Comparison of Multiuser Detection Techniques for Asynchronous Multirate DS-CDMA Systems
Bibliographic record
Abstract
This paper compares performance of various multiuser receiver structures for asynchronous multirate DS-CDMA systems over multipath Rayleigh fading channels. Bit-error-rates (BER) of the novel decorrelator based successive interference cancellation (DBSIC) detector and other commonly used suboptimum multiuser receivers such as the decor relating, minimum mean-square-error (MMSE), SIC, parallel interference cancellation (PIC) and decorrelating decision-feedback (DF) detectors are evaluated for variable processing gain (VPG) CDMA systems with both perfect and imperfect channel side information. Simulation results show that DBSIC outperforms all other considered multiuser detection techniques in various multirate scenarios including cases with more than two rates at the expense of some additional complexity. In particular, DBSIC provides gains in the case where some physical users have increased data rates (i.e. heavier system's load in terms of virtual users). It is also observed that, while all the receivers suffer degradation in performance in the case of imperfect channel estimates, DBSIC still outperforms the other detection schemes
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".