Cross-layer dynamic subcarrier allocation in multiuser OFDM system with MAC layer diverse QoS constraints
Bibliographic record
Abstract
Multiuser OFDM (MU-OFDM) is widely applied nowadays to provide diverse Quality of Service (QoS) for multiple users. Subcarrier allocation according to the instantaneous channel state information (CSI) in MU-OFDM system has been well studied while the research of allocating subcarriers to each user constrained by diverse QoS requirements still remains large space undisclosed. In this paper, in order to meet users' diverse QoS requirements in MU-OFDM system, we propose a cross-layer dynamic subcarrier allocation algorithm where users' MAC layer diverse QoS requirements and the subcarrier allocation at PHY layer are jointly considered. The MAC layer queue status is modeled as a finite-state Markov chain (FSMC), using which the QoS constraints are transformed to the minimal PHY layer data rate requirement of each user. A sub-optimal dynamic subcarrier allocation algorithm is then proposed not only to satisfy the PHY layer data rate but also to significantly reduce the computational burden, aiming at maximizing system capacity. Finally, we verify the proposed cross-layer algorithm by simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".