Multiuser Detection for S-UMTS and GMR-1 Mobile Systems
Bibliographic record
Abstract
Application of multiuser detection (MUD) techniques to current and to-be deployed mobile communication systems is currently under study within the scientific and industrial communities. In this paper, we dwell on the applicability of successive interference cancellation (SIC) and turbo spatial minimum mean squared error-interference cancellation (turbo SMMSE-IC) to a CDMA and a TDMA mobile satellite system (MSS), i.e. S-UMTS and GMR-1, which provide seamless service and coverage extension to their terrestrial counterparts: UMTS and GSM. The adoption of MUD techniques for these two systems turns out to be instrumental for achieving the required high spectral efficiency, but also very challenging due to the peculiarities of the MSS environment. Performance of the two techniques is evaluated considering correlated Rician fading, non-linear distortion introduced by high power amplifiers, and residual parameter estimation errors. Our results show that MUD techniques allow to largely increase MSS throughput: in S-UMTS a number of users as large as twice the spreading factor can be sustained, while a full frequency reuse can be adopted in GMR-1
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".