2022/11/25 by Rasoul Nikbakht, Nikbakht, Rasoul, Sarang Kahvazadeh +3
Computer Science · #Artificial intelligence #Backhaul (telecommunications) #Base station #Cache #Caching and Content Delivery #Cellular network #Computer network #Computer science #Cooperative Communication and Network Coding #Edge device #Enhanced Data Rates for GSM Evolution #FOS: Computer and information sciences #Image and Video Quality Assessment #Networking and Internet Architecture (cs.NI) #Operating system #Popularity #Reinforcement learning #Scalability #Scheme (mathematics) #Server #Telecommunications
paper · pdf · doi:10.48550/arxiv.2211.13962
openalex publication_date 2022/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Edge caching can significantly improve the 5G networks' performance both in terms of delay and backhaul traffic. We use a reinforcement learning-based (RL-based) caching technique that can adapt to time-location-dependent popularity patterns for on-demand video contents. In a private 5G, we implement the proposed caching scheme as two virtual network functions (VNFs), edge and remote servers, and measure the cache hit ratio as a KPI. Combined with the HLS protocol, the proposed video-on-demand (VoD) streaming is a reliable and scalable service that can adapt to content popularity.