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作 者:刘芳[1] 王亚娟 赖峥嵘 刘元安[1] LIU Fang;WANG Yajuan;LAI Zhengrong;LIU Yuan’an(School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China;Research Institute of China Mobile Communications Co.,Ltd.,Beijing 100032,China;Guangdong Communications and Networks Institute,Guangzhou 510760,China)
机构地区:[1]北京邮电大学电子工程学院,北京100876 [2]中国移动通信有限公司研究院,北京100053 [3]广东省新一代通信与网络创新研究院,广东广州510760
出 处:《电信科学》2020年第10期79-86,共8页Telecommunications Science
基 金:广东省重点领域研发计划项目(No.2019B010157001);国家自然科学基金资助项目(No.61821001)。
摘 要:全双工技术理论上可以使频谱效率提升一倍,将其应用于双向中继系统,能进一步提升系统的频谱效率。考虑残余自干扰与信道环境,以安全容量最大化为目标进行中继选择,将该选择优化问题建模为多分类问题,提出了一种基于卷积神经网络(CNN)的智能中继选择策略。在设计分类模型时利用CNN提取信道的空间相关性,设置卷积核的维度与中继数目相关,为了保留输入特征的矩阵特性未使用池化层。仿真结果表明,在降低计算复杂度和减少反馈开销的情况下,基于CNN的分类器具有更高的分类准确率,能获得与传统最优中继选择方案一致的安全容量。Full-duplex can double the spectrum efficiency theoretically.Thus it can further improve the spectrum efficiency when it is used in the relay systems.Considering the residual self-interference and signal-to-noise ratio,a problem was set to maximize the security capacity by selecting the optimal relay.This optimization problem was transformed into multi-classification problem.Thus a convolutional neural network(CNN)-based intelligent relay selection scheme was proposed.In the design of the classification model,the CNN was used to extract the spatial correlation of the channel,and the dimension of the convolution kernel was related to the number of relays.The pooling layer was not used to preserve the matrix characteristics of the input features.The simulation results show that the proposed CNN-based intelligent selection classification model has high classification accuracy,and can obtain the same security performance as the traditional exhaustive search scheme,and the real-time performance is significantly improved.
分 类 号:TP393[自动化与计算机技术—计算机应用技术]
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