Cost-efficient computation offloading in SAGIN: a deep reinforcement learning and perception-aided approach

• Main Authors: Gao, Yulan, Ye, Ziqiang, Yu, Han
• Other Authors: College of Computing and Data Science
• Format: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Space-air-ground integrated network; Deep reinforcement learning
• Online Access: https://hdl.handle.net/10356/181022
• Institution: Nanyang Technological University

The Space-Air-Ground Integrated Network (SAGIN), crucial to the advancement of sixth-generation (6G) technology, plays a key role in ensuring universal connectivity, particularly by addressing the communication needs of remote areas lacking cellular network infrastructure. This paper delves into the...