The safety in autonomous driving depends on reliable and efficient sharing of massive sensor data via the internet of vehicles (IoV). In the upcoming sixth-generation (6G) mobile communication network, joint communication and sensing (JCAS) on millimeter wave (mmWave) bands has become a representative technique to achieve the dual functions of ultra-broadband communications and high-precision sensing at the same hardware and software costs. However, under high-mobility IoV,directional mmWave beams cannot provide robust network coverage. Based on this observation, in this paper we devise a sub-6GHz and mmWave integrated IoV wireless network architecture, where the sub-6GHz bands carry control information, and the mmWave bands perform large-volume communication and driving environment sensing. Each vehicle operates two mmWave beams respectively to realize communication and environment sensing. Then, we analyze the inter-beam interference between communication and sensing beams, and propose a Q-learning based beam resource allocation scheme to alleviate the interference problem. Simulation results demonstrate that our proposed scheme can highly improve the mmWave communication and sensing performance.
Q-Learning Based mmWave Beam Adjustment for Joint Communication and Sensing Under IoV Wireless Networks
24.06.2024
1197696 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch