes a platform-aware framework to characterize wireless links by empirically modeling the `near-platform' scattering and reflections induced by the hardware mounting structures of both endpoints. We model the link characteristics as a novel mutual antenna pattern: a joint function of the angle of arrival (AoA) and angle of departure (AoD). We demonstrate that while individual platform-aware patterns are mathematically unidentifiable from power measurements, the coupled mutual pattern can be effectively estimated in a least-squares sense. Our framework is evaluated using noisy measurement data, revealing that as few as 10 measurements per joint-angular bin are sufficient. The proposed methodology is validated through cross-validation of experimental subsets, demonstrating that the learned mutual radiation pattern reduces path loss estimation errors by up to 10 dB compared to traditional models using isolated anechoic chamber antenna gains.

Paper Structure

This paper contains 12 sections, 6 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Proposed Framework of Channel Modeling
  3. Structurally-Induced Wireless Link Environments
  4. Mutual Antenna Pattern
  5. Empirical Estimation of $G_\text{m}$ from Noisy RSS Measurements
  6. Experimental Results
  7. Datasets and Experimental Configurations
  8. Baselines and Evaluation Metrics
  9. Snapshot of Reconstructed Mutual Antenna Gain
  10. RSS Estimation Performance
  11. Discussion
  12. Conclusion

Figures (4)

  • Figure 1: Wireless link between two nodes with integrated physical structures. Reflections and scattering from the node bodies create multipath components that depend on their 3D orientations and the departure/arrival angles at both terminals.
  • Figure 2: Snapshot of reconstructed mutual antenna gain as a function of elevation angle. Subfigures (a)--(d) denote the experimental training subsets in Table \ref{['tab:exp_list']}.
  • Figure 3: RSS prediction performance for test experiments A1 and A3: comparison between baseline and proposed method. (a) and (d) spatial tracking of measured and predicted RSS across 3D distance; (b) and (e) CDF of absolute error; (c) and (f) MAE as a function of elevation angle alongside the elevation angle probability distribution.
  • Figure 4: RSS prediction performance for test experiments B1 and B2: comparison between baseline and proposed method. (a) and (d) spatial tracking of measured and predicted RSS across 3D distance; (b) and (e) CDF of absolute error; (c) and (f) MAE as a function of elevation angle alongside the elevation angle probability distribution.