Tone diversity for OFDMA in broadband wireless communications
Bibliographic record
Abstract
Abstract This paper presents two group‐based tone diversity schemes, namely, group‐orthogonal tone‐combining‐diversity (GO‐TCD) and group‐optimal tone‐selection‐diversity (GO‐TSD), for orthogonal frequency division multiple access (OFDMA) system in broadband wireless communications. In both schemes, the entire frequency band ofNsub‐carriers is divided intoNG = N/Lfsub‐carrier groups with each havingLfsub‐carriers. When the channel knowledge is not available at the transmitter, GO‐TCD, which has a split‐and‐group structure to reduce the peak‐to‐average ratio (PAR) and employs the multi‐user detection for maximum‐likelihood (ML) estimation to increase the number of active users, is proposed and shown to provide a lower PAR and similar performance as compared with the group‐orthogonal multi‐carrier CDMA, and outperforms the random‐hopping (RH)‐OFDMA and matched‐filter based MC‐CDMA. When the channel knowledge is available at the transmitter, GO‐TSD can be used to select the best sub‐carrier in a given sub‐carrier group for any active user and shown to offer a throughput comparable to the optimal tone selection but with much less complexity. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".