MDC-NOMA: Multiple Description Coding-Based Nonorthogonal Multiple Access for Image Transmission
Bibliographic record
Abstract
Recently, nonorthogonal multiple access (NOMA) technology has emerged as a key technology for enhancing power and spectrum efficiency of fifth-generation (5G) wireless networks. On the one hand, multiple-description coding (MDC) is a coding technique with high efficiency and strong antiinterference capabilities that can combat burst interference and solve the problem of unreliable transmission due to link impairments and network congestion. In this article, by combining the principles of MDC and NOMA, we propose a hybrid MDC-NOMA scheme to further improve system throughput and transmission robustness. In the proposed scheme, a base station communicates with two users simultaneously over two orthogonal subchannels. In this setup, one subchannel is sufficient to reconstruct an image of acceptable quality. We derive the closed-form expressions for the outage probability and ergodic rate and analyze its peak signal to noise ratio, bit error rate, and visual transmission performance. Furthermore, numerical and simulation results are presented in order to validate the analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".