Sparse Code Multiple Access With Time Spreading and Repetitive Transmissions
Bibliographic record
Abstract
ABSTRACT For the next‐generation communication systems, to improve spectral efficiency and increase the data rate, new multiple access techniques have been investigated. Orthogonal multiple access techniques are widely used in traditional communication systems while nonorthogonal multiple access (NOMA), proposed in 5G, has been a promising technology for satisfying the demand for future wireless communication networks. Sparse code multiple access (SCMA) is a code‐domain NOMA method that provides diversity gain with signal constellation coding. However, to increase the performance of SCMA, there are only limited works provided in the literature in terms of codebook design and receiver design. In this paper, a new multiple‐access model is proposed by applying various diversity techniques for downlink SCMA. The performance of the proposed model is evaluated with both computer simulations and theoretical analysis. Results show that the proposed model provides a 1.6 dB gain in terms of the bit error rate (BER) under the Rayleigh fading channel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".