A Random Fourier Feature Based Receiver Detection for Enhanced BER Performance in Nonlinear PD-NOMA
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) has been proposed as a potential enabler for massive connectivity. Among the different NOMA schemes, power-domain (PD-NOMA) is particularly appealing as it improves user fairness while enhancing spectral efficiency. However, the practical implementation of NOMA faces several challenges, including radio frequency impairments, such as power-amplifier (PA) nonlinearity, which can limit its performance. In this paper, we study the impact of PA nonlinearity on the detection performance of PD-NOMA, and propose a random Fourier feature (RFF) based solution to mitigate the effects of such imperfections. Computer simulations carried out assuming different PA nonlinearity types demonstrate that the proposed RFF based algorithm considerably improves the BER performance and achieves results that are close to the ideal case. Lastly, the analytical proof of our proposed algorithms performance is provided to support our simulation results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".