Adaptive Duplicated Filters and Interference Canceller for DS-CDMA Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A low complexity multiuser detection (MUD) technique, the Adaptive Duplicated Filters and Interference Canceller (ADIC) (patent pending), is proposed in the DS-CDMA context. Of particular interest is the use of adaptive filters block (AFB) dedicated to each user with its respective input signals independent from other users’ contributions. The AFBs are mixed with interference canceller block in a cascade arrangement. As shown in this paper, this MUD can outperform the Decision Feedback Soft MultiStage Interference Canceller (DF-Soft-MPIC) MUD with complexity reduction by a factor of 4 to 8 for the data payload throughput from 64 kbps to 384 kbps, respectively. In addition to performance and algorithmic description of the proposed MUD method, a VLSI implementation strategy and hardware resources evaluation are investigated; permitting to estimate the maximum number of users in FPGA devices with respect to WCDMA constraints. The present work proposes a low complexity MUD wherein an interesting trade-off between performance and implementation complexity is described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it