The radio access network (RAN) landscape is undergoing a transformative shift from traditional, communication-centric infrastructures towards converged compute-communication platforms. This article introduces AIRAN which integrates both RAN and artificial intelligence (AI) workloads on the same infrastructure. By doing so, AI-RAN not only meets the performance demands of future networks but also improves asset utilization.

The demands from wireless connectivity continue to grow, placing greater emphasis in designing efficient wireless networks and even greater emphasis on End-to-End (E2E) system verification. Next generation Telecom networks need to enable the proliferation of Artificial Intelligence (AI) in wireless communications, in addition to expanding traditional road map items (such as throughput, latency, user capacity, etc.). We provide a 5G network architecture reference design and present a methodology to evaluate and validate the 5G Stand Alone (SA) network.

6G brings a paradigm shift towards AI-native wireless systems, necessitating the seamless integration of digital signal processing (DSP) and machine learning (ML) within the software stacks of cellular networks. This transformation brings the life cycle of modern networks closer to AI systems, where models and algorithms are iteratively trained, simulated, and deployed across adjacent environments. In this work, we propose a robust framework that compiles Python-based algorithms into GPU-runnable blobs.

As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments.

Airborne Base Stations (ABSs) allow for flexible geographical allocation of network resources with dynamically changing load as well as rapid deployment of alternate connectivity solutions during natural disasters. Since the radio infrastructure is carried by unmanned aerial vehicles (UAVs) with limited flight time, it is important to establish the best location for the ABS without exhaustive field trials.

Channel-state information (CSI)-based sensing will play a key role in future cellular systems. However, no CSI dataset has been published from a real-world 5G NR system that facilitates the development and validation of suitable sensing algorithms. To close this gap, we publish three real-world wideband multiantenna multi-open RAN radio unit (O-RU) CSI datasets from the 5G NR uplink channel: an indoor lab/office room dataset, an outdoor campus courtyard dataset, and a device classification dataset with six commercial-off-the-shelf (COTS) user equipments (UEs).

AI and communication is a key usage scenario envisaged for 6G. In contrast to 5G, where AI adoption has come only as an afterthought, 6G aims to embrace AI right from the beginning. AI-native RAN shifts the trend from fixed, one-size-fits-all algorithms to continuously adaptive, data-driven intelligence embedded throughout the RAN stack for site-specific optimizations. However, embedding AI for RAN introduces stringent latency constraints and computational challenges.