Iterative Signal Processing for Blind Code Phase Acquisition of CDMA 1x Signals for Radio Spectrum Monitoring
Bibliographic record
Abstract
This paper addresses the problem of recovering the code phase of the composite spreading sequence for a CDMA 1x signal transmitted from a handset, without the benefit of a priori information from the system. The spreading code is required for the radio spectrum monitoring system for signal detection and measurements rather than for communications. The structure of the CDMA 1x signal is exploited by processing sequential pairs of received samples to form a single soft sample for each pair. The approach models the combination of the long‐code generator and the two short‐code generators, along with the pair‐wise processing, by a single linear system over GF(2), with the initial states of the long‐ and short‐code generators forming the input vector. Consequently, a vector of the pair‐wise soft samples can be treated as a noisy received codeword that is decoded using iterative soft‐in decoding techniques. If the decoder yields the correct candidate “codeword,” the original states of the code generators can be computed. This approach does not require direct access to the transmitted spreading sequence but can be applied to the data modulated signal. Simulation results provide performance estimates of the method with noise, Rayleigh fading, and co‐channel interference.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".