Multiple Antennas and Multipass Structure for Adaptive Duplicated Filters and Interference Canceller in WCDMA Systems
Bibliographic record
Abstract
An adaptive multistage multiuser detection (MUD) method namely adaptive duplicated filters and interference canceller (ADIC), has been proposed in a uplink WCDMA context. It was shown that ADIC approach provides, in a single receive antenna scenario, the same performance as the decision feedback soft multistage parallel interference canceller (DF-soft-MPIC) while affording a complexity reduction by a factor of 4. This paper proposes and analyses new versions of ADIC: the first efficiently takes into account the diversity offered by in a multiple receiving antennas context and the second aims at improving the performance based on multipass channel estimation and joint ADIC MUD, referred herein as MADIC. With respect to performance and implementation complexity trade-offs, MADIC presents a good avenue for WCDMA base station compared to DF-soft-MPIC.
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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 | 0.000 | 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 | 0.000 |
| 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".