Hardware Implementation of Fixed-Point Decoder for Low-Density Lattice Codes
Bibliographic record
Abstract
In this paper, a fixed-point arithmetic low-density lattice code (LDLC) decoder is designed and implemented on an FPGA where Gaussian mixture messages that are exchanged during the iterative decoding process are approximated to a single Gaussian. Numerical simulations are performed to find the minimum number of bits required for the fixed-point decoder implementation to attain a frame-error-rate (FER) performance similar to a floating-point LDLC decoder implemented in C. This is found to be 12 integer bits, 8 fractional bits and a sign bit. Efficient methods are used to numerically approximate the required non-linear functions such as division and exponentiation. A two-node serial LDLC decoder implemented on an Arria 10 FPGA is presented as a hardware proof-of-concept that attains a throughput of 440 Ksymbols/sec at high signal-to-noise ratio (SNR). This throughput is obtained at clock frequency of 125MHz and for a block length of 1000. The throughput is further improved with a partially-parallel architecture that achieves a throughput of 5.75 Msymbols/sec at high SNR.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".