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作 者:雷泰雅 毛开 郑永丰[2] 宋茂忠[1] 朱秋明[1] LEI Taiya;MAO Kai;ZHENG Yongfeng;SONG Maozhong;ZHY Qiuming(Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space,Ministry of Industry and Information Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;Beijing Aerospace Measurement and Control Technology Co.,Ltd.,Beijing 100041,China)
机构地区:[1]南京航空航天大学电磁频谱空间认知动态系统工业和信息化部重点实验室,江苏南京211106 [2]北京航天测控技术有限公司,北京100041
出 处:《移动通信》2022年第12期69-74,共6页Mobile Communications
基 金:国家自然科学基金面上项目(62271250)。
摘 要:毫米波通信因其可以提供更高的传输速率和系统容量受到了广泛关注。无人机空地通信技术是未来通信网络的重要组成部分,路径损耗预测对于无人机的节点布局、轨迹优化和功率分配具有重要意义。针对无人机通信多场景PL预测问题,结合参数化的几何场景和多输入反向传播神经网络,提出了一种具有跨场景能力的PL迁移预测模型。该模型利用有限场景下信道数据进行网络训练,可以预测未知新场景下的PL。最后,利用射线跟踪仿真数据进行模型有效性验证,仿真结果表明,所提模型的神经网络训练收敛效果较好,在新场景下预测PL结果与RT仿真结果基本吻合,验证了该模型的跨场景迁移预测能力。Millimeter wave communication has gained extensive attention due to its capability of higher transmission rate and system capacity. UAV air-to-ground communication technology is an important part in future communication network, where path loss(PL) prediction is of great significance for UAV placement, trajectory optimization, and power allocation. Aiming at the multi-scenario PL prediction problem of UAV communication, a path loss migration prediction model with cross-scenario capability is proposed by combining parameterized geometric scenarios and multi-input backpropagation neural network. The proposed model utilizes the channel data in limited scenarios for network training and can predict the PL in unknown new scenarios. Finally, the effectiveness of the proposed model is validated by ray tracing simulation data. The simulation results show that the neural network of the proposed model is well trained, and the PL prediction results are well consistent with those of RT simulation in a new scenario, which verifies the cross-scenario prediction ability.
关 键 词:毫米波 无人机通信 路径损耗 反向传播神经网络 射线跟踪 跨场景参数迁移
分 类 号:TN928[电子电信—通信与信息系统]
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