A Fast Adaptive Algorithm for MMSE Receivers in 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
In this letter, we consider the application of an iterative interference cancelation (IC) scheme to improve the speed of convergence of the adaptive minimum mean-squared error (MMSE) receiver for the reverse-link of a direct-sequence code-division multiple-access (DS-CDMA) system. Our aim is to reduce the overhead introduced during the receiver's training period. This will be achieved using an iterative interference cancelation algorithm such as the parallel interference cancelation (PIC) algorithm. The proposed iterative algorithm makes use of the available knowledge of all users' training sequences at the base-station receiver to jointly cancel multiple-access interference (MAI) and adapts to the MMSE optimum filter taps using the combined adaptive MMSE/PIC receiver. We employ the proposed iterative algorithm to both the least mean square and the recursive least squares algorithms where we show that a significant improvement in terms of convergence speed is achieved. Moreover, we demonstrate the near-far resistance of the proposed receiver.
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 | 0.001 |
| 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 it