On the performance of polar codes for 5G eMBB control channel
Bibliographic record
Abstract
Polar codes are a class of error-correcting codes which can provably achieve the capacity of a binary memoryless symmetric channel with low-complexity encoding and decoding algorithms. They have been selected for use in the next generation of wireless communications, as a coding scheme for the enhanced mobile broadband (eMBB) control channel, which requires codes with short lengths and low rates. Successive-cancellation (SC), SC list (SCL), and their modifications, are some of the most studied polar code decoding algorithms. In this paper, we study polar codes of short lengths and different code rates. We show that for a fixed target frame error rate (FER), there is an optimal code rate with which SC and SCL decoders can achieve it with maximum power efficiency. In addition, we study the effect of CRC on the error-correction performance of SCL decoders and show that there is an optimal CRC length with which the decoder achieves its best results. We further analyze the speed of polar code decoding by considering state-of-the-art fast SCL decoders available in literature, thus providing a survey of the decoder design space for eMBB, considering error-correction performance, achievable throughput, flexibility and estimated complexity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".