Maintaining Utility Fairness Using Weighting Factors in Wireless Networks
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
Maintaining fairness using weighting factors is a common approach in resource allocation. However, computing weighting factors for multiservice wireless networks is not trivial because users' rate requirements are heterogeneous and their channel gains are variable. In this paper, we propose weighting factor computation and scheduling schemes for orthogonal frequency division multiple access (OFDMA) networks. The weighting factor computation scheme determines each user's share of rate for maintaining a utility notion of fairness. We then present a scheduling scheme which takes the users' weighting factors into consideration to allocate sub-carriers and power in OFDMA networks. The simulation results demonstrate that the proposed scheduling scheme outperforms an opportunistic scheme in terms of fairness performance in different scenarios, where the users are fixed or mobile.
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 | 0.001 |
| 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