Angle of Arrival Estimation for Terahertz-enabled Space Information Networks
Bibliographic record
Abstract
Space information networks (SINs) empowered by Terahertz (THz) frequencies are expected to play a vital role in next-generation wireless space networks due to the unique transmission characteristics and coverage extension capabilities of SINs, owing to their high altitudes. Also, utilizing THz frequencies allows the usage of more bandwidth. However, communications in this frequency range come at the cost of extreme path loss, especially for low-orbit implementation of SINs. High gain narrow beamforming utilizing a large number of antennas can be considered to overcome substantial losses at these frequencies. Consequently, highly accurate and efficient angle of arrival (AoA) estimation algorithms are required to achieve successful beamforming and eventually to increase the signal-to-noise ratio (SNR) in SINs. To this end, we propose the utilization of a two-stage gold-MUSIC algorithm over an array of subarray (AoSA) structure for AoA estimation with lower power consumption and less hardware complexity compared to the contemporary arrays due to the reduced RF-chain in AoSA. Furthermore, we introduce an analysis of AoA estimation performance in terms of residual Doppler spread, which is a realistic metric for SINs since Doppler cannot be accurately estimated in the case of instantaneous rapid motional changes of satellites especially at high frequencies. Results show that the proposed two-staged gold-MUSIC method for AoSA provides accurate AoA estimation while being computationally efficient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".