Novel Efficient Multiwavelet-Based Modulation for Downlink NOMA Systems
Bibliographic record
Abstract
A new modulation scheme using multiwavelets for downlink non-orthogonal multiple-access (NOMA) transceivers is presented in this work. Multiwavelets leading to remarkable spectral diversity are exploited to modulate/demodulate the superimposed multi-user signals in downlink NOMA systems, where the discrete multiwavelet transform and the inverse discrete multiwavelet transform are invoked in the new demodulator of the user equipment and the new modulator of the base station, respectively. The performance evaluation and the computational-complexity analysis of our proposed new NOMA scheme are also conducted. Simulation results demonstrate that the proposed new scheme can significantly increase the system capacity while dramatically suppressing the peak-to-average-power ratio (PAPR) compared to the conventional OFDM-based NOMA approach. Meanwhile, our new scheme can achieve the same system capacity as the scalar-wavelet-based NOMA approach but with a lower PAPR at little extra cost of computational-complexity. All of these three aforementioned NOMA schemes can result in the identical bit-error-rate under the same signal-to-noise ratio condition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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".