Measured flat fading performance of Doppler corrected Nyquist filtered 4800 bps DQPSK modem
Bibliographic record
Abstract
We consider the measured performance of three Doppler correction algorithms with asynchronous timing recovery for the differential detection of filtered DQPSK transmitted over a flat fading channel. The three algorithms all involve a combination of open and closed loop components. Their relative performance is found to be similar and, in addition, all have similar complexity when implemented in digital-signal processor (DSP) firmware. Some algorithms have burst communication application but herein we only do real time tests for serial communication links. One fading simulator is based on DSP firmware and realizes the Rician fading model with time variation based on the vehicle Doppler frequency. We also present results that include a commercial channel simulator that operates at an intermediate frequency of 70 MHz. Our experiments are all conducted at a transmission rate of 4800 bps and the results are expected to be applicable to DSP based, mobile satellite communications. Implementation losses are measured relative to a digital computer simulation of the communication process. Losses are found to be a small fraction of a dB, even in severe Doppler variation environment (i.e., a Doppler rate of 20 Hz/s).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".