A New SS-VDS-SLM Technique for PAPR Reduction in High-Mobility Real-Time OTSM
Bibliographic record
Abstract
This paper presents a novel semi-supervised vectored delay-sequency selective mapping (SS-VDS-SLM) technique designed to address the peak-to-average power ratio (PAPR) reduction challenge in orthogonal time sequency multiplexing (OTSM) systems. The proposed method leverages in-phase-quadrature delay-sequency symbol vectors and integrates a hybrid agglomerative hierarchical clustering with a weighted K-nearest neighbour algorithm to effectively reduce transmission computational complexity (CC) and transmission latency (TL) and enhance spectral efficiency (SE), while simultaneously mitigating out-of-band emissions (OOB). The approach ensures that the bit error rate (BER) and error vector magnitude (EVM) performance are preserved within the 3GPP doubly dispersive high-mobility delay Doppler multipath channels. Benchmark comparisons against ten contemporary PAPR reduction schemes in both OTSM, orthogonal frequency division multiplexing (OFDM) and both of their variants including the base orthogonal time frequency space (OTFS), demonstrate the superior performance of the SS-VDS-SLM technique in OTSM. Further, the proposed method offers a more effective trade-off among the PAPR, OOB, CC, TL, SE, BER, and EVM performance metrics compared to the ten considered contemporary schemes. Thus, the proposed SS-VDS-SLM technique provides a more efficient and scalable solution for reducing PAPR in high-mobility speeds of 500 km/h and 1000 km/h in real-time OTSM system applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".