A segmented matched filter for CDMA code synchronization in systems with Doppler frequency offset
Bibliographic record
Abstract
This paper presents a segmented matched filter (SMF) for codephase acquisition in direct sequence spread spectrum systems. While conventional matched filters provide fast acquisition in the presence of high co-user noise, they are unable to handle significant carrier frequency offset (Doppler). This problem is alleviated by segmentation with non-coherent summation. The paper develops expressions to approximately relate the matched filter partitioning to the pre-detection filter and dwell time integrator of the conventional non-coherent correlator. It also investigates 1-bit versus 2-bit quantization. A mixed-signal application specific integrated circuit (ASIC) has been fabricated to implement a 512 chip SMF with half chip codephase resolution. The paper presents calculated and measured probability density functions (pdf) for the filter output decision variable for 10, 25, and 50 co-users with 0 to 20 kHz Doppler shift. For the example of a GPS receiver, expected acquisition time is shown as a function of multiple access interference and carrier Doppler shift.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".