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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".