Application of noisy-independent component analysis for CDMA signal separation
Bibliographic record
Abstract
We propose a noisy-independent component analysis (ICA) based CDMA receiver for multiple access communication channels. ICA is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. We apply noisy-ICA as a post processor attached to a subspace based CDMA receiver in the presence of Gaussian noise. The proposed algorithm reduces the bias caused by channel noise in ordinary ICA algorithms and further decreases the noise by dimension reduction. The downlink CDMA channel is investigated and we assume that only the code of the wanted mobile user is known (i.e., blind symbol separation). We compare the proposed receiver with noisy-ICA ability to the conventional matched filter, well-known linear MMSE multiuser detector and ordinary (noise free) ICA based receivers. Numerical simulations indicate that the performance of the noisy-ICA based receiver is superior to conventional detectors, and comparable to exact-MMSE (i.e., all user codes are known) detection performance in a synchronous multiple access CDMA channel. The performance of the ordinary ICA based CDMA receiver is improved with noise bias removal and principal component analysis (PCA) based dimension reduction.
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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 | 0.002 |
| 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 | 0.000 | 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".