Iterative multiuser detection and decoding for highly correlated narrowband systems and heavily loaded CDMA systems
Bibliographic record
Abstract
When a soft-in soft-out (SISO) iterative multiuser detector cooperates with a bank of SISO single-user decoders, the multiuser system performance can be shown to converge to that of the single-user system. In this paper, a novel SISO iterative detector which employs a decorrelator on the output of soft interference cancellation is proposed. By making use of the advantages of decorrelating detection, the performance of the proposed system is improved with only a small complexity increase compared with pure soft interference cancellation. The performance improvement is reflected in lower bit error rates at low signal-to-noise ratios and in the higher convergence speed. Therefore, the proposed iterative detector is especially suitable for highly correlated narrowband systems and heavily loaded code-division multiple access (CDMA) systems. Both performance analysis and simulation results are providedto show this improvement. Finally, the computational complexity of the detector is analyzed.
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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 | 0.000 |
| 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 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".