In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utiliz...In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.展开更多
目的将神经网络模型与传统中药药性理论(四性、五味、归经)相结合来预测分析中药肾毒性,方法通过文献检索具有肾毒性证据的中药,并将《中华本草(精选本)》中去除上述肾毒性中药的其他中药作为非肾毒性中药纳入数据。以《中华本草》为标...目的将神经网络模型与传统中药药性理论(四性、五味、归经)相结合来预测分析中药肾毒性,方法通过文献检索具有肾毒性证据的中药,并将《中华本草(精选本)》中去除上述肾毒性中药的其他中药作为非肾毒性中药纳入数据。以《中华本草》为标准,确定每味中药的四性、五味和归经归属,分别进行肾毒性/非肾毒性中药与其四性、五味、归经因素的相关性检验,筛选出相关性变量因素,用于构建神经网络模型(Neural Networks Model,NNM)。同时,绘制模型的"受试者工作特征曲线"(Receiver Operator Characteristic Curve,ROC曲线),并计算曲线下面积(Area Under the Curve,AUC),用于评估模型的预测能力。结果肾毒性/非肾毒性中药与四性、五味归属具有相关性(P<0.05),与归经归属无相关性(P>0.05)。NNM结果显示,热性、辛味、温性和苦味是影响中药肾毒性的前4位重要因素,热性排在重要性第1位,模型ROC曲线的AUC计算结果为0.739。结论将传统中药理论与现代数理统计方法相结合建立的中药肾毒性神经网络模型具有一定的预测性,该建模方法可为中药肾毒性及中药毒理学研究提供一定的参考。展开更多
基金supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61925102in part by the National Natural Science Foundation of China(62201087&92167202&62101069&62201086)in part by the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.
文摘目的将神经网络模型与传统中药药性理论(四性、五味、归经)相结合来预测分析中药肾毒性,方法通过文献检索具有肾毒性证据的中药,并将《中华本草(精选本)》中去除上述肾毒性中药的其他中药作为非肾毒性中药纳入数据。以《中华本草》为标准,确定每味中药的四性、五味和归经归属,分别进行肾毒性/非肾毒性中药与其四性、五味、归经因素的相关性检验,筛选出相关性变量因素,用于构建神经网络模型(Neural Networks Model,NNM)。同时,绘制模型的"受试者工作特征曲线"(Receiver Operator Characteristic Curve,ROC曲线),并计算曲线下面积(Area Under the Curve,AUC),用于评估模型的预测能力。结果肾毒性/非肾毒性中药与四性、五味归属具有相关性(P<0.05),与归经归属无相关性(P>0.05)。NNM结果显示,热性、辛味、温性和苦味是影响中药肾毒性的前4位重要因素,热性排在重要性第1位,模型ROC曲线的AUC计算结果为0.739。结论将传统中药理论与现代数理统计方法相结合建立的中药肾毒性神经网络模型具有一定的预测性,该建模方法可为中药肾毒性及中药毒理学研究提供一定的参考。
