Adaptive bit-interleaved coded OFDM
Bibliographic record
Abstract
Bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) is a popular transmission format for high and also some low data rate power line communication (PLC) systems. In this paper, we consider the problem of optimizing the BIC-OFDM transmitter based on channel information feedback from the receiver side. In particular, the problem of adaptive bit-loading, power allocation, and code (rate) selection for bit-error rate (BER) minimization is addressed. To this end, we first derive BER approximations for BIC-OFDM. The main difference between this analysis and many previous works on adaptive OFDM for PLC is that we include the effect of coding on BER. In the next step, we use the derived expression to formulate adaptive BIC-OFDM for BER minimization and show that the optimization problem can be solved efficiently using greedy-type algorithms. Finally, we propose a simple formula to predict the performance of the optimized system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".