On Error-Correction Performance and Implementation of Polar Code List\n Decoders for 5G
Bibliographic record
Abstract
Polar codes are a class of capacity achieving error correcting codes that has\nbeen recently selected for the next generation of wireless communication\nstandards (5G). Polar code decoding algorithms have evolved in various\ndirections, striking different balances between error-correction performance,\nspeed and complexity. Successive-cancellation list (SCL) and its incarnations\nconstitute a powerful, well-studied set of algorithms, in constant improvement.\nAt the same time, different implementation approaches provide a wide range of\narea occupations and latency results. 5G puts a focus on improved\nerror-correction performance, high throughput and low power consumption: a\ncomprehensive study considering all these metrics is currently lacking in\nliterature. In this work, we evaluate SCL-based decoding algorithms in terms of\nerror-correction performance and compare them to low-density parity-check\n(LDPC) codes. Moreover, we consider various decoder implementations, for both\npolar and LDPC codes, and compare their area occupation and power and energy\nconsumption when targeting short code lengths and rates. Our work shows that\namong SCL-based decoders, the partitioned SCL (PSCL) provides the lowest area\noccupation and power consumption, whereas fast simplified SCL (Fast-SSCL)\nyields the lowest energy consumption. Compared to LDPC decoder architectures,\ndifferent SCL implementations occupy up to 17.1x less area, dissipate up to\n7.35x less power, and up to 26x less energy.\n
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".