Powerful LDPC Codes for Broadband Wireless Networks: High-performance Code Construction and High-speed Encoder/Decoder Design
Bibliographic record
Abstract
This paper discusses high-performance code construction and high-speed encoder and decoder designs for low density parity check (LDPC) codes. Thanks to their nice structures, LDPC codes constructed from shifted identity matrices have been emphasized. Several techniques in code constructions have been proposed to lower the bit error floor down to 10-10. Characterized by a parity check matrix in a triangular plus dual-diagonal form, these structured LDPC codes can be encoded in linear time using a layered encoding algorithm. To increase the throughput of decoders, a joint row-column decoding algorithm has been proposed and parallel decoding architectures have been used. The implementation results into field programmable gate array (FPGA) devices indicate that the encoder for these high-performance LDPC codes attains a throughput of up to 115 Gbits/sec and the decoder attains a throughput of up to 1 Gbits/sec. The proposed codes are suitable for high-speed and high-performance applications which demand relatively low error floor, including application in broadband wireless networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".