Hierarchical resource allocation in femtocell networks using graph algorithms
Bibliographic record
Abstract
This paper presents a hierarchical approach to resource allocation in open-access femtocell networks. The major challenge in femtocell networks is interference management which in our system, based on the Long Term Evolution (LTE) standard, translates to which user should be allocated which physical resource block (or fraction thereof) from which femtocell access point (FAP). The globally optimal solution requires integer programming and is mathematically intractable. We propose a hierarchical three-stage solution: first, the load of each FAP is estimated considering the number of users connected to the FAP, their average channel gain and required data rates. Second, based on each FAP's load, the physical resource blocks (PRBs) are allocated to FAPs in a manner that minimizes the interference by coloring the modified interference graph. Finally, the resource allocation is performed at each FAP considering users' instantaneous channel gain. The two major advantages of this suboptimal approach are the significantly reduced computation complexity and the fact that the proposed algorithm only uses information that is already likely to be available at the nodes executing the relevant optimization step. The performance of the proposed solution is evaluated in networks based on the LTE standard.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".