Optimal Multi-Stage Clipping for Impulsive Noise Mitigation in OFDM-NOMA Systems
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) was recently proposed as a viable technology and a strong candidate for future wireless communications. On the other hand, numerous emerging technologies present environments which are characterized by the presence of impulsive electromagnetic interference, known as impulsive noise. This noise can significantly affect the reliability of the communication link. Therefore, given the distinct characteristics of NOMA, this article proposes a multistage clipping approach for the mitigation of impulsive noise in orthogonal frequency division multiplexing (OFDM)-NOMA systems. Moreover, for each stage, considering the weighted combination criterion, we proceed to derive the optimum clipping threshold. Provided Monte-Carlo simulation results prove that our proposed approach outperforms the conventional singlestage clipping applied to NOMA and that the proposed optimum threshold significantly reduces the detrimental effects of impulsive noise in OFDM-NOMA systems where a gain of at least 10dB compared to the conventional clipping approach case is achieved by the successive interference cancellation (SIC) user.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".