SOVA-based soft interference cancellation for multi-user FEC-coded DS-CDMA for 384 kbps transmission in WCDMA
Bibliographic record
Abstract
This paper introduces a multi-user detection (MUD) algorithm applying iterative soft interference cancellation for a direct-sequence code-division multiple-access (DS-CDMA) system with forward error correction (FEC) coding. The proposed algorithm involves iterative decoding and soft interference cancellation that uses SOVA-based, FEC decoder decision feedback. The investigation of the iterative algorithm uses a full CDMA system model which involves three types of commonly used spreading sequences: Gold, Kasami, and extended S(2) sequences. Computer simulations are used to obtain the performance of the proposed algorithm with each of the spreading sequences. For an AWGN channel, the classical CDMA detector with no MUD supports 5 high-bit-rate users in 5 MHz WCDMA at a bit-error-rate of 10/sup -5/ for the constraint length 7, rate- 1/2 convolutionally-coded system. The iterative algorithm proposed doubles the number of users in the targeted application when SOVA is used for soft decision feedback. Perfect power control and chip and carrier synchronization, with no multipath fading, are assumed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 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".