Symbol-aided channel estimation and multiuser detection for CDMA systems using a decorrelating detector
Bibliographic record
Abstract
A decorrelating filter detector is considered for multiuser detection. This filter operates on the outputs of matched filters which are matched to the original code waveforms of system. A maximum likelihood estimation to estimate the parameters of the decorrelating filter is proposed and derived. The estimation method is based on inserting known trailing sequences into the information data by all users simultaneously. To achieve the minimum mean square error (MMSE) in the estimation, a criterion for selecting the training sequences is suggested. Orthogonal training sequences are good candidates to approach the MMSE. The estimation method requires a matrix inversion at the end of each training period. An iterative matrix inversion method is suggested to distribute the computational load of the matrix inversion over the training period. The simulation results show some degradations in system performance compared to the case where a perfect knowledge of the channel is assumed. This degradation is clearly due to errors in the channel estimation.
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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 | 0.001 |
| 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".