The rapid development of Generative Artificial Intelligence (GenAI) has catalyzed a transformative technological revolution across all walks of life. As the backbone of wideband communication, optical networks are expecting high-level autonomous operation and zero-touch management to accommodate their expanding network scales and escalating transmission bandwidth. The integration of GenAI is deemed as the pivotal solution for realizing zero-touch optical networks. However, the lifecycle management of optical networks involves a multitude of tasks and necessitates seamless collaboration across multiple layers, which poses significant challenges to the existing single-agent GenAI systems. In this paper, we propose a GenAI-driven hierarchical multi-agent framework designed to streamline multi-task autonomous execution for zero-touch optical networks. We present the architecture, implementation, and applications of this framework. A field-deployed mesh network is utilized to demonstrate three typical scenarios throughout the lifecycle of optical network: quality of transmission estimation in the planning stage, dynamic channel adding/dropping in the operation stage, and system capacity increase in the upgrade stage. The case studies, illustrate the capabilities of multi-agent framework in multi-task allocation, coordination, execution, evaluation, and summarization. This work provides a promising approach for the future development of intelligent, efficient, and collaborative network management solutions, paving the way for more specialized and adaptive zero-touch optical networks.

This paper investigates a range of cutting-edge technologies and architectural innovations aimed at simplifying network operations, reducing operational expenditure (OpEx), and enabling the deployment of new service models. The focus is on (i) Proposing novel, more efficient 6G architectures, with both Control and User planes enabling the seamless expansion of services, while addressing long-term 6G network evolution. (ii) Exploring advanced techniques for constrained artificial intelligence (AI) operations, particularly the design of AI agents for real-time learning, optimizing energy consumption, and the allocation of computational resources. (iii) Identifying technologies and architectures that support the orchestration of backend services using serverless computing models across multiple domains, particularly for vertical industries. (iv) Introducing optically-based, ultra-high-speed, low-latency network architectures, with fast optical switching and real-time control, replacing conventional electronic switching to reduce power consumption by an order of magnitude.

The drive toward automating cellular network operations has grown with the increasing complexity of these systems. Despite advancements, full autonomy currently remains out of reach due to reliance on human intervention for modeling network behaviors and defining policies to meet target requirements. Network Digital Twins (NDTs) have shown promise in enhancing network intelligence, but the successful implementation of this technology is constrained by use case-specific architectures, limiting its role in advancing network autonomy. A more capable network intelligence, or "telecommunications brain", is needed to enable seamless, autonomous management of cellular network. Large Language Models (LLMs) have emerged as potential enablers for this vision but face challenges in network modeling, especially in reasoning and handling diverse data types. To address these gaps, we introduce Hermes, a chain of LLM agents that uses "blueprints" for constructing NDT instances through structured and explainable logical steps. Hermes allows automatic, reliable, and accurate network modeling of diverse use cases and configurations, thus marking progress toward fully autonomous network operations.

Telecom networks are becoming increasingly complex, with diversified deployment scenarios, multi-standards, and multi-vendor support. The intricate nature of the telecom network ecosystem presents challenges to effectively manage, operate, and optimize networks. To address these hurdles, Artificial Intelligence (AI) has been widely adopted to solve different tasks in telecom networks. However, these conventional AI models are often designed for specific tasks, rely on extensive and costly-to-collect labeled data that require specialized telecom expertise for development and maintenance. The AI models usually fail to generalize and support diverse deployment scenarios and applications. In contrast, Foundation Models (FMs) show effective generalization capabilities in various domains in language, vision, and decision-making tasks. FMs can be trained on multiple data modalities generated from the telecom ecosystem and leverage specialized domain knowledge. Moreover, FMs can be fine-tuned to solve numerous specialized tasks with minimal task-specific labeled data and, in some instances, are able to leverage context to solve previously unseen problems. At the dawn of 6G, this paper investigates the potential opportunities of using FMs to shape the future of telecom technologies and standards. In particular, the paper outlines a conceptual process for developing Telecom FMs (TFMs) and discusses emerging opportunities for orchestrating specialized TFMs for network configuration, operation, and maintenance. Finally, the paper discusses the limitations and challenges of developing and deploying TFMs.

As telecommunications networks become increasingly complex, the integration of advanced technologies such as network digital twins and generative artificial intelligence (AI) emerges as a pivotal solution to enhance network operations and resilience. This paper explores the synergy between network digital twins, which provide a dynamic virtual representation of physical networks, and generative AI, particularly focusing on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We propose a novel architectural framework that incorporates these technologies to significantly improve predictive maintenance, network scenario simulation, and real-time data-driven decision-making. Through extensive simulations, we demonstrate how generative AI can enhance the accuracy and operational efficiency of network digital twins, effectively handling real-world complexities such as unpredictable traffic loads and network failures. The findings suggest that this integration not only boosts the capability of digital twins in scenario forecasting and anomaly detection but also facilitates a more adaptive and intelligent network management system.

The Robot Operating System (ROS2) is the most widely used software platform for building robotics applications. FogROS2 extends ROS2 to allow robots to access cloud computing on demand. However, ROS2 and FogROS2 assume that all robots are locally connected and that each robot has full access and control of the other robots. With applications like distributed multi-robot systems, remote robot control, and mobile robots, robotics increasingly involves the global Internet and complex trust management. Existing approaches for connecting disjoint ROS2 networks lack key features such as security, compatibility, efficiency, and ease of use. We introduce FogROS2-SGC, an extension of FogROS2 that can effectively connect robot systems across different physical locations, networks, and Data Distribution Services (DDS). With globally unique and location-independent identifiers, FogROS2-SGC securely and efficiently routes data between robotics components around the globe. FogROS2-SGC is agnostic to the ROS2 distribution and configuration, is compatible with non-ROS2 software, and seamlessly extends existing ROS2 applications without any code modification. Experiments suggest FogROS2-SGC is 19x faster than rosbridge (a ROS2 package with comparable features, but lacking security). We also apply FogROS2-SGC to 4 robots and compute nodes that are 3600km apart. Videos and code are available on the project website https://sites.google.com/view/fogros2-sgc.

Traditionally, network operations (NetOps) teams used performance monitoring tools to manage the health and performance of corporate networks. However, as network usage has increased and network deployments have become more disaggregated, many people are looking for alternative performance monitoring methods, such as Artificial Intelligence for IT operations or AIOps. This blog discusses NetOps and AIOps in detail and analyses how NetOps can be a bridge to AIOps, which will revolutionise the world of tech & business. Artificial intelligence for IT operations refers to applying AI and related technologies such as ML and NLP to traditional IT businesses and activities (AIOps). AIOps assist IT Ops, DevOps, and SRE teams (Site reliability engineers) in working smarter and faster.

Artificial intelligence as part of network infrastructure monitoring has been a popular topic for several years. But only recently has the development of AI network monitoring made it practical to deploy in production networks on a broader scale. With AI network monitoring, the main objectives are to sustain optimal service levels, gain accurate insight into potential infrastructure issues and get that data before business and network operations are affected. To help with this process, machine learning -- a type of AI -- applies algorithms to telemetry and other data streams to gauge a baseline for normal operations. Once the AI network monitoring service establishes that baseline, it can then look for deviations that might indicate a potential infrastructure problem.

While each revolutionizes sectors and creates new experiences on its own, the combination of 5G and AI will be really disruptive. On-device computing, the edge cloud, and 5G work together to form a ubiquitous connectivity fabric of smart devices and services. This point of convergence is critical to our concept of the intelligent wireless edge. The commercial deployment of 5G has begun. But, to put it another way, 5G isn't just another G. It's a total ecosystem shift in how networks are managed and administered, as well as how apps function on them.

The commercial roll out of 5G is now under way. But simply put, 5G is not just another G. It's a complete ecosystem change in the way networks are run and managed, including how applications run on the network. Other, emerging use case groups include massive machine type communication, or MTC. This is where the connectivity and density of 5G really comes into play. MTC enables the connectivity of a huge number of devices –millions, billions of devices in fact, all of which are connected.