Error Rate Analysis for Bit-Loaded Coded OFDM
Bibliographic record
Abstract
Bit-loaded orthogonal frequency division multiplexing (OFDM) with convolutional coding is a powerful technique for transmission over quasi-static frequency-selective fading channels. Motivated by the lack of appropriate error rate analysis techniques for this popular type of system and channel model, we develop a novel analytical method for bit error rate (BER) estimation of bit-loaded coded OFDM systems operating over frequency-selective quasi-static channels with non- ideal interleaving. To illustrate the application of the proposed analysis, we compare the performance of a number of OFDM bit-loading schemes applied to Multiband OFDM (MB-OFDM) for Ultra-Wideband (UWB) radio, and to IEEE 802.11a/g systems for wireless local area networks (WLANs). We also propose and evaluate a hybrid loading scheme which selects the best loading for each channel realization from amongst a number of candidates.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".