This paper explores the use of soft decision trees [1] in basic reinforcement applications to examine the efficacy of using passive-expert like networks for optimal Q-Value learning on Artificial Neural Networks (ANN)...This paper explores the use of soft decision trees [1] in basic reinforcement applications to examine the efficacy of using passive-expert like networks for optimal Q-Value learning on Artificial Neural Networks (ANN). The soft decision tree networks were built using the PyTorch machine learning and the OpenAi’s Gym environment frameworks. The conducted research study aimed at assessing the performance of soft decision tree networks on Cartpole as provided in the OpenAi Gym software package. The baseline performance metric that the soft decision tree networks were compared against was a simple Deep Neural Network using several linear layers with ReLU and Softmax activation functions for the input and output layers, respectively. All networks were trained using the Backpropagation algorithm provided generically by PyTorch’sAutograd module.展开更多
As an effective error correction technology,the Low Density Parity Check Code(LDPC)has been researched and applied by many scholars.Meanwhile,LDPC codes have some prominent performances,which involves close to the Sha...As an effective error correction technology,the Low Density Parity Check Code(LDPC)has been researched and applied by many scholars.Meanwhile,LDPC codes have some prominent performances,which involves close to the Shannon limit,achieving a higher bit rate and a fast decoding.However,whether these excellent characteristics are suitable for the resource-constrained Wireless Sensor Network(WSN),it seems to be seldom concerned.In this article,we review the LDPC code’s structure brief.ly,and them classify and summarize the LDPC codes’construction and decoding algorithms,finally,analyze the applications of LDPC code for WSN.We believe that our contributions will be able to facilitate the application of LDPC code in WSN.展开更多
第3代GPS民用信号L1C的CNAV-2电文采用了LDPC编码以提高电文解调性能。由于传统接收机常用的硬判决译码不能充分利用信道信息,探讨了译码性能更优的软判决译码方法。首先仿真分析了适合L1C接收机的软判决译码算法及其性能和复杂度,表明...第3代GPS民用信号L1C的CNAV-2电文采用了LDPC编码以提高电文解调性能。由于传统接收机常用的硬判决译码不能充分利用信道信息,探讨了译码性能更优的软判决译码方法。首先仿真分析了适合L1C接收机的软判决译码算法及其性能和复杂度,表明软判决译码能够提供比硬判决高2~3 d B的编码增益。然后设计了完整的CNAV-2电文译码方案,包括帧同步、LDPC和BCH译码、解交织、CRC校验和相干合并策略及流程,并在软件接收机上得以实现。最后通过实际QZSS卫星信号和商用射频GNSS模拟器信号验证了设计的正确性。展开更多
文摘This paper explores the use of soft decision trees [1] in basic reinforcement applications to examine the efficacy of using passive-expert like networks for optimal Q-Value learning on Artificial Neural Networks (ANN). The soft decision tree networks were built using the PyTorch machine learning and the OpenAi’s Gym environment frameworks. The conducted research study aimed at assessing the performance of soft decision tree networks on Cartpole as provided in the OpenAi Gym software package. The baseline performance metric that the soft decision tree networks were compared against was a simple Deep Neural Network using several linear layers with ReLU and Softmax activation functions for the input and output layers, respectively. All networks were trained using the Backpropagation algorithm provided generically by PyTorch’sAutograd module.
基金This work is partially supported by the National Natural Science Foundation of China(No.61571004)the Shanghai Natural Science Foundation(No.17ZR1429100)+2 种基金the Science and Technology Innovation Program of Shanghai(No.115DZ1100400)Fujian Science and Technology Plan STS Program(2017T3009)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(No.YJKYYQ20170074).
文摘As an effective error correction technology,the Low Density Parity Check Code(LDPC)has been researched and applied by many scholars.Meanwhile,LDPC codes have some prominent performances,which involves close to the Shannon limit,achieving a higher bit rate and a fast decoding.However,whether these excellent characteristics are suitable for the resource-constrained Wireless Sensor Network(WSN),it seems to be seldom concerned.In this article,we review the LDPC code’s structure brief.ly,and them classify and summarize the LDPC codes’construction and decoding algorithms,finally,analyze the applications of LDPC code for WSN.We believe that our contributions will be able to facilitate the application of LDPC code in WSN.
文摘第3代GPS民用信号L1C的CNAV-2电文采用了LDPC编码以提高电文解调性能。由于传统接收机常用的硬判决译码不能充分利用信道信息,探讨了译码性能更优的软判决译码方法。首先仿真分析了适合L1C接收机的软判决译码算法及其性能和复杂度,表明软判决译码能够提供比硬判决高2~3 d B的编码增益。然后设计了完整的CNAV-2电文译码方案,包括帧同步、LDPC和BCH译码、解交织、CRC校验和相干合并策略及流程,并在软件接收机上得以实现。最后通过实际QZSS卫星信号和商用射频GNSS模拟器信号验证了设计的正确性。
