LSTM-Based Distributed Conditional Generative Adversarial Network for Data-Driven 5G-Enabled Maritime UAV Communications

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, the authors 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, they have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, they have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for their 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.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3187941
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Rasheed, Iftikhar
  • Asif, Muhammad
  • Ihsan, Asim
  • Khan, Wali Ullah
  • Ahmed, Manzoor
  • Rabie, Khaled M
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2431-2446
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Drones; Machine learning; Millimeter wave communication systems; Water transportation; Wireless communication systems
• Subject Areas: Aviation; Data and Information Technology; Marine Transportation;

Filing Info

• Accession Number: 01888998
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 26 2023 3:59PM