Joint iterative multiuser detection and channel estimation for differentially coded asynchronous CDMA systems
Bibliographic record
Abstract
A joint channel estimation and multiuser detection scheme is proposed for the uplink of differentially coded asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems. A channel estimation is developed by exploiting the orthogonality between signal and noise subspaces and the soft estimate of coded bits of each user in conjunction with the output of the multiuser detector. The blind subspace channel estimation yields channel estimation with discrete valued phase ambiguities. By exploiting the robust combination of recursive systematic convolutional (RSC) decoder with a differential decoder for each user, an algorithm to resolve the phase error and simultaneously improving signal detection is proposed. Based on the proposed channel estimator and phase corrector, an iterative receiver is introduced for joint channel estimation, soft interference cancellation, linear minimum mean-square error (MMSE) filtering and iterative channel decoding. By exchanging soft information between different stages, the receiver performance is improved via iteration. Simulation results show that very close to single user performance in additive white Gaussian noise is possible
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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) | 0.001 | 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) | 0.000 | 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 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".