AI‐Enabled Radio Access Network

Abstract

Initialization and performance optimization of wireless networks are key enablers for the fifth‐generation (5G) radio access network (RAN) design. However, heterogeneity of wireless devices and massive connectivity requirements on 5G systems result in intractable end‐to‐end communication models where traditional model‐based wireless protocols cannot be straightforwardly applied. To tackle this issue, a new 5G RAN design paradigm is addressed based on artificial intelligence (AI) techniques, which lead to data‐driven optimization solutions for nontrivial implementation problems of 5G systems. Still, however, it has not been adequately investigated how to include AI technologies into 5G RAN architecture. To answer this fundamental question, this article illustrates the possibility and the feasibility of using AI for handling implementation difficulties occurring in 5G communication systems. The way to replace traditional model‐based communication solutions with AIs is investigated, especially focused on massive random access and nonconvex resource management applications. Several examples of reinforcement learning, supervised learning, and unsupervised learning techniques applied to 5G RAN are visited. Recent successes of AI‐defined 5G RAN verify that AIs can effectively identify unknown and optimal communication rules in various networking scenarios including initial accessing protocols for massive connectivity and wireless resource control strategies for network performance optimization.