基于多智体强化学习的接入网络切片动态切换

网络切片技术将广泛应用于以5G为代表的下一代移动通信网络中，为网络中多样化的业务提供按需的网络服务。在基于切片的移动通信网络中，用户往往需要根据不断变化的网络状态，进行接入切片的动态切换，以获得更好的网络传输和服务性能。考虑到存在多个用户的网络中，某一用户的接入选择将对接入切片的可用传输资源产生影响，从而影响其他用户的接入和切换决策。因此，该文将基于网络切片的移动通信网络中多用户的接入切换建模为一个多人随机博弈问题，采用多智体强化学习的方法对该问题进行求解，并设计了一种基于分布式多智体强化学习算法的多用户接入切片动态切换机制。在此基础上，通过仿真实验验证了该切换算法性能。

Dynamical Accessing Handoff by Using Multi-Agent Reinforcement Learning in Slice Based Mobile Networks

In future mobile networks, such as 5G networks, network slicing will be a promising technology to provide customizing services for different users with different transmission requirements. According to the dynamic network state in slice based mobile networks, users need to make accessing slice handoff periodically for improving the transmission performance. However, in a multi-user networks, the accessing choice of a user changes the amount of available transmission resources in the system, which impacts the accessing choices of other users. Thus, in this paper, we model the multi-user handoff problem in slice based mobile networks as a multi-agent random game. Then, we use multi-agent reinforcement learning (MARL) to solve this game, and propose a multi-user accessing handoff algorithm based on distributed MARL method. The numerical results validate the performance of our proposed multi-user accessing handoff algorithm in slice based mobile networks.