Learning-Assisted Access Management for Dense 3D Small Cell Networks
Bibliographic record
Abstract
Efficient access management for wireless devices in a small cell (SC) network is with a significant importance in meeting Quality of Service (QoS) aspects of the services and the applications under constrained radio resource (RR) conditions. Furthermore, with ongoing network densification, future networks are to be designed with 3-Dimensional (3D) SCs and more efficient RR allocation mechanisms, while considering location specific information like location based propagation characteristics. In this study, random access channel (RACH) congestion problem is addressed for 3D SC networks while considering the conditions arisen due to 3D spatial positions of the devices, their data types, QoS needs and other priority requirements. As a solution, a learning-assisted fast RR allocation and access prioritization algorithm is presented using Q-learning (QL) and Slotted-ALOHA (S-ALOHA) principles together with device-network coordination. Approximately, 68 %, 43%, 29% and 16% better occupancy rates are shown for the initial iterations of the algorithm for the case of maximum or a greater number of devices giving impressive results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".