The evolution of mobile communications towards beyond 5th-generation (B5G) networks is envisaged to incorporate high levels of network automation. Network automation requires the development of a network architecture that accommodates multiple solutions based on artificial intelligence (AI) and machine learning (ML). Consequently, integrating AI into the 5th-generation (5G) systems such that we could leverage the advantages of ML techniques to optimize and improve the networks is one challenging topic for B5G networks. Based on a review of 5G system architecture, the state-of-the-art candidate AI/ML techniques, and the progress of the state of the art, and the on AI/ML for 5G in standards we define an AI architecture and performance evaluation framework for the deployment of the AI/ML solution in B5G networks. The suggested framework proposes three AI architectures alternatives, a centralized, a completely decentralized and an hybrid AI architecture. More specifically, the framework identifies the logical AI functions, determines their mapping to the B5G radio access network architecture and analyses the associated deployment cost factors in terms of compute, communicate and store costs. The framework is evaluated based on a use case scenario for heterogeneous networks where it is shown that the deployment cost profiling is different for the different AI architecture alternatives, and that this cost should be considered for the deployment and selection of the AI/ML solution.

预计移动通信向超越第 5 代 (B5G) 网络的演进将包含高水平的网络自动化。网络自动化需要开发一种网络架构，以适应基于人工智能 (AI) 和机器学习 (ML) 的多种解决方案。因此，将 AI 集成到第 5 代 (5G) 系统中，以便我们可以利用 ML 技术的优势来优化和改进网络，是 B5G 网络的一个具有挑战性的主题。基于对 5G 系统架构、最先进的候选 AI/ML 技术、最先进技术的进展以及针对 5G 的 AI/ML 标准的回顾，我们定义了 AI 架构和性能在 B5G 网络中部署 AI/ML 解决方案的评估框架。建议的框架提出了三种 AI 架构替代方案，即集中式、完全分散式和混合 AI 架构。更具体地说，该框架识别逻辑 AI 功能，确定它们与 B5G 无线接入网络架构的映射，并在计算、通信和存储成本方面分析相关的部署成本因素。该框架是基于异构网络的用例场景进行评估的，其中表明不同 AI 架构替代方案的部署成本分析是不同的，并且在部署和选择 AI/ML 解决方案时应考虑此成本。确定它们与 B5G 无线接入网络架构的映射，并在计算、通信和存储成本方面分析相关的部署成本因素。该框架是基于异构网络的用例场景进行评估的，其中表明不同 AI 架构替代方案的部署成本分析是不同的，并且在部署和选择 AI/ML 解决方案时应考虑此成本。确定它们与 B5G 无线接入网络架构的映射，并在计算、通信和存储成本方面分析相关的部署成本因素。该框架是基于异构网络的用例场景进行评估的，其中表明不同 AI 架构替代方案的部署成本分析是不同的，并且在部署和选择 AI/ML 解决方案时应考虑此成本。