军事卫星通信由于需满足信息实时性、传输速率高、通信容量大以及符合星间链路信道的时变特性等要求,通常采用高编码增益、高吞吐量的信道编码方案。低密度奇偶校验(Low-Density Parity-Check,LDPC)码由于具备接近Shannon极限的优异纠...军事卫星通信由于需满足信息实时性、传输速率高、通信容量大以及符合星间链路信道的时变特性等要求,通常采用高编码增益、高吞吐量的信道编码方案。低密度奇偶校验(Low-Density Parity-Check,LDPC)码由于具备接近Shannon极限的优异纠错性能和可并行计算的特性成为卫星通信主导信道编码标准之一。目前卫星通信接收机的译码器模块设计仍存在诸如无法实时在线判断迭代停止、系统吞吐量受限、大量判决电路影响核心译码电路的低功耗和实时性等问题。考虑上述问题,以因子图模型为基础,针对空间数据系统咨询委员会(Consultative Committee for Space Data Systems,CCSDS)标准深空通信码型,将校验节点归一化满足概率进化图案与LDPC译码器状态紧密耦合,给出可实时在线判断迭代停止的最优停止准则,实现高性能、低复杂度的停止准则译码算法设计。当优先考虑高吞吐量时,误码率(Bit Error Rate,BER)性能退化0.13 dB,中低信噪比平均迭代次数(Average Number of Iteration,ANI)降低50%以上;当优先考虑纠错性能时,BER性能仅退化0.02 dB,同时大幅降低ANI。该译码算法为高效低复杂度LDPC译码器设计提供有效解决方案。展开更多
针对半双工译码转发中继信道,提出了一种可逼近三节点中继信道容量限的空间耦合RA码的设计方法。针对二进制删除信道,源节点分别向中继节点和目的节点发送空间耦合RA码,中继节点先正确恢复出源节点发送的空间耦合RA,然后再次编码产生额...针对半双工译码转发中继信道,提出了一种可逼近三节点中继信道容量限的空间耦合RA码的设计方法。针对二进制删除信道,源节点分别向中继节点和目的节点发送空间耦合RA码,中继节点先正确恢复出源节点发送的空间耦合RA,然后再次编码产生额外的校验比特并转发给目的节点;目的节点结合中继节点发送的额外校验比特和源节点发送的空间耦合RA码进行译码,正确恢复出源节点的信息。为了评估所设计的空间耦合RA码在三节点中继信道下的渐近性能,推导了密度进化算法用于计算阈值。阈值分析结果表明,所提出的空间耦合RA码能够同时逼近源到中继链路和源到目的链路的容量限。同时,基于半双工二进制删除中继信道,仿真了所设计的空间耦合RA码的误码性能,结果表明,其误码性能与所推导的密度进化算法计算的阈值结果一致,呈现出逼近于容量限的优异性能,且优于采用空间耦合低密度奇偶校验(Low Density Parity Check,LDPC)码的性能。展开更多
Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved s...Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.展开更多
This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to...This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).展开更多
低密度奇偶校验(Low-Density Parity-Check,LDPC)码是第五代移动通信技术(5th Generation Mobile Communication Technology,5G)系统采用的信道编码技术之一,用于业务信道高速数据传输,具有很强的抗干扰能力和纠错能力。5G-LDPC码编译...低密度奇偶校验(Low-Density Parity-Check,LDPC)码是第五代移动通信技术(5th Generation Mobile Communication Technology,5G)系统采用的信道编码技术之一,用于业务信道高速数据传输,具有很强的抗干扰能力和纠错能力。5G-LDPC码编译码在嵌入式平台的实现是一个值得关注的研究方向。CEVA-XC4500数字信号处理(Digital Signal Processing,DSP)芯片具有极低功耗、高密度计算、集成了超长指令字(Very Long Instruction Word,VLIW)和单指令多数据(Single Instruction Multiple Data,SIMD)矢量功能的特点。针对CEVA-XC4500 DSP矢量汇编指令和内联指令集的特点,提出一系列针对5G-LDPC码编码的代码优化方法,使其满足5G-LDPC码编码工程应用指标要求。仿真结果表明,优化后的5G-LDPC码编码在CEVA-XC4500 DSP内核上表现良好,中长块编码吞吐率超过100 Mb/s、核心矩阵吞吐率超过1 Gb/s,最大吞吐率达到250 Mb/s、最大核心矩阵吞吐率达到1.6 Gb/s。如果CEVA-XC4500 DSP芯片的最大数据位宽将来能进一步增大,吞吐率可以做得更好。该5G-LDPC码编码的代码优化方法为其他信道编码在类似嵌入式平台的实现提供了参考。展开更多
文摘军事卫星通信由于需满足信息实时性、传输速率高、通信容量大以及符合星间链路信道的时变特性等要求,通常采用高编码增益、高吞吐量的信道编码方案。低密度奇偶校验(Low-Density Parity-Check,LDPC)码由于具备接近Shannon极限的优异纠错性能和可并行计算的特性成为卫星通信主导信道编码标准之一。目前卫星通信接收机的译码器模块设计仍存在诸如无法实时在线判断迭代停止、系统吞吐量受限、大量判决电路影响核心译码电路的低功耗和实时性等问题。考虑上述问题,以因子图模型为基础,针对空间数据系统咨询委员会(Consultative Committee for Space Data Systems,CCSDS)标准深空通信码型,将校验节点归一化满足概率进化图案与LDPC译码器状态紧密耦合,给出可实时在线判断迭代停止的最优停止准则,实现高性能、低复杂度的停止准则译码算法设计。当优先考虑高吞吐量时,误码率(Bit Error Rate,BER)性能退化0.13 dB,中低信噪比平均迭代次数(Average Number of Iteration,ANI)降低50%以上;当优先考虑纠错性能时,BER性能仅退化0.02 dB,同时大幅降低ANI。该译码算法为高效低复杂度LDPC译码器设计提供有效解决方案。
文摘针对半双工译码转发中继信道,提出了一种可逼近三节点中继信道容量限的空间耦合RA码的设计方法。针对二进制删除信道,源节点分别向中继节点和目的节点发送空间耦合RA码,中继节点先正确恢复出源节点发送的空间耦合RA,然后再次编码产生额外的校验比特并转发给目的节点;目的节点结合中继节点发送的额外校验比特和源节点发送的空间耦合RA码进行译码,正确恢复出源节点的信息。为了评估所设计的空间耦合RA码在三节点中继信道下的渐近性能,推导了密度进化算法用于计算阈值。阈值分析结果表明,所提出的空间耦合RA码能够同时逼近源到中继链路和源到目的链路的容量限。同时,基于半双工二进制删除中继信道,仿真了所设计的空间耦合RA码的误码性能,结果表明,其误码性能与所推导的密度进化算法计算的阈值结果一致,呈现出逼近于容量限的优异性能,且优于采用空间耦合低密度奇偶校验(Low Density Parity Check,LDPC)码的性能。
基金funded by the Key Project of NSFC-Guangdong Province Joint Program(Grant No.U2001204)the National Natural Science Foundation of China(Grant Nos.61873290 and 61972431)+1 种基金the Science and Technology Program of Guangzhou,China(Grant No.202002030470)the Funding Project of Featured Major of Guangzhou Xinhua University(2021TZ002).
文摘Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.
基金supported by Beijing Natural Science Foundation (L202003)。
文摘This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).
