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：业务导向（TS）是一种通过将网络流量负载分配至适当基站（BS）来支持多样化业务需求并提升传输可靠性的有效方法。在传统以小区为中心的TS策略中，基站以集中式方式为所有用户设备（UE）做出TS决策，这更侧重于整个小区的整体性能，而忽视了个体UE的特定需求。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TS技术的出现。本文提出了一种面向高动态5G无线接入网（RAN）的知识迁移与联邦学习赋能的以用户为中心（KT-FLUC）TS框架。具体而言：首先，我们提出一种注意力加权分组联邦学习方案，使智能UE能利用本地模型与观测自主做出TS决策，并通过定义全局模型来协调局部TS决策并在UE间共享经验；其次，针对个体UE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，以减轻UE计算负担并降低联邦学习通信开销；此外，提出基于Q值的知识迁移方法初始化新接入UE，实现其训练效率的跳跃式提升。仿真结果表明，与基于小区的TS及其他以用户为中心的TS策略相比，本文提出的KT-FLUC算法能有效提升服务质量，分别降低65%和38%的时延，并提升52%和57%的吞吐量。