Network Coding Aware Dynamic Subcarrier Assignment in OFDMA Wireless Networks
Bibliographic record
Abstract
Taking advantage of the frequency diversity and multiuser diversity in OFDMA based wireless networks, dynamic subcarrier assignment mechanisms have shown to be able to achieve much higher downlink capacity than static assignment. A rich literature exists that proposes MAC and physical layer schemes aiming at exploiting the diversity gain with low implementation complexity. In this paper, we propose a cross layer approach that explores the joint advantage of network coding and dynamic subcarrier assignment. Our algorithm improves the bandwidth efficiency of OFDMA downlink by encoding frames of the mobile stations that exchange information. We highlight a tradeoff between diversity gain and the network coding advantage, which is critical to the network performance. To explore the tradeoff, we formulate the coding aware dynamic assignment scheme as a mixed integer program, and design a polynomial time heuristic that can be used in practical systems. Based on a network flow formulation and a penalty scheme, our heuristic well approximates the performance of an optimal algorithm, in terms of both throughput and fairness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".