LTE multi-cell dynamic resource allocation for wireless network virtualization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of native wireless network virtualization implies introducing a new set of base station schedulers that considers the efficient allocation of wireless resources to different Service Providers (SPs) based on flexible Service Level Agreements (SLAs). In this paper we develop an efficient and fast centralized heuristic to allocate the radio resource blocks in multi-cell LTE networks. The scheme maximizes the network-wide sum rate while keeping track of the SLA of each SP expressed as a minimum bandwidth allocation in each cell. We also propose an iterative solution procedure for the non-convex power allocation problem based on DC programming. We find that the results of the heuristic are quite close to those of the iterative method. We also show that there is a significant rate reduction due to the service contracts. Finally, we find that even if the heuristic cannot meet all the requirements in one particular scheduling period, it does provide the required rate over a large number of periods.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it