Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base stations (BSs). In conventional cell-centric TS strategies, BSs make TS decisions for all user equipment (UEs) in a centralized manner, which focuses more on the overall performance of the whole cell, disregarding specific requirements of individual UE. The flourishing machine learning technologies and evolving UE-centric 5G network architecture have prompted the emergence of new TS technologies. In this paper, we propose a knowledge transfer and federated learning-enabled UE-centric (KT-FLUC) TS framework for highly dynamic 5G radio access networks (RAN). Specifically, first, we propose an attention-weighted group federated learning scheme. It enables intelligent UEs to make TS decisions autonomously using local models and observations, and a global model is defined to coordinate local TS decisions and share experiences among UEs. Secondly, considering the individual UE's limited computation and energy resources, a growing and pruning-based model compression method is introduced, mitigating the computation burden of UEs and reducing the communication overhead of federated learning. In addition, we propose a Q-value-based knowledge transfer method to initialize newcomer UEs, achieving a jump start for their training efficiency. Finally, the simulations show that our proposed KT-FLUC algorithm can effectively improve the service quality, achieving 65\% and 38\% lower delay and 52% and 57% higher throughput compared with cell-based TS and other UE-centric TS strategies, respectively.

翻译：业务导引是一种通过将网络负载分配至合适基站来支持多样化业务需求并增强传输可靠性的前景方案。在传统以小区为中心的业务导引策略中，基站以集中方式为所有用户设备制定导引决策，这种策略更关注整个小区的整体性能，而忽略了单个用户设备的具体需求。蓬勃发展的机器学习技术与不断演进的以用户为中心的5G网络架构催生了新型业务导引技术。本文针对高动态5G无线接入网，提出了一种基于知识迁移与联邦学习的以用户为中心的业务导引框架。具体而言：首先，我们提出了一种注意力加权组联邦学习方案，使智能用户设备能够利用本地模型与观测自主制定业务导引决策，并定义全局模型以协调本地导引决策、实现用户间的经验共享；其次，针对单个用户设备计算与能源资源受限的问题，引入基于增长剪枝的模型压缩方法，减轻用户设备计算负担并降低联邦学习通信开销；此外，我们提出基于Q值的知识迁移方法初始化新接入用户设备，实现其训练效率的跳跃式提升。最后，仿真结果表明，与基于小区的业务导引及其他以用户为中心的业务导引策略相比，所提KT-FLUC算法可有效提升服务质量，分别实现65%和38%的时延降低，以及52%和57%的吞吐量提升。