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Deep learning-based code indexed modulation for autonomous underwater vehicles systems
Vehicular Communications ( IF 5.8 ) Pub Date : 2020-11-13 , DOI: 10.1016/j.vehcom.2020.100314
Zeyad A.H. Qasem , Hussein A. Leftah , Haixin Sun , Jie Qi , Junfeng Wang , Hamada Esmaiel

The Multiuser Direct Sequence Spread Spectrum (DSSS) has been proposed for the autonomous underwater vehicles (AUV) communication systems in a long transmission distance to transmit multiple users over the same channel bandwidth. Unfortunately, the DSSS data rate is limited by four users as a maximum due to the extensive multipath arrivals. This paper proposes a new scheme for the AUV communication systems called, deep learning coded index modulation-spread spectrum (DL-CIM-SS), to overcome the increasing data rate restriction of limited users number. The proposed DL-CIM-SS transmits the majority of information bits via the index of spreading code instead of transmitting all information bits physical. That doesn't only harvest more energy efficiency as the majority of information bits are not transmitted physically anymore, but also provide almost perfect detection at the receiver end. To further save the AUV energy, a pre-processing stage is added before feeding the received signal into the DL-based detector; the DL-based detector becomes environment-independent and no more training will be required during the online deployment. The proposed DL-CIM-SS performance is evaluated in this paper over simulation and measured underwater acoustic channels. The simulation results show the ability of the proposed scheme to increase the underwater acoustic data rate with significant energy efficiency improvement and low system bit and symbol error rate.



中文翻译:

基于深度学习的自动水下航行器系统的代码索引调制

已经提出了用于自动水下航行器(AUV)通信系统的长距离传输距离中的多用户直接序列扩频(DSSS),以在相同的信道带宽上传输多个用户。不幸的是,由于广泛的多径到达,DSSS数据速率最多受四个用户限制。本文提出了一种用于AUV通信系统的新方案,称为深度学习编码索引调制扩频(DL-CIM-SS),以克服有限的用户数量所增加的数据速率限制。所提出的DL-CIM-SS经由扩频码的索引发送大多数信息比特,而不是物理地发送所有信息比特。由于大多数信息位不再以物理方式传输,因此不仅可以提高能源效率,而且还可以在接收器端提供几乎完美的检测。为了进一步节省AUV能量,在将接收到的信号馈送到基于DL的检测器之前,添加了一个预处理阶段。基于DL的检测器变得与环境无关,并且在在线部署期间将不需要进行任何培训。拟议的DL-CIM-SS性能在本文中通过仿真和实测水下声通道进行了评估。仿真结果表明,该方案具有提高水下声数据速率,显着提高能源效率和降低系统位和符号错误率的能力。基于DL的检测器变得与环境无关,并且在在线部署期间将不需要进行任何培训。拟议的DL-CIM-SS性能在本文中通过仿真和实测水下声通道进行了评估。仿真结果表明,该方案具有提高水下声数据速率,显着提高能源效率和降低系统位和符号错误率的能力。基于DL的检测器变得与环境无关,并且在在线部署期间将不需要进行任何培训。拟议的DL-CIM-SS性能在本文中通过仿真和实测水下声通道进行了评估。仿真结果表明,该方案具有提高水下声数据速率,显着提高能源效率和降低系统位和符号错误率的能力。

更新日期:2020-11-13
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