Proactive clipping and filtering for trading in- and out-of-band distortion in OFDM transmitters
Bibliographic record
Abstract
The high peak-to-average power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM) signals combined with nonlinear transmitter characteristics results in poor bit error rate (BER) performance and causes out-of-band spectrum regrowth of the waveforms entering the channel. A popular technique with low complexity to reduce nonlinear distortions in memoryless high power amplifiers (HPA) is amplitude clipping which limits the peak envelope of the HPA input to a predetermined level. This paper characterizes in- and out-of-band distortion as a result of clipping the Nyquist sampled OFDM signal which is then filtered and subsequently passed through the transmitter HPA. For transmitters with limited filtering of the HPA output, this offers a trade-off between out-of-band distortion reduction and in-band distortion increase in the transmitted OFDM signal. To this end, this paper documents spectral regrowth reduction versus irreducible BER using computer simulations. Also, to verify the feasibility of the pre-distortion system, the clipping and filtering has been implemented on a field-programmable gate array (FPGA) platform.
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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".