An ML-Assisted OFDM-Based Hemispherical Array Antenna With Hybrid Beamforming for HAPS
Bibliographic record
Abstract
A high-altitude platform station (HAPS) located in the stratosphere can provide connectivity over a large area. However, a HAPS can create only a limited number of uncorrelated beams. Therefore, we cannot dedicate a beam to each user, and we need to perform user scheduling to be able to serve a large number of users with a HAPS. To this end, in this paper, we group users into clusters using the K-means algorithm and allocate the users in each cluster to orthogonal frequency resource blocks. The frequencies are reused across the clusters. Since HAPS has limited power resources, we also propose a novel codebook-based hybrid beamforming scheme. The results of our simulations conclusively show that our proposed beamforming scheme outperforms the traditional steering vector-based beamforming scheme in high-rise urban areas. Furthermore, we propose a deep Q-network (DQN)-based power allocation method that considerably outperforms the baseline equal power allocation scheme for large coverage areas. Finally, we compare the interference management efficiency of our proposed orthogonal frequency-division multiplexing (OFDM)-based hybrid beamforming-enabled hemispherical array antenna and baseline rectangular and cylindrical array antennas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".