PAPR reduction in OFDM based cognitive radio with blockwise-subcarrier activation
Bibliographic record
Abstract
In this paper, we consider the high peak-to-average power ratio (PAPR) problem of non-contiguous orthogonal frequency division multiplexing (NC-OFDM) signals in cognitive radio systems. A high PAPR can lead to saturation in the power amplifier (PA) of secondary users (SUs) and consequently increase spectral spreading, and cause interference to adjacent primary users (PUs). To overcome this problem, existing PAPR reduction techniques for OFDM systems can be applied to NC-OFDM, but they should provide a low PAPR with no side information and relatively low complexity. We consider NC-OFDM with blockwise-subcarrier activation and show that it can intrinsically employ tone reservation (TR) as a PAPR reduction technique. The proposed TR reserves subcarriers within inactive subblocks that are not used by the primary and secondary users. This eliminates data rate loss due to reserved peak reduction tones (PRTs). Further, dynamic PRT allocation in NC-OFDM typically requires side information about the PRT locations to be sent to the receiver. Since we choose PRTs from inactive subblocks, they are simply discarded at the receiver without any side information. The power spectral density (PSD) and bit error rate (BER) are evaluated at the output of the nonlinear PAs to provide a realistic performance comparison.
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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) | 0.000 | 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".