Leveraging mobility and content caching for proactive load balancing in heterogeneous cellular networks. Issue 2 (3rd September 2019) - Manzoor, Sanaullah; Mazhar, Suleman; Asghar, Ahmad; Noor Mian, Adnan; Imran, Ali; Crowcroft, Jon
Abstract: Evolution of cellular networks into dynamic, dense, and heterogeneous networks have introduced new challenges for cell resource optimization, especially in the imbalanced traffic load regions. Numerous load balancing schemes have been proposed to tackle this issue; however, they operate in a reactive manner that confines their ability to meet the top‐notch quality of experience demands. To address this challenge, we propose a novel proactive load balancing scheme. Our framework learns users' mobility and demands statistics jointly to proactively cache future contents during their stay at lightly loaded cells, which results in quality of experience maximization and load minimization. System level simulations are performed and compared with the state‐of‐the‐art reactive schemes. Abstract : A novel proactive load balancing framework is proposed, which leverages user mobility and content demand statistics jointly to maximize downlink throughput and minimize cell loads. This framework models user mobility patterns through Semi‐Markov renewal process and exploits content demand history for proactive content caching.