Iterative multiuser detection and error control code decoding in random CDMA
Bibliographic record
Abstract
The combination of forward-error control (FEC) coding with code-division multiple access (CDMA) using random spreading sequences is considered. Such systems can be viewed as serially concatenated, and iterative (turbo) decoding principles can be applied. An analysis of the component systems is presented by studying variance input-output behaviors. Soft symbols are derived from the FEC decoders' extrinsic information outputs. A variance measure of the error of these soft symbols is used in a variance transfer (VT) analysis between component systems to give an accurate description of the convergence properties of the iterative joint decoder. This VT analysis is applied to three CDMA interference resolution component-systems: 1) simple interference cancellation; 2) per-user minimum-mean-square-error (MMSE) cancellation; and 3) a low-complexity multistage method that is proposed to approximate the complex MMSE filter. Closed-form equations for large-scale systems are presented for all three filters as the number of users K/spl rarr//spl infin/. It is shown that per-user MMSE filtering has a spectral advantage of 1+1//spl alpha/ over simple matched filtering, where /spl alpha/ is the system load supported by the latter, and the multistage filter can approach the MMSE performance with a few stages. Moreover, the number of filter stages to achieve a certain performance is independent of the number of users. The impact of the FEC systems is studied, and it is shown that for low signal-to-noise ratios (SNRs) powerful concatenated codes are required, while for higher SNRs, simple single-error control codes support higher system loads. FEC code examples and simulation results are presented and put in contrast with the capacity limits of the random CDMA channel.
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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) | 0.000 | 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 | 0.000 |
| 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".