A hybrid technique for the PAPR reduction of NOMA waveform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Non‐orthogonal multiple access (NOMA) is a great contender for future cellular modulation due to its desirable properties like massive connectivity, high data rate transmission, and high spectral efficiency. However, its peak‐to‐average power ratio (PAPR) is significant, which becomes a significant disadvantage for the efficient operability of the NOMA waveform compared to current techniques. Several PAPR reduction algorithms like selective mapping (SLM), partial transmission sequence (PTS), and companding techniques have been proposed to lower the PAPR of multicarrier waveforms (MCWs). PTS reduces the PAPR but has high complexity. On the other hand, SLM has a less complex framework, but its PAPR performance is not as efficient as PTS. Companding methods reduce the PAPR by compressing the signals at the transmitter, which unfortunately reduces the dynamic range of the signal. In this work, we propose a hybrid algorithm (SLM + PTS) with a companding method for the first time for the NOMA waveform, which efficiently reduces the PAPR with low computational complexity. Furthermore, we compare the performances of a host of candidate algorithms like SLM, PTS, hybrid (SLM + PTS), hybrid + A law (SLM–PTS–A law), and hybrid + Mu law (SLM–PTS–Mu law). The results of the experiments show that the hybrid + Mu law did a better job than the existing PAPR reduction algorithms.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 | 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 it