Turbo Coding for Satellite and Wireless Communications
Bibliographic record
Abstract
6. 5 137 7 Performance of BTCs and 139 their Applications 7. 1 Introduction 139 7. 2 Some Results from the Literatures 139 7. 3 Applications of Block Turbo Codes. 142 7. 3. 1 Broadband Wireless Access Standard 144 7. 3. 2 Advanced Hardware Architectures (AHA) 145 7. 3. 3 COMTECH EF DATA 147 7. 3. 4 Turbo Concept 149 7. 3. 5 Paradise Data Com 150 Summary 7. 4 151 8 Implementation Issues 153 8. 1 Fixed-point Implementation of Turbo Decoder 153 8. 1. 1 Input Data Quantization for DVB-RCS Turbo Codes 155 8. 1. 2 Input Data Quantization for BTC 157 8. 2 The Effect of Correction Term in Max-Log-MAP Algorithm 159 8. 3 Effect of Channel Impairment on Turbo Codes 163 8. 3. 1 System Model for the Investigation of Channel Impairments 163 8. 3. 2 Channel SNR Mismatch 164 8. 3. 2. 1 Simulation Results 165 8. 3. 3 Carrier Phase Recovery 170 8. 3. 3. 1 The Effect of Phase Offset on the Performance of RM Turbo Codes 170 8. 3. 3. 2 The Effect of Preamble Size on the Performance of RM Turbo Codes 170 8. 3. 3. 3 Simulation Results 170 8. 4 Hardware Implementation of Turbo Codes 171 8. 5 Summary 175 9 177 Low Density Parity Check Codes 9. 1 Gallager Codes: Regular Binary LDPC Codes 177 9. 2 Random Block Codes 178 9. 2. 1 Generator Matrix 179 9. 2
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".