Dynamic Coded Caching in Cellular Networks with User Mobility: A Reinforcement Learning Method.

Coded caching manages to release cellular network traffic by increasing transmission rate via satisfying multiple user requests simultaneously. Specific contents stored in the private cache memory are used as side information to decode individual requests from the coded broadcasting messages. Considering local content popularity could improve caching performance dramatically. However, in the mobility scenario, local content popularity varies with user movements. Even worse, contents in the cache memory might become outdated when the user location changes. In this paper, we propose a dynamic coded caching scheme that reduces the loss of coded caching gain due to the user movement and local content popularity dynamic changing. We quantify the relationship between user preference, local popularity, and user mobility. We formulate a metric to measure the performance of the proposed coded caching scheme and propose a reinforcement learning problem to obtain the cache replacement strategy in the mobility scenario. Numerical results verify that our obtained replacement policy significantly outperforms the popularity-based, least-frequently-used, and multilayer replacement policy in terms of traffic offloading.