Industry adoption of Artificial Intelligence (AI)-native wireless receivers, or even modular, Machine Learning (ML)-aided wireless signal processing blocks, has been slow. The main concern is the lack of explainability of these trained ML models and the significant risks posed to network functionalities in case of failures, especially since (i) testing on every exhaustive case is infeasible and (ii) the data used for model training may not be available.

The 3rd generation partnership project (3GPP) initiated 5G-Advanced in Release 18, laying a solid foundation for the further evolution of 5G-Advanced. Release 19-the next wave of 5G-Advanced-will primarily focus on commercial deployment needs while serving as a bridge toward 6G. In this article, we provide an in-depth overview of the 5G-Advanced evolution in 3GPP Release 19. We not only delve into the key technology components and their corresponding use cases in 5G-Advanced evolution but also shed light on initial 3GPP studies toward 6G.

As the telecommunications industry stands at the crossroads between the fifth generation (5G) and sixth generation (6G) of mobile communications, the 3rd generation partnership project (3GPP) Release 20 emerges as a pivotal point of transition. By striking a balance between enhancing 5G-Advanced capabilities and setting the stage for 6G, Release 20 provides the crucial foundation upon which future mobile communication standards and deployments will be built.

As 5G deployments continue throughout the world, concerns regarding its energy consumption have gained significant traction. This article focuses on radio access networks (RANs) which account for a major portion of the network energy use. Firstly, we introduce the state-of-the-art 3GPP and O-RAN standardization work on enhancing RAN energy efficiency.

The rise of generative artificial intelligence (GenAI) is transforming the telecom industry. GenAI models, particularly large language models (LLMs), have emerged as powerful tools capable of driving innovation, improving efficiency, and delivering superior customer services in telecom. This paper provides an overview of GenAI for telecom from theory to practice. We review GenAI models and discuss their practical applications in telecom. Furthermore, we describe the key technology enablers and best practices for applying GenAI to telecom effectively.

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.