Content caching has brought huge potential for the provisioning of non-safety related infotainment services in future vehicular networks. Assisted by multiaccess edge computing, roadside units (RSUs) could become cache-capable and… Click to show full abstract
Content caching has brought huge potential for the provisioning of non-safety related infotainment services in future vehicular networks. Assisted by multiaccess edge computing, roadside units (RSUs) could become cache-capable and offer fast caching services to moving vehicles for content providers. On the other hand, deep learning makes it possible to accurately estimate the behavior of vehicles, which enables effective proactive caching strategies. However, caching services considering both the mobility of vehicles and storage could incur increased latency and considerable cost due to the cache size needed in RSUs. In this paper, we model such a problem using Markov decision processes, and propose a heuristic
               
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