Stochastic Computing based BCH Decoder for WBAN Systems
Bibliographic record
Abstract
BCH codes have been accepted as the error correction coding scheme by the IEEE 802.15.6 standard for wireless body area networks (WBAN). Soft-decision BCH decoders are attractive for the reason that the decoding gains result in significant transmitting energy reduction. An approximate computing technology called stochastic computing is a promising low power and low hardware cost implementation candidate for BCH soft decision decoders. In this paper, a stochastic computing based soft decision decoder is presented for the BCH code defined in the IEEE 802.15.6 standard. According to the evaluation results, the proposed design has the advantages of energy consumption and hardware cost while approaching the decoding performance in terms of block error rate (BLER) compared to existing BCH soft decision decoders. In addition, the proposed design requires no noise power estimation for the soft decision demodulation module, which could further reduce the hardware cost and energy consumption of the WBAN receiver.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".