Jun 26, 2025 AI-Wireless Infrastructure 101 Explore the intelligent future of mobile networks Artificial Intelligence is reshaping industries—and mobile broadband is no exception. At the heart of this transformation is a simple truth: AI thrives on data, and data depends on fast, reliable connectivity. In this resource guide, we’ll first dive into how AI is redefining the Radio Access Network (RAN), unlocking smarter, more autonomous operations, boosting service quality, improving energy efficiency, and making multi-vendor networks work in harmony. We will also provide an overview of how wireless networks are supporting and advancing AI across industries. AI-Wireless Infrastructure 101 Resource Guide Section 1: Artificial Intelligence in Wireless RAN Modern RANs must support thousands of simultaneous connections, low latency use cases like autonomous driving and virtual reality, and highly variable traffic patterns. The transition toward multi-vendor, virtualized and cloud-native architectures such as Open RAN (ORAN) and virtual RAN (vRAN) complicates the networks further. The challenge is how to manage this level of complexity. Using static configuration rules or manual operations is no longer viable. AI offers a solution to this challenge. It enables data-driven, adaptive and predictive control of RAN components, replacing many static configuration methods with algorithms that can learn and optimize over time. Read More Section 2: Technical Applications of AI in Wireless RAN AI enables Self-Organizing Networks (SON), traffic prediction, and load balancing, all of which can improve the quality of service (QoS) for the end user, improve the efficiency of radio resources, and reduce the amount of manual intervention required by network managers. Explore in detail some of the ways that AI is being used in the RAN, specifically: Network Optimization, Energy Efficiency, Enhanced Spectrum Management, and Edge Intelligence and Distributed RAN (virtual RAN/Open RAN). Read More Section 3: The Challenges of Artificial Intelligence in the RAN Deploying AI across diverse RAN environments — crossing traditional, virtualized, Open RAN, and private networks — presents technical, operational, regulatory and strategic challenges. Understanding these limitations is essential for MNOs, vendors and enterprises to set realistic expectations and plan for effective implementation. The challenges and issues can be broken down into the following areas: Data collection Model applicability Compute constraints Operational trust Security and privacy Business considerations Read More Join our mailing list to be notified when the complete resource guide is released. Sign up for Updates Research and White Papers