Bibliographic record
Abstract
Direct sequence code division multiple access (DS-CDMA) is a core technology for wireless access that is proposed for future generation (3G) mobile and personal communication systems. The use of a base-station antenna array is strongly endorsed in these systems, because of its capabilities of filtering out the interference in the space domain. This paper considers space-time one shot single user receivers for DS-CDMA, based on a multiple input single output time invariant linear filter. The optimization of the filter response is performed jointly in the space and time domains yielding maximal signal-to-interference-plus-noise ratio. Using this framework, a space-time noise whitening matched filter (ST-NWMF) for DS-CDMA demodulation is introduced. It is shown that combined spatio-temporal filtering, taking advantage of the fact that the multiple access interference is colored in the space as well as time domains, can reduce the effects of strong interference components and improves the performance. Simpler suboptimal space-time linear receivers, consisting of a single temporal LT filter following the antenna array, are also considered. It is shown that such a filter approaches the ST-NWMF in the limit, when the M chip waveform has a flat spectrum with no excess bandwidth and N the observation interval is infinite. These schemes, optimal as well as suboptimal, for large observation intervals are suitable also 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".