论文标题
基于LSTM的分布式有条件生成的对抗网络，用于数据驱动的5G海事无人机通信
LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications
论文作者
论文摘要
5G启用海上无人机（UAV）通信是5G无线网络的重要应用之一，它需要最小的延迟和更高的可靠性才能支持关键任务应用程序。因此，与高数据速率的无损可靠通信是现代无线通信系统中的关键要求。这些因素在很大程度上取决于通道条件。在这项工作中，提出了一个频道模型，用于利用毫米波（MMWave）的空气向下链路，用于5G启用的海上无人机（UAV）通信。首先，我们将提出公式的通道估计方法，该方法直接旨在从无人机在长期短期内存（LSTM）分布的条件生成对逆网络（DCGAN）即（LSTM-DCGAN）中采用的无人机（LSTM-DCGAN）在每个光束方向上采用MMWAVE的通道状态信息（CSI）。其次，为了增强空间域训练训练的通道模型的应用，我们设计了一个基于LSTM-DCGAN的无人机网络，每个网络都会为所有分布学习MMWave CSI。最后，我们对最有利的LSTM-DCGAN培训方法进行了分类，并为我们的无人机网络颁布了某些条件以提高渠道模型学习率。仿真结果表明，所提出的基于LSTM-DCGAN的网络对通过本地培训产生的错误有力。已经与其他可用的最先进的CGAN网络体系结构（即独立的CGAN）（无CSI共享），简单的CGAN（具有CSI共享），多歧视者CGAN，Federated Learning Cgan和DCGAN进行了详细的比较。仿真结果表明，与先前的艺术品相比，提出的LSTM-DCGAN结构在学习过程中表现出更高的精度，并获得了更高的下行链路传输数据速率。
5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.
