Mobility-Aware Coded Edge Caching in Vehicular Networks with Dynamic Content Popularity
Wenjie Liu, Haixia Zhang, Hui Ding, Dongyang Li, Dongfeng Yuan · 2021
Edge caching has been explored as an effective technology to alleviate the heavy traffic burden of the backhaul and avoid transmission congestion in vehicular networks. However, high mobility of vehicles could lead to repetitive content caching, resulting in high system cost. Because content popularity changes very frequently in vehicular networks, to provide better service for vehicle users, it is essential to update content frequently. This leads to expensive update cost at the same time. To reduce such cost, we propose a mobility-aware cost effective edge caching strategy, in which vehicle mobility, file encoding technology and dynamic content popularity are jointly taken into consideration. To reduce the complexity of formulated problem, deep reinforcement learning (DRL) approach is adopted. Simulation results show that the proposed mobility-aware coded edge caching strategy can dramatically reduce the system cost (up to 36% compared with classic caching algorithm).