Turbo multiuser detection with integrated channel estimation for differentially coded asynchronous CDMA systems
Bibliographic record
Abstract
The problem of joint iterative multiuser detection and channel estimation for the uplink of differentially coded asynchronous direct-sequence code-division multiple-access (DS- CDMA) systems with periodic spreading sequences is considered. The proposed receiver consists of a first stage of channel esti- mation, soft interference cancellation and soft-in-soft-out (SISO) multiuser filtering, followed by a second stage of single-user iterative decoders. The single-user iterative decoder for each user consists of a powerful combination of a recursive systematic convolutional (RSC) decoder and a differential decoder which incorporate their soft information in an iterative fashion. In terms of channel estimation for the first iteration, the multipath channel is estimated blindly by exploiting orthogonality between the signal and noise subspaces. In the next iterations, the channel estimates are improved by using the soft estimates of the coded bits of each user provided by the single-user iterative decoders in conjunction with the output of multiuser detector. By exchanging soft information between the two stages, the receiver performance is improved through iteration. Simulation results demonstrate that the proposed Turbo multiuser receiver offers performance approaching the single-user bound. Keywords-Code-division multiple-access (CDMA) systems, Iterative channel estimation, Differential encoding, Minimum- mean-square-error (MMSE) filtering.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".