Multiuser interference cancellation aided adaptation of a MMSE receiver for direct-sequence code-division multiple-access systems
Bibliographic record
Abstract
We consider the application of iterative interference cancellation (IC) schemes to improve the convergence speed of the linear adaptive minimum mean-squared error (MMSE) receiver in a direct-sequence code-division multiple-access (DS-CDMA) system. Our aim is to reduce the overhead introduced during the training period. This will be achieved using an iterative interference cancellation algorithm such as the parallel interference cancellation (PIC) algorithm. The proposed system makes use of the available knowledge of the training sequences for all users (i.e., at the base station) to jointly cancel the multiple access interference (MAI) and adapts to the MMSE optimum filter taps using the combined adaptive MMSE/PIC. An examination of the adaptive MMSE/PIC receiver reveals that it is near-far resistant. Moreover, it is shown that using the least-mean-square (LMS) as a simple form of the adaptive MMSE receiver, the proposed algorithm requires only a few tens of symbols for convergence as compared to a few hundred training symbols needed for the conventional adaptive LMS receiver. Also employing a more complex, yet faster, a recursive-least squares algorithm (RLS) convergence is reached with few training symbols when PIC is used.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".