A linear MMSE receiver for multipath asynchronous random-CDMA with chip pulse shaping
Bibliographic record
Abstract
This paper studies the design and implementation of a linear minimum mean-square error (LMMSE) receiver in asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems that employ long-code pseudonoise (PN) sequences and operate in multipath environments. The receiver is shown to be capable of multiple-access interference (MAI) suppression and multipath diversity combining without the knowledge of other users' signature sequences. It maximizes output signal-to-noise ratio (SNR) with the aid of a new chip filter which exploits the cyclostationarity of the received signal and combines all paths of the desired user that fall within its supported time span. The performance of the LMMSE receiver is compared with that of the coherent selective RAKE receiver. The achieved gain is on the order of 0.6-1.8 dB in dense multipath environments of current narrow-band settings and nonuniform power distribution scenarios of next-generation CDMA systems. An example of adaptive implementation of the LMMSE receiver is presented and accompanied by complexity analysis, training curves, and quantitative performance comparisons illustrating the convergence rate and steady-state performance of the adaptive algorithms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".