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Radio access networks (RAN) are experiencing a paradigm shift toward interoperable, intelligent, software-defined, and cloud-native open RAN solutions. A key challenge toward the adoption and deployment of open RAN at scale is performance, since commercial RAN must meet stringent requirements in terms of capacity, data rate, latency, coverage, energy efficiency, reliability, and security, among others. To overcome the performance challenge, it is critical to leverage the power of hardware acceleration to offload compute-heavy RAN workloads to specialized hardware devices to enable accelerated compute for open RAN deployments. In this article, we provide an overview of state of the art hardware acceleration for open RAN in the fifth generation (5G) cellular networks by reviewing RAN architectural evolution, presenting the role of hardware acceleration in RAN, and introducing the latest O-RAN Alliance's development of acceleration abstraction layer. We also present a practical implementation of inline hardware acceleration for open RAN layer 1 processing and identify several areas for future exploration.

Digital twin networks (DTNs) are real-time replicas of physical networks. They are emerging as a powerful technology for design, diagnosis, simulation, what-if-analysis, and artificial intelligence (AI)/ machine learning (ML) driven real-time optimization and control of the sixth generation (6G) wireless networks. Despite the great potential of what digital twins can offer for 6G, realizing the desired capabilities of DTNs requires tackling many design aspects including data, models, and interfaces. In this article we provide an overview of DTNs by presenting prominent use cases and their service requirements, describing a reference architecture, and discussing fundamental design aspects. We also present a real-world example to illustrate how DTNs can be built upon and operated in a real-time reference development platform - Omniverse.

Artificial intelligence (AI) has emerged as a powerful technology that improves system performance and enables new features in 5G and beyond. Standardization - defining functionality and interfaces - is essential for driving the industry alignment required to deliver the mass adoption of AI in 5G-Advanced and 6G. However, fragmented efforts in different standards bodies, such as the 3rd Generation Partnership Project (3GPP) and the Open Radio Access Network (O-RAN) Alliance, can lead to confusion and uncertainty about which standards to follow and which aspects of the standards to embrace. This article provides a joint 3GPP and O-RAN perspective on the state of the art in AI adoption in mobile communication systems, including the fundamentals of 5G architecture and its evolution toward openness and intelligence, AI for 5G-Advanced evolution and open RAN, and a case study on AI-enabled traffic steering. We also identify several areas for future exploration to accelerate AI adoption on the path toward 6G.