Radio access virtualization: Cell follows user
Bibliographic record
Abstract
Virtual radio access (VRA) technology wherein groups of cooperative transmit points (TPs) form virtual TPs (VTPs) to serve user equipments (UEs) continue to be a thriving subject of research in future generations of wireless networks. In this paper, we propose a technique that uses UE-centric metrics to provide multiple partitions of a wireless network into VTP sets. Our technique guarantees that all UEs enjoy a required gain in at least one VTP; effectively eliminating the edge UE experience in the network. To further enhance the performance of the proposed VRA technique in practical scenarios wherein there is a large load imbalance in the network, we also introduce a new concept of soft UE-TP association in which each UE is partially associated with multiple TPs. The use of our soft association concept when forming VTP sets facilitates load-balancing among various TPs. Finally, a technique is also offered to select the best VTP set at each scheduling resource unit. Numerical simulations are used to demonstrate the performance of our virtualization techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".