Design of Generalized Concatenated Codes Based on Polar Codes With Very Short Outer Codes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a new design approach to construct generalized concatenated codes (GCCs) based on polar codes (PCs). It is already known that PCs can be considered as a special class of the GCCs with polar outer and inner codes. We show how density evolution (DE) can be used to develop a channel-specific method to design outer codes of length L under maximum-likelihood (ML) decoding. Once a bank of outer codes of length L and different rates have been designed, we develop a rate-allocation algorithm to allocate rates to the outer codes of the GCC with the goal of minimizing the overall block error rate for a given overall rate of K/N. To maintain the low complexity of the SC decoder, we only design very short outer codes (L ≤ 8). We show that, at this outer code length, it is possible to design GCC-PCs that can result in up to 0.5-dB coding gain while reducing decoding complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it