Highly reliable data transmission using concatenated coding and hybrid-type I ARQ in DS-CDMA systems
Bibliographic record
Abstract
Highly reliable data transmission using Reed-Solomon(RS)/convolutional concatenated coding in conjunction with hybrid type-I automatic retransmission request (ARQ) is investigated. The study is based on an analysis of the tradeoff between error correction and error detection capabilities of RS codes for the direct-sequence code-division multiple-access (DS-CDMA) systems. Two RS decoding schemes are considered: error-only decoding (decoding without side information) and error-and-erasure (EE) decoding. In the EE scheme, the inner code is decoded with a modified Viterbi algorithm, which produces reliable information along with the decoded output. Numerical results illustrate the importance of properly selecting the design parameters for the purpose of maximizing spectral efficiency. The results also show that an E/sub b//N/sub 0/ gain of 0.1 to 0.5 dB can be obtained by using EE decoding, depending on the selected parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".