Content Caching Optimization Based on Improved Bandit Learning Algorithm

The combination of MEC and artificial intelligence technology effectively solves the problems of network congestion, high network latency, and high local computing energy consumption of users. The basis for solving these problems is to solve the problem of low caching hit rate. In large-scale Internet of Things, utilizing caching solutions can effectively improve the caching hit rate of edge nodes, thereby enhancing the user experience. This chapter establishes a large-scale collaborative mobile edge computing architecture supported by 6G. In this architecture, a fine-grained context based caching optimization problem is established with the goal of achieving the hit rate of all user requests in the system. To solve this optimization problem, this article uses a caching optimization algorithm based on Lin UCB to solve the optimization algorithm. Finally, this article uses simulation experiments to verify the superiority of the cache optimization scheme and the rationality of the experiments.