Recently,a generalized successive cancellation list(SCL)decoder implemented with shiftedpruning(SP)scheme,namely the SCL-SP-ωdecoder,is presented for polar codes,which is able to shift the pruning window at mostωtim...Recently,a generalized successive cancellation list(SCL)decoder implemented with shiftedpruning(SP)scheme,namely the SCL-SP-ωdecoder,is presented for polar codes,which is able to shift the pruning window at mostωtimes during each SCL re-decoding attempt to prevent the correct path from being eliminated.The candidate positions for applying the SP scheme are selected by a shifting metric based on the probability that the elimination occurs.However,the number of exponential/logarithm operations involved in the SCL-SP-ωdecoder grows linearly with the number of information bits and list size,which leads to high computational complexity.In this paper,we present a detailed analysis of the SCL-SP-ωdecoder in terms of the decoding performance and complexity,which unveils that the choice of the shifting metric is essential for improving the decoding performance and reducing the re-decoding attempts simultaneously.Then,we introduce a simplified metric derived from the path metric(PM)domain,and a custom-tailored deep learning(DL)network is further designed to enhance the efficiency of the proposed simplified metric.The proposed metrics are both free of transcendental functions and hence,are more hardware-friendly than the existing metrics.Simulation results show that the proposed DL-aided metric provides the best error correction performance as comparison with the state of the art.展开更多
针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一...针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一种新的译码算法——基于可靠性函数的路径分裂选择策略辅助串行抵消列表(Path Splitting Selecting Strategy Based on Reliability Function under the Successive Cancellation List,PSS-RF-SCL)译码算法。该算法在译码阶段,每个信息比特在进行路径分裂前,会计算所有路径的路径度量(Path Metric,PM)值。利用这些PM值,进一步计算该比特的可靠性函数值。算法将可靠性函数值低于其平均值(即阈值α)的信息比特视为需要进行路径分裂的比特,从而减少了多余的路径分裂次数。此外,算法计算每条路径的APM值,并将APM值高于正确译码路径的APM平均值(即阈值β)的路径视为不可靠路径,对不可靠路径进行剪枝,有效控制了译码列表总数。仿真结果表明,相较于传统的基于搜索集的路径分裂策略辅助的SCL译码算法,所提出的PSS-RF-SCL译码算法在保持相同译码性能的前提条件下,显著降低了译码复杂度。展开更多
多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption,MUPP-AEC)模型,该模型考...多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption,MUPP-AEC)模型,该模型考虑了无人机在加速、减速、匀速、转角等飞行条件下的能耗差异。针对MUPP-AEC的特点,提出目标空间聚类离散头脑风暴优化算法(discrete brain storm optimization algorithm in objective space,DBSO-OS)。该算法采用个体空间整数编码和带2-opt的分阶段贪婪法解码策略,并对扰动算子和个体更新算子进行了离散化定义。个体更新算子中采用了混合随机反转变换和部分匹配变换的生成策略。实验结果表明:DBSO-OS能有效地求解MUPP-AEC;所提离散头脑风暴算子在全局收敛能力、求解精度和稳定性等方面均优于传统头脑风暴算子;在中小规模测试算例和较大规模测试算例的测试中,DBSO-OS优于对比算法。展开更多
基金supported in part by the National Key Research and Development Program of China under Grant 2018YFB1802303in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LQ20F010010。
文摘Recently,a generalized successive cancellation list(SCL)decoder implemented with shiftedpruning(SP)scheme,namely the SCL-SP-ωdecoder,is presented for polar codes,which is able to shift the pruning window at mostωtimes during each SCL re-decoding attempt to prevent the correct path from being eliminated.The candidate positions for applying the SP scheme are selected by a shifting metric based on the probability that the elimination occurs.However,the number of exponential/logarithm operations involved in the SCL-SP-ωdecoder grows linearly with the number of information bits and list size,which leads to high computational complexity.In this paper,we present a detailed analysis of the SCL-SP-ωdecoder in terms of the decoding performance and complexity,which unveils that the choice of the shifting metric is essential for improving the decoding performance and reducing the re-decoding attempts simultaneously.Then,we introduce a simplified metric derived from the path metric(PM)domain,and a custom-tailored deep learning(DL)network is further designed to enhance the efficiency of the proposed simplified metric.The proposed metrics are both free of transcendental functions and hence,are more hardware-friendly than the existing metrics.Simulation results show that the proposed DL-aided metric provides the best error correction performance as comparison with the state of the art.
文摘针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一种新的译码算法——基于可靠性函数的路径分裂选择策略辅助串行抵消列表(Path Splitting Selecting Strategy Based on Reliability Function under the Successive Cancellation List,PSS-RF-SCL)译码算法。该算法在译码阶段,每个信息比特在进行路径分裂前,会计算所有路径的路径度量(Path Metric,PM)值。利用这些PM值,进一步计算该比特的可靠性函数值。算法将可靠性函数值低于其平均值(即阈值α)的信息比特视为需要进行路径分裂的比特,从而减少了多余的路径分裂次数。此外,算法计算每条路径的APM值,并将APM值高于正确译码路径的APM平均值(即阈值β)的路径视为不可靠路径,对不可靠路径进行剪枝,有效控制了译码列表总数。仿真结果表明,相较于传统的基于搜索集的路径分裂策略辅助的SCL译码算法,所提出的PSS-RF-SCL译码算法在保持相同译码性能的前提条件下,显著降低了译码复杂度。
文摘多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption,MUPP-AEC)模型,该模型考虑了无人机在加速、减速、匀速、转角等飞行条件下的能耗差异。针对MUPP-AEC的特点,提出目标空间聚类离散头脑风暴优化算法(discrete brain storm optimization algorithm in objective space,DBSO-OS)。该算法采用个体空间整数编码和带2-opt的分阶段贪婪法解码策略,并对扰动算子和个体更新算子进行了离散化定义。个体更新算子中采用了混合随机反转变换和部分匹配变换的生成策略。实验结果表明:DBSO-OS能有效地求解MUPP-AEC;所提离散头脑风暴算子在全局收敛能力、求解精度和稳定性等方面均优于传统头脑风暴算子;在中小规模测试算例和较大规模测试算例的测试中,DBSO-OS优于对比算法。
