Problem
Research questions and friction points this paper is trying to address.
CV-QKD
LDPC decoding
fixed-point arithmetic
hardware implementation
decoder performance
Fixed Point Exploration For CV-QKD IR QC-MET-LDPC Toward Hardware Implementation
🤖 AI Summary
This study addresses the complexity–efficiency trade-off in high-speed LDPC decoding for continuous-variable quantum key distribution (CV-QKD). Within a unified low-SNR fixed-point framework, the authors systematically evaluate the performance of sum-product algorithm (SPA), min-sum algorithm (MSA), and normalized min-sum (NMS) decoding under identical quasi-cyclic multi-edge-type LDPC (QC-MET-LDPC) code structures and quantization schemes (e.g., Q8.4, Q16.8), analyzing both frame error rate (FER) and average iteration count. The work reveals a strong coupling between decoding rules and numerical precision, establishing performance boundaries for each algorithm at hardware-friendly quantization levels: SPA consistently achieves the best overall performance; under low-complexity constraints, Q16.8 represents the minimum precision enabling stable decoding, with NMS outperforming MSA; notably, SPA combined with Q8.4 offers an optimal balance of reliability and efficiency for large-scale hardware deployment.
📝 Abstract
High-speed LDPC decoding is a major bottleneck in CV-QKD and motivates hardware acceleration with fixed-point arithmetic. This work compares SPA, MSA, and NMS under a unified low-SNR fixed-point framework using common graph, matrix, and quantization settings. Multiple formats are evaluated through FER, and average iterations. The results show that performance depends strongly on the interaction between decoder rule and numerical precision. SPA achieved the best overall performance. For reduced-complexity decoders, Q16.8 was the lowest consistent precision, with NMS outperforming MSA. Practically, SPA with Q8.4 offered the best balance between reliability and hardware efficiency for large-scale implementations.
Innovation
Methods, ideas, or system contributions that make the work stand out.
fixed-point arithmetic
LDPC decoding
CV-QKD
hardware implementation
decoder optimization
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