LOMA Map for Location based Resource Management and Data Transmission in future RAN
Bibliographic record
Abstract
In this work, a LOcation based RAN resource Management and Access (LOMA) map is designed for future radio access networks (RAN) to allocate radio resources in complex wireless environment, and to facilitate uplink/downlink data transmissions. Enabled by the AI and high-precise positioning techniques, the LOMA map can associate a set of radio resources and data transmission parameters (e.g, transmit power, MCS level) with a geographical location in the RAN area. Given the LOMA map, each user equipment (UE) or infrastructure associated with the RAN can directly determine the radio resources and parameters used for uplink/downlink data transmissions according to its location. An AI enabled LOMA map generation method are proposed to generate LOMA maps according to the statistical traffic and wireless environment data collected by UEs and infrastructures. A reinforcement learning (RL) based algorithm is further proposed in the LOMA map generation method to dynamically quantify the available radio resources according to the real-time data traffic and the performance of applied resource scheduling scheme. Case studies with numerical results are presented to show the benefits provided by LOMA map technique in terms of increasing resource sharing efficiency, reducing signal overheads in data transmissions, and enabling resource scheduling schemes with less computing cost.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".