Resource allocation for relay-aided OFDMA networks with constraints on queue stability
Bibliographic record
Abstract
In this paper we consider subcarrier allocation to relay-assisted users in an OFDMA wireless network. The two-hop downlink transmission is modeled as a network of queues in series. We have studied the queue length evolution at each hop and propose a rate control mechanism to stabilize the considered queues. To the best of our knowledge this is the first work that stabilizes the system without sending queue length information that causes extra transmission overhead. The suggested allocation problem aims to maximize the system throughput with respect to the channel condition and the stability requirements. In order to solve the resulting combinatorial problem we apply a time-shared approach and then convert the outcome to binary allocations which is called anti-relaxation mechanism. Since the optimization problem requires exponential computation time, we have proposed a less complex heuristic approach. The extensive numerical trials confirm that the stability control mechanism balances the data arrival and departure rates which is the required condition for queue stability. When time-sharing is not permitted, the heuristic algorithm can guarantee the queue length stability in significantly smaller execution time comparing to the anti-relaxation method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".