Analysis of Peak to Average Power in the 5G NOMA-FBMC Waveform
Bibliographic record
Abstract
Objectives: In this work, we investigate suitable techniques to reduce the Peak to Average Power Ratio (PAPR) for advanced modulation schemes in order to obtain better performance than current or commonly used modulation schemes for Fourth Generation (4G) and Fifth Generation (5G). Methods: The proposed scheme incorporates a combination of Selective Mapping (SLM) and Partial Transmission Scheme (PTS) and thereby efficiently minimizes the PAPR and the complexity of the framework. Further, it is seen that the proposed algorithm is crucial to achieving better spectral and power characteristics compared with the existing waveforms. Findings: The comparative results of the bit error rate (BER) and PAPR of the advanced SLM-PTS when applied to the OFDM, FBMC, NOMA, and NOMA-FBMC structures are shown, and it is found that the power and complexity are significantly decreased in the advanced waveforms, which makes the proposed algorithm efficient for the advanced waveforms. Novelty: A natural motivation for future modulation schemes is to harmoniously merge the newer modulation technique, Filter Bank Multi Carrier (FBMC), with the Non-Orthogonal Multiple Access (NOMA) framework. This has led to a recent modulation paradigm called FBMC-NOMA, wherein the NOMA power domain principle is applied to a group of FMBC modulated signals. The proposed SLM-PTS-based NOMA-FBMC structure efficiently enhances the throughput and PAPR performance for 5G and beyond 5G systems. Keywords: PAPR; FBMC; SLM; PTS; NOMA
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".