Performance analysis of nonlinear decision-feedback detection in CDMA systems over Rayleigh fading channels
Bibliographic record
Abstract
The precombining minimum mean-squared error decision-feedback detector (MMSE-DFD) (termed precombining MMSE-DFD) is shown to be independent of the instantaneous channel coefficients. Hence it can be implemented adaptively unlike the standard MMSE-DFD (termed postcombining MMSE DFD) where it is known to break-down in fast-fading channels due to the severe tracking problems. The performance of the various MMSE-DFDs is investigated and compared to the linear MMSE detector (i.e., no feedback) over different fading channel models and using the Gaussian approximation. Our numerical results show that, in all cases, the postcombining receivers perform significantly better than the corresponding precombining receivers. However, the advantage of the postcombining detection over the precombining detection is shown to be significantly smaller for a 2-stage MMSE-DFD that consists of two cascaded MMSE-DFDs each performing successive interference cancel- lation prior to signal combining. Finally, we derive a simple adaptive implementation for the 2-stage detector where the receiver coefficients are adjusted using the normalized least- mean-square (NLMS) algorithm.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".