AI in EE

AI IN DIVISIONS

AI in Circuit Division

Multiple-Resolution Decoding Architecture for QC-LDPC Codes (박인철 교수 연구실)

Abstract

This paper proposes a hardware-efficient multiple-resolution decoding architecture for quasi-cyclic low-density parity-check (QC-LDPC) codes, specifically designed to meet the stringent requirements of the 5G New Radio (NR) standard. The proposed architecture adopts a single-instruction multiple-data (SIMD) approach to dynamically adjust the bitwidth of log-likelihood ratio (LLR) values based on the Eb/N0 condition, significantly reducing hardware complexity and improving throughput area ratio. Unlike conventional single-resolution decoders, the architecture processes 2-bit LLR values in high Eb/N0 regions and scales up to 4-bit or 8-bit LLR values for moderate and low Eb/N0 conditions, maintaining robust error-correcting performance. Key innovations include SIMD-based design for variable-node units (VNUs), check-node units (CNUs), and quasi-cyclic shifting networks (QSNs), as well as optimized memory access scheduling to support all 51 lifting sizes defined in the 5G NR standard. Designed in a 65-nm CMOS process, the decoder achieves a peak throughput of 27.24 Gbps under error-free conditions with a throughput area ratio improvement of 2.07× compared to the state-of-the-art designs. Furthermore, the proposed architecture demonstrates superior throughput-area ratio and flexibility, supporting all code rates and lifting sizes specified in the 5G NR standard. Simulation results confirm that the proposed decoder meets the peak throughput requirement under error-free conditions, while maintaining robust performance in challenging channel environments.

 

Main Figure

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