Fair Allocation of Subcarrier and Power in an OFDMA Wireless Mesh Network
Bibliographic record
Abstract
This paper presents a new fair scheduling scheme for orthogonal frequency-division multiple-access-based wireless mesh networks (WMNs), which fairly allocates subcarriers and power to mesh routers (MRs) and mesh clients to maximize the Nash bargaining solution fairness criterion. In WMNs, since not all the information necessary for scheduling is available at a central scheduler (e.g., MR), it is advantageous to involve the MR and as many mesh clients as possible in distributed scheduling based on the limited information that is available locally at each node. Instead of solving a single global control problem, we hierarchically decouple the subcarrier and power allocation problem into two subproblems, where the MR allocates groups of subcarriers to the mesh clients, and each mesh client allocates transmit power among its subcarriers to each of its outgoing links. We formulate the two subproblems by nonlinear integer programming and nonlinear mixed integer programming, respectively. A simple and efficient solution algorithm is developed for the MR's problem. Also, a closed-form solution is obtained by transforming the mesh client's problem into a time-division scheduling problem. Extensive simulation results demonstrate that the proposed scheme provides fair opportunities to the respective users (mesh clients) and a comparable overall end-to-end rate when the number of mesh clients increases
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".