An Asymptotically Fair Subcarrier Allocation Algorithm in OFDM Systems
Bibliographic record
Abstract
Dynamic subcarrier allocation improves the performance of OFDM systems by exploiting multi-user diversity. Fairness index is a parameter which indicates how fairly the sub-carriers are allocated among the users in a system. A greedy sub-carrier allocation algorithm optimizes the system performance in terms of throughput, but it sacrifices the instantaneous fairness. In this paper, we define a new term called "asymptotic fairness". It is shown that for a small number of users greedy subcarrier allocation algorithm leads to a normalized fairness index close to unity after a few channel realizations; therefore, if the users of the same group can wait for a few OFDM symbols, they all can get almost the same data rate. To generalize the idea for larger number of users, we have proposed grouping of the users into smaller group sizes. The proposed subcarrier allocation algorithm allocates the subcarriers in two steps: group-allocation and user-allocation. Group-allocation is performed to maintain fairness among different groups by using a fairness-oriented subcarrier allocation algorithm such as max-min algorithm. In the user-allocation step, the subcarriers are allocated to the users within the group using the greedy algorithm to maximize the throughput. The proposed algorithm is specifically suitable for non-real-time applications. According to the required average fairness index in the system and the maximum allowable waiting time, it is possible to find the proper group size in the proposed two-step subcarrier allocation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".