AI-RAN: The pathway to future wireless networks

With the rapid advancement of artificial intelligence (AI), the radio access network (RAN) is poised to undergo a transformative evolution toward the convergence of AI and RAN functionalities, referred to as the AI-RAN paradigm. AI-RAN integrates high-performance computing resources into RAN infrastructures, thereby enabling the execution of both AI and RAN workloads on the same infrastructure. As a result, it improves resource utilization, reduces energy consumption, and promotes swift AI-related responses. In this paper, we provide a comprehensive overview of AI-RAN, whereby we broadly categorize the discussion into three aspects: AI and RAN, AI for RAN, and AI on RAN. In particular, we begin with AI and RAN, which encompass the hardware architecture, software stack, as well as orchestration of computational and communication resources. We subsequently elaborate on the AI on RAN, examining various approaches to leveraging AI methods to enhance RAN performance. For the topic of AI on RAN, we conduct an in-depth investigation into the schemes that take RAN as a platform to facilitate AI services, where we review distributed learning for multi-cell and multi-vendor RANs, including federated and multi-agent reinforcement learning, highlighting issues of data heterogeneity, control-plane overhead, convergence under mobility, privacy, and adversarial robustness in the RAN ecosystems. We also demonstrate several use cases pertaining to the AI-RAN framework. We conclude by outlining key open issues and research directions.