利用光卫星通信自适应稀疏量化核最小均方算法设计和分析随机 5G 新无线电 LDPC 解码器

R. Krishna Priya, N. Sakhare, Ajay Paithane, R. Shekhar, M. Sabarimuthu
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引用次数: 0

摘要

随机低密度奇偶校验(LDPC)解码器是一种采用随机技术进行解码的 5G 新无线电标准 LDPC 解码器。采用 5G 新无线电标准的随机 LDPC 解码通常使用迭代过程,即在可变节点(VN)和校验节点之间多次交换信息。随机 LDPC 解码器通常用于接收信号受不同程度噪声影响的场景。与传统 LDPC 解码器相比,它们能提供更好的纠错性能,尤其是在处理 5G 网络中信噪比不同的信道时。本文利用自适应稀疏量化核最小均方算法(SLDPC-ASQ-KLMSA),提出了一种面积效率高的随机 LDPC 解码器架构设计。本研究采用的 LDPC 码(2048,1723)来自 LOGBASE-T 标准。我们研究了 ASQ-KLMSA 连接效果。从 VN 开始。它通过将外部信息长度限制为 2 比特,使检查节点的功能更容易实现,并降低了相互连接的复杂性。由于 ASQ-KLMSA 简化了检查节点操作,解码器节点必须以更高的精确度交换信息。在(2048,1723)码中,6 级 VN 的 3-3 输入分组子节点被改为基于加法器的 5-1 输入分组子节点,以便在校验到变量信息不够强时获得更精确的结果。利用为台积电 65 纳米工艺(2048,1723)开发的随机 LDPC 解码器,确定了建议的解码器架构。咬合误差率、吞吐量、均方误差、延迟、功耗和面积使用是用来评估 SLDPC-ASQ-KLMSA 算法有效性的一些指标,该算法已被提出并用 Python 实现。因此,与通过相关分析设计的高性能随机 LDPC 解码器架构(HP-SLDPC-CA)、利用比特串行随机更新的更高吞吐量和硬件效率混合 LDPC 解码器(HLDPC-BSSU)、基于 FPGA 的 5G LDPC 码灵活随机解码器(FPGA-SD-5G-LDPC)等现有方法相比,所提出的方法分别实现了 34.44% 和 38.39% 的低均方误差。
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Design and analysis of stochastic 5G new radio LDPC decoder using adaptive sparse quantization kernel least mean square algorithm for optical satellite communications
A Stochastic Low‐Density Parity‐Check (LDPC) decoder is a type of 5G New Radio standard LDPC decoder that uses stochastic techniques to perform decoding. Stochastic LDPC decoding with 5G NR standard typically uses an iterative process, where messages exchanged among variable nodes (VN), check nodes multiple times. Stochastic LDPC decoders are often used in scenarios where the received signal is subject to varying levels of noise. They will provide improved error correction performance compared to traditional LDPC decoders, especially when dealing with channels with varying signal‐to‐noise ratios in 5G networks. Using the adaptive sparse quantization kernel least mean square algorithm (SLDPC‐ASQ‐KLMSA), this paper proposes an area‐efficient architecture design for a stochastic LDPC decoder. The LDPC code (2048, 1723) is taken from the LOGBASE‐T standard and used in this study. We examine the ASQ‐KLMSA connection effects. Starting with the VN. It makes checking node functioning easier and reduces inter‐connect complexity by capping extrinsic message length at 2 bits. Because of the simplified check node operation in ASQ‐KLMSA, the decoder nodes must exchange messages with a greater degree of accuracy. The 3–3 input grouping sub‐node of the degree‐6 VN was changed with an adder‐based 5–1 input grouping sub‐node for the (2048, 1723) code in order to get more accurate results when the check‐to‐variable messages aren't strong enough. A suggested decoder architecture was determined using a stochastic LDPC decoder developed for TSMC 65 nm process (2048, 1723). Bite error rate, throughput, mean square error, latency, power, and area usage are some of the metrics used to evaluate the effectiveness of the SLDPC‐ASQ‐KLMSA algorithm that has been suggested and implemented in Python. Thus, the proposed approach has attained 34.44%, and 38.39% low mean square error while compared with the existing methods such as higher‐performance stochastic LDPC decoder architecture designed through correlation analysis (HP‐SLDPC‐CA), Higher Throughput and Hardware Efficient Hybrid LDPC Decoder Utilizing Bit‐Serial Stochastic Updating(HLDPC‐BSSU), Flexible FPGA‐Based Stochastic Decoder for 5G LDPC codes (FPGA‐SD‐5G‐LDPC), respectively.
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