Multiuser Detection of DS-CDMA Signals Using Partial Parallel Interference Cancellation in Satellite Communications
Bibliographic record
Abstract
Multiuser detection (MUD) using parallel interference cancellation (PIC) technique provides a good complexity, latency, and performance compromise. This technique is suitable for satellite systems using either code-division multiple-access (CDMA) or a combination of time-division multiple-access (TDMA) and CDMA. We offer a new scheme that is a combination of soft and hard PIC detectors whose performance is superior to that of the other famous suboptimal detectors. In soft partial parallel interference cancellation (PPIC), in the first few stages, when the performance is still poor, the accurate knowledge of power and phase cannot be of much use. However, in the following stages, accurate power and phase estimation can improve the performance. This coincides with the time when the decisions are reliable enough to be used for parameter estimation. In our scheme, after a few stages of soft interference cancellation (IC), estimation of the parameters will start. Having these estimates, in the subsequent stages hard IC is performed. The complexity of this scheme grows linearly with the number of users. Moreover, this scheme is much faster than other receivers such as successive interference cancellation (SIC). PIC detectors are usually studied in equal-power case, i.e., a perfect power control scheme is assumed. In this paper, PIC detector in a near-far condition where user signals arrive at the receiver with different power levels is also investigated.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".