Hemispherical Massive MIMO Architecture for High-Altitude Platform Station (HAPS)
Bibliographic record
Abstract
In this paper, we present a novel hemispherical antenna array (HAA) designed for High-Altitude Platform Stations (HAPS). Traditional rectangular antenna arrays for HAPS suffer from a significant limitation - their antenna elements are perpetually oriented downward, resulting in low gain for distant users. Meanwhile, cylindrical antenna arrays were introduced to mitigate this drawback, but they, in turn, exhibit a distinct problem: their antenna elements continually face the horizon, leading to suboptimal gain for users located beneath the HAPS. To address these challenges, we introduce the HAA. In the HAA configuration, antenna elements are strategically distributed across the surface of a hemisphere, ensuring that each user receives direct alignment with specific antenna elements, thereby maximizing the gain for all users. We derive the achievable data rates for users within this proposed scheme, employing an analog beamforming technique that leverages the steering vectors of the selected antenna elements for each user. We also formulate an op-timization problem focused on maximizing the minimum Signal-to-Interference-plus-Noise Ratio (SINR) for users. Additionally, we introduce an antenna selection algorithm based on the gains of the antenna elements. To further enhance system performance, we employ the Bisection method to determine the optimal power allocation for each user. Our simulation results substantiate the superior rate performance of the proposed HAA when compared to the conventional rectangular and cylindrical baseline arrays. The proposed approach demonstrates to reach sum data rates of up to 14 Gigabit/s. Furthermore, in contrast to the baseline schemes, the proposed scheme achieves more consistent spectral efficiencies across the entire coverage area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".