Application of noisy-independent component analysis for CDMA signal separation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it