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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".