An iterative semi-blind multiuser detector for coded MC-CDMA uplink
Bibliographic record
Abstract
Iterative multiuser detection is a powerful signal processing technique to increase performance and capacity of coded CDMA systems. However, previously proposed iterative multiuser receivers have been based on complete knowledge of spreading codes of all users in the system. Performance of these receivers degrades significantly in the presence of unknown interference, caused by out-of-cell users whose spreading codes are not known. A significant part of the interference in a typical uplink system is often due to such unknown users. In this paper, we propose an iterative semi-blind receiver for a coded system in such an uplink environment. It is based on the minimum mean square error criterion. The proposed iterative receiver utilizes known users' information for the computation of log-likelihood ratios, while blindly suppressing unknown interference. We consider a multicarrier CDMA system, which has received considerable attention for future high-speed wireless systems. Turbo code is used for channel coding, with log-MAP decoding. Simulation results demonstrate that the proposed iterative semi-blind method offers substantial performance gain over that of the conventional noniterative and non blind iterative receivers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".