Capacity Improvement of DS-CDMA Mobile Satellite Communication System Using the Adaptive Duplicated Filters and Interference Canceller
Bibliographic record
Abstract
An efficient adaptive multistage multiuser detection (MUD) method known as adaptive duplicated filters and interference canceller (ADIC), is presented in the context of an uplink terrestrial mobile network DS-CDMA. The performance of this proposed MUD scheme has been proven equivalent to a DF soft MPIC while affording complexity reductions by a factor of 4 (64 kbps). In this paper, an ADIC receiver is applied to yield a link satellite mobile environment based on DS-CDMA systems and able to outperform DF soft MPIC and soft MPIC methods using complexity reduction. At 64 kbps, compared to DF soft MPIC, soft MPIC and Rake receivers, ADIC MUD allows capacity increases of 8%, 30%, and 160% respectively.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".