A low-complexity iterative multiuser receiver for turbo-coded MC-CDMA system
Bibliographic record
Abstract
A low-complexity iterative multiuser receiver is proposed using groupwise maximum likelihood sequence estimation (MLSE) combined with a modified interference cancellation scheme in a turbo coded MC-CDMA system. It is based on grouping the active users according to their signal strengths and computing the log-likelihood ratios by MLSE among each group of users at the first iteration. For subsequent iterations, a modified interference cancellation scheme that can benefit from code extrinsic information available through decoding is used, for lower complexity. Simulation results are presented in a Rayleigh multipath fading environment for a typical data transmission scheme. Comparisons are made with iterative receivers using the maximum a posteriori (MAP) criterion and soft interference cancellation. Performance very close to that of a MAP based iterative receiver is achieved by this novel scheme within a few iterations, with much lower computational cost. This approach also performs significantly well under near-far conditions.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.002 | 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".