Rate adaptation using long range channel prediction based on discrete prolate spheroidal sequences
Bibliographic record
Abstract
Rate adaptation which adjusts the transmission rate based on channel quality, plays a key role in the performance of 802.11 networks and is critical for achieving high throughput. Traditional statistics-based methods for rate adaptation are unsuited for mobility scenarios because of the delay involved in statistics gathering. Methods based on channel state information (CSI) perform better but still fall short of optimal performance in high mobility. In this paper, we consider adaptive modulation based on Slepian channel prediction as a basis for rate adaptation in high mobility scenarios. Our proposed method utilizes low-complexity projection on a subspace spanned by discrete prolate spheroidal (DPS) sequences. These sequences are simultaneously bandlimited and highly energy concentrated, and they can be used to obtain a minimum energy bandlimited extension of a finite sequence. Using the predicted channel coefficients, we select the modulation scheme resulting in the highest expected throughput. Unlike Wiener prediction, the proposed method does not require detailed knowledge of the channel correlation, but only of the Doppler bandwidth. Our numerical results show that adaptive modulation based on the low-complexity Slepian prediction is substantially better than using outdated CSI and performs very close to Wiener prediction.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".