Comparison of Interference Cancellation Schemes for Non-Orthogonal Multiple Access System
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Bibliographic record
Abstract
Three potential interference cancellation schemes are compared for the application to a non-orthogonal multiple access communication system. One is the conventional hard successive interference cancellation (SIC) scheme based on independent single-user decodings. The other two, proposed in this paper, are a soft-in soft-out parallel interference cancellation (SISO-PIC) and a hybrid interference cancellation (HIC). The SISO-PIC is an improved joint iterative multi-user detection scheme, which has lower complexity than the prevalent multi-user detection. The HIC combines the advantages of the above two schemes to permit users to be successively processed by a SISO-PIC window according to their receive power levels. A comprehensive comparison is given for these three schemes in aspects of error propagation, detection delay, and complexity when a practical channel code, repeat-accumulate code, is employed. Numerical results show that HIC is a trade-off scheme of the three aspects.
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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 | 0.000 |
| Open science | 0.000 | 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 it