A Polar-Coded PAPR Reduction Scheme Based On Hybrid Index Modulation
Bibliographic record
Abstract
Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a promising technique for next-generation wireless communications due to its superior error performance and flexibility. However, as a type of multi-carrier modulation, it suffers from a high peak-to-average power ratio (PAPR), which can compromise transmission reliability. Therefore, in this paper, a polar-coded PAPR reduction scheme based on hybrid index modulation (PC-HIM) for OFDM-IM is proposed. Also, the proposed framework employs sets of various frozen bits and spatial modulation (SM) to solve the high PAPR issue in OFDM-IM systems. At the receiving side, the detection of the activated antennas status facilitate the recovery of selected frozen bit set, whose indices are embedded in the SM operations. Therefore, the need for transmitting side information, which is a necessary process in probabilistic PAPR reduction schemes, can be eliminated. Further, to enhance detection accuracy, a construction method for frozen bit sets based on Hadamard matrix is proposed. Additionally, to address the high complexity inherent in the proposed PC-HIM PAPR reduction framework, a low-complexity version, termed LC-PC-HIM, is proposed. This framework simplifies both the transmitting and receiving operations through a redesign of the information processing procedures and the selected frozen bit set detection step. Simulation results demonstrate that the proposed PAPR reduction scheme (PC-HIM and LC-PC-HIM) outperforms existing polar code-based PAPR reduction schemes by at most 12.5%, delivering the most effective PAPR reduction performance. Furthermore, the proposed receiving approach achieves error performance comparable to that of a receiver utilizing perfect side information.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".