Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning
Jilin University · Jilin Province Science and Technology Department · +3 more institutions
Abstract
Autonomous aerial vehicles (AAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a AAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a AAV-enabled virtual antenna array to improve transmission performance from the AAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective…
Citation impact
- FWCI
- 243.08
- Percentile
- 100%
- References
- 65
Authors
7- GSGeng SunCorresponding
Jilin University, Jilin Province Science and Technology Department
- JXJian Xiao
Jilin University, Jilin Province Science and Technology Department
- JLJiahui Li
Jilin University, Jilin Province Science and Technology Department
- JWJiacheng Wang
Nanyang Technological University
- JKJiawen Kang
Guangdong University of Technology
Topics & keywords
- Computer science
- Reinforcement learning
- Mobile telephony
- Mobile computing
- Artificial intelligence
- Human–computer interaction
- Multimedia
- Computer network