DNN Assisted Anti-Crosstalk Parallel Demodulation Method for PD-NOMA in Vehicular Communications
Bibliographic record
Abstract
Serial interference cancellation (SIC) is a conventional demodulation method for Power domain non-orthogonal multiple access (PD-NOMA) in vehicular communications. In this article, we derived that the traditional SIC method causes inter user crosstalk under specific power allocation strategies, resulting in the inability to correctly decode bit information. We call this phenomenon serial demodulation crosstalk (SDC). In addition, when the channel conditions change, the receiving user is unable to quickly obtain the changes of the power allocation strategy, which might also lead to error demodulation. To address these issues, one Deep-Learning Neural Network (DNN)-assisted anti-crosstalk parallel demodulation method is proposed. After receiving the superimposed signals from the base station, the users can employ a shallow DNN to identify dynamic power ratios in a very short time. Then, the current user information can be demodulated with the proposed anti-SDC parallel demodulation algorithm based on this power ratio. The experimental results show that the proposed DNN-assisted anti-crosstalk parallel demodulation method can adapt to various power ratio states and achieve high-precision demodulation with low computational complexity in the case of high modulation orders.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".