A SLM Scheme for PAPR Reduction in Polar Coded OFDM-IM Systems Without Using Side Information
Bibliographic record
Abstract
In the present article, we propose a novel selected mapping (SLM) scheme based on a Polar coding technique for peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. The proposed scheme by utilizing random frozen bits in polar codes generates different number of candidate sequences. Then the candidate sequence with the smallest PAPR is selected for transmission. The proposed scheme can be considered as one block in an OFDM-IM system that performs both PAPR reduction and error correction. For a fair comparison, the performance of an OFDM-IM system based on the proposed scheme is compared with a system that carries out the same operations with two separate blocks where a Polar encoder is cascaded with the one for PAPR reduction (such as conventional SLM or PTS), and a Polar coded OFDM-IM system without any PAPR reduction scheme. Moreover, based on our proposed SLM scheme, a novel receiver without using side information (SI) is proposed. This SI free receiver is based on the successive cancellation list (SCL) polar decoding scheme. Simulation results show that as the Polar code can bring both error correction and PAPR reduction capabilities by using our proposed scheme. Also, the proposed SI free receiver can achieve similar bit error rate (BER) performance as that of the ideal case in both additive Gaussian white noise (AWGN) and frequency selective channels.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".