A Fast Adaptive Algorithm for MMSE Receivers in DS-CDMA Systems
Bibliographic record
Abstract
In this letter, we consider the application of an iterative interference cancelation (IC) scheme to improve the speed of convergence of the adaptive minimum mean-squared error (MMSE) receiver for the reverse-link of a direct-sequence code-division multiple-access (DS-CDMA) system. Our aim is to reduce the overhead introduced during the receiver's training period. This will be achieved using an iterative interference cancelation algorithm such as the parallel interference cancelation (PIC) algorithm. The proposed iterative algorithm makes use of the available knowledge of all users' training sequences at the base-station receiver to jointly cancel multiple-access interference (MAI) and adapts to the MMSE optimum filter taps using the combined adaptive MMSE/PIC receiver. We employ the proposed iterative algorithm to both the least mean square and the recursive least squares algorithms where we show that a significant improvement in terms of convergence speed is achieved. Moreover, we demonstrate the near-far resistance of the proposed receiver.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".