Chip-locked space-time filtering for maximizing SINR in asynchronous DS-CDMA systems
Bibliographic record
Abstract
This paper considers a chip-locked space-time (CLST) filtering technique for direct-sequence code-division multiple access (DS-CDMA) systems. CLST filtering exploits the knowledge of the multiple access interference (MAI) chip delays, as well as the fact that the MAI spectrum is colored in the time and space domains. Chip delays of interferers from the same cell as the desired user are available at the base station and can be used to improve performance of single-user receivers. CLST filtering reduces MAI through joint optimization of spatio-temporal filtering and exploitation of chip delays of locked interference, without the need of interferers spreading codes. A significant improvement in signal-to-interference plus noise ratio can be achieved through CLST filtering with respect to the case when the interference is unlocked. The capabilities of CLST filtering to suppress chip-delay-locked interference improves with increasing chip waveform excess bandwidth. Numerical results show that CLST filtering already provides significant performance gains with square-root raised cosine chip pulses of small excess bandwidth. Furthermore, it is also shown that CLST filtering for a long observation interval is suitable for DS-CDMA systems employing long sequence spreading.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".