A blind adaptive receiver for interference suppression and multipath reception in long-code DS-CDMA
Bibliographic record
Abstract
This paper examines a blind adaptive implementation of a recently proposed linear minimum mean square error (LMMSE) receiver for code-division multiple-access (CDMA) systems with aperiodic spreading sequences in multipath channels. The receiver has been previously shown to perform multiple-access interference (MAI) suppression and multipath diversity combining. The adaptive implementation is based on a fractionally spaced equalizer (FSE) whose taps are updated by the leaky constant modulus algorithm (LCMA) in the cold start and the decision-directed least-mean-square (DD-LMS) algorithm when the channel eye is opened. The LCMA adds a quadratic constraint, defined as the squared norm of the FSE weight vector, to the constant modulus (CM) cost function. Simulation results show that the LCMA with a uniform initialization strategy (all taps equally set to a small non-zero value) acquires all paths associated with the desired user, suppresses MAI, and opens the channel eye for the DD-LMS algorithm to converge to the proximity of the MMSE solution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".