Deep-Learning-Based Radio Channel Prediction for Vehicle-to-Vehicle Communications [Project Summary]

Details

• Creators:
  Molisch, Andreas F
• Corporate Creators:
  University of Southern California ; Pacific Southwest Region 9 UTC, University of Southern California ; METRANS Transportation Center (Calif.)
• Corporate Contributors:
  California Department of Transportation ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Vehicle to vehicle communications
  • Machine learning
  • Radio transmitters
  • Neural networks
  • Networks
  • channel modeling
  • spatial-temporal prediction
  • meta learning
• Publication/ Report Number:
  PSR-22-10 TO 065
• Resource Type:
  Brief
• Geographical Coverage:
  California ; United States
• Edition:
  Project Summary Report
• Corporate Publisher:
  Pacific Southwest Region 9 UTC, University of Southern California
• Abstract:
  Driver-assistance systems are essential to reduce accidents and improve energy efficiency through efficient convoying. To improve their effectiveness, it is important that vehicles are able to communicate with each other, informing each other of their intentions, and warning each other of obstacles or cross traffic that only some of the vehicles might see. However, since such communication must be done wirelessly, reliability and latency of the communication might become an issue. The objective of this project is to improve these aspects of the communication, by means of improved radio channel prediction.
• Format:
  PDF
• Funding:
  65A0674
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/77400/dot_77400_DS1.pdf
• File Type:
  [PDF - 241.18 KB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:d56e4e3438360b3e8aaadcb2672154c1cdddde934cd73d6c2407dec0c33fe83d7e3cf4a72febd4ce5c763076469e112691259fef75547a1b879f11c4e84f38c0