Impact of Direct Sequence Spreading on the Channel Capacity of Binary Non-Gaussian CDMA
Bibliographic record
Abstract
Multi-user interference (MUI) severely degrades performance in wireless CDMA transmission. Due to the central limit theorem, such interference is usually (near) Gaussian distributed (Vembu and Viterbi, 1996; and Verdu, 1998). Recently, it has been shown that for frequency/time hopping CDMA with single-user demodulation/decoding at the receiver, one can intentionally create appropriate non-Gaussian multi-user interference that allows increasing the capacity up to five times when compared to CDMA with Gaussian MUI (Garba and Bajcsy, 2006 and 2008). This paper considers the impact of direct sequence (DS) spreading on the capacity increases for CDMA with nonGaussian MUI. First, appropriate channel models are constructed for non-Gaussian DS CDMA when traditional single-user matched filter demodulator is used as well as when the optimal single-user CDMA receiver is used. The obtained numerical capacity results show that the nonGaussian CDMA capacity gains can be realized with appropriate CDMA receiver.
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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 | 0.009 |
| 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 | 0.001 |
| 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".