Novel pilot-free adaptive modulation for wireless OFDM systems
Bibliographic record
Abstract
Orthogonal frequency division multiplexing (OFDM) is the contemporary technology adopted for digital audio/video broadcasting as well as wireless local-area and metropolitan-area networks. Since the wireless multimedia services often have different quality-of-service requirements and their performance is sensitive to the channel conditions, the conventional fixed OFDM modulation scheme might not be a satisfactory solution nowadays. In this paper, we introduce a novel pilot-free adaptive modulation scheme, which is bandwidth-efficient and allows variable data rates, for the future robust OFDM systems. We design a number of modulation modes in a combination of different constellation sizes and different polynomial cancellation coding methods (PCC) to combat the crucial intercarrier interference problem. Instead of estimating the channel quality based on the overhead pilot symbols, we propose to directly estimate the signal-to-noise ratio (SNR) without using any pilot. Besides, our scheme offers more modulation modes than some other existing adaptive modulation methods which are simply based on different constellation sizes. According to the Monte Carlo simulations, the empirical results show that our adaptive modulation scheme, in most channel conditions (SNR/spl ges/15 dB), not only can satisfy the predetermined bit error rate (BER) requirement (BER/spl les/10/sup -4/) but also can dynamically enhance the throughputs in the rather clean environments with high SNR values.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".