Blind adaptive multiuser detection using linear parallel interference cancellation for CDMA systems
Bibliographic record
Abstract
We consider the application of parallel interference cancellation (PIC) schemes to improve both convergence speed and bit error rate (BER) performance of blind adaptive minimum mean output-energy (MMOE) detectors for direct-sequence code-division multiple-access (DS-CDMA) systems in near-far environments. The approach taken is to make use of the available knowledge of spreading codes for all users (i.e., at the base-station) to cancel multiple access interference (MAI) using a combined adaptive MMOE-PIC algorithm. The BER of the proposed system is evaluated using the Gaussian approximation. Simulation results show that the Gaussian approximation yields a precise evaluation at BER levels that are of practical interest. Moreover, it is shown that for a 10 user system with a severe near-far scenario and binary phase shift keying (BPSK) transmission, a 4-stage adaptive MMOE-PIC receiver does not need a training period for convergence to be reached. Furthermore, the proposed receiver is shown to attain a steady-state BER performance close to the standard minimum mean-squared error (MMSE) receiver while the adaptive MMOE detector suffers from a higher BER due to the imperfect filter coefficients.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".