A Fair Subcarrier Allocation Algorithm for Cooperative Multiuser OFDM Systems with Grouped Users
Bibliographic record
Abstract
Dynamic resource allocation improves the performance of multiuser OFDM systems by exploiting multiuser diversity. Cooperative diversity is a technique where multiple users share their resources to realize a spatial diversity gain through cooperation. In this paper, the problem of downlink subcarrier allocation in a cooperative multiuser system is investigated. We assume a single-cell case where the base station has perfect knowledge of subchannel gains and all the mobile users are paired in cooperative groups. The mobile users in one cooperative group relay their partner data stream which is received from base station using a time division protocol. Based on the capacity contribution from the relaying terminal, a new parameter called cooperation coefficient is introduced. Considering the cooperation among users in assigning the subcarriers, a new subcarrier allocation algorithm is proposed. The performance of the proposed algorithm is then evaluated for different values of cooperation coefficients and is shown to maintain the same level of fairness but higher data rates compared with a similar algorithm which does not consider cooperation.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".