Pilot Power Protocol for Autonomous Infrastructure Based Multihop Cellular Networks
Bibliographic record
Abstract
In a multihop cellular network, mobile terminals are able to transmit directly to other mobile terminals allowing them to lower their maximum transmission power and use other terminals as relays to forward traffic towards the base station. However, a large amount of interference is created near the base station because all traffic either emanates or is destined to the base station making it the capacity bottleneck of the network. In an autonomous infrastructure multihop cellular network, certain mobile terminals that have a connection to the backbone network act as access points and send traffic directly onto the backbone network, as would a base station. This reduces the amount of traffic required to be handled by the base station and increases network capacity. However, access points will require transmission parameters like their pilot power to be adjusted autonomously to optimal levels. In this paper, we propose an autonomous pilot power protocol that can be used by both access points and base stations. Our simulation results show that by adjusting a parameter within the pilot power protocol, a required percentage of covered terminals can be achieved by the network without prior knowledge of the location or density of terminals. Furthermore, the pilot power protocol determines the pilot power level which is optimal in terms of SINR and power consumption that achieves the required coverage while effectively eliminating the capacity bottleneck that existed at the base station.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".