An opportunistic subcarrier allocation algorithm based on cooperative coefficient for OFDM relaying systems
Bibliographic record
Abstract
The downlink subcarrier allocation in a cooperative multiuser system with an amplify-and-forward relaying is studied for OFDM systems. We use the cooperation coefficient (Γ) from the literature as a basis for subcarrier allocation. Mean of the coefficient is first derived for Rayleigh faded channel. Then, a well known subcarrier allocation algorithm namely Max-Min is modified to make use of Γ. In this algorithm, the base station (BS) allocates the subcarriers to the users based on the combination of the direct and weighted indirect subcarrier gains. How to weigh the indirect path depends on the amount of information about Γ available at BS. Three different scenarios, each with varying level of implementation complexity, are considered: (a) knowledge of the instantaneous Γ at BS, (b) knowledge of the mean of Γ at BS, and (c) an estimate of the mean of Γ at BS. Finally, the performance of the Γ-modified Max-Min algorithm is evaluated for the three scenarios using Monte-Carlo simulation. It is shown that (i) the proposed algorithm outperforms the non-cooperative counterpart in terms of total throughput, (ii) the throughput gain moderates with larger group size showing the group diversity behavior in opportunistic communication and (iii) using the mean of the coefficient, instead of instantaneous coefficient, in cooperative subcarrier allocation provides comparable performance in Rayleigh faded channel environment, giving implementation advantage to the mean-based implementation of the Γ-modified Max-Min algorithm.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".