A Cell-Free Scheme for UAV Base Stations with HAPS-Assisted Backhauling in Terahertz Band
Bibliographic record
Abstract
In this paper, we propose a cell-free scheme for unmanned-aerial-vehicle (UAV) base-stations (BSs) to manage the severe intercell interference between aerial and terrestrial nodes. Since the cell-free scheme requires a huge bandwidth for backhauling, we propose to use the terahertz (THz) band for the wireless backhaul links between UAV-BSs and central-processing-unit (CPU). Also, because the THz band requires a reliable line-of-sight (LoS) link, instead of a terrestrial CPU, we propose to use a high-altitude-platform-station (HAPS) as a CPU. At the first time-slot of the proposed scheme, users send their messages to UAVs at the sub-6 GHz band. Then each UAV applies match-filtering to align the received signals from users, and performs power allocation for the aligned signal of each user. At the second time-slot, we allocate orthogonal resource-blocks (RBs) for each user at the THz band, and send signals towards HAPS. In HAPS, for aligning the received signals for each user from different UAVs, we perform analog beamforming. Finally, we demodulate and decode the message of each user at its unique RBs. We formulate an optimization problem that maximizes the minimum SINR of users, and find the optimum allocated powers for users in each UAV by the bisection method. Simulation results prove the superiority of the proposed scheme compared with aerial-cellular and terrestrial-cell-free baseline schemes. Simulation results also showed that utilizing HAPS as a CPU is useful when the huge path-loss between UAV-BSs and HAPS in the THz band is compensated by a high number of antennas at HAPS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".