Joint Iterative Demodulation and Decoding of Differential Frequency Hopping Signals
Bibliographic record
Abstract
In this paper, joint iterative demodulation and decoding of differential frequency hopping (DFH) signals based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is considered. The DFH is a nonlinear modulation with memory in the frequency sequence of the successive transmitted symbols, which can be represented by a trellis diagram. The proposed receiving scheme is primarily composed of an a posteriori probability (APP) decoder/filter and an APP demodulator, by which the extrinsic information on the coded bits extracted from the output of the APP filter can be utilized again by the demodulator as updated a priori information. Performance over AWGN and time-varying Rayleigh flat fading channels is investigated by Monte Carlo simulation. By comparison between the symbol-by-symbol maximum a posteriori probability (SBS-MAP) detection with soft decision Viterbi decoding and the proposed method, it can be shown that considerable improvement can be achieved for both static and time-varying fading channels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".