On Application of MUSIC Algorithm to Doppler Estimation for Aeronautical Satellite OFDM
Bibliographic record
Abstract
Doppler frequency shift arises due to the different angles of arrival of the various multipath to/from aircraft and the relative velocity to the satellite. Therefore implementing robust high throughput Orthogonal Frequency Division Multiplexing (OFDM) system based, it depends crucially on the Doppler shift mitigation. In the aeronautical satellite channel, the Doppler spread has a significant impact on the OFDM system performance. The rapid changes in the channel conditions, which severely destroys the orthogonality between sub-carrier and lead to Inter-Carrier Interference (ICI). In this paper, we propose the OFDM with multiple transmitters, antenna array at the receiver, and the Multiple Signal Classification (MUSIC) algorithm as a method to mitigate the Doppler frequency shifts. The simulation results showed the ideal number of simultaneous transmission of OFDM pilot symbols to enhance the estimation error, where there was no significant improvement in the estimation beyond nine transmitters at low SNR levels 0-4 dB. Also, The system performance was presented to illustrate the Doppler effect before and after the compensation.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".