Future 6G radio access networks (RANs) will be artificial intelligence (AI)-native: observed, reasoned about, and re-configured by autonomous agents cooperating across the cloud-edge continuum. We introduce MX-AI, the first end-to-end agentic system that (i) instruments a live 5G Open RAN testbed based on OpenAirInterface (OAI) and FlexRIC, (ii) deploys a graph of Large-Language-Model (LLM)-powered agents inside the Service Management and Orchestration (SMO) layer, and (iii) exposes both observability and control functions for 6G RAN resources through natural-language intents. On 50 realistic operational queries, MX-AI attains a mean answer quality of 4.1/5.0 and 100 % decision-action accuracy, while incurring only 8.8 seconds end-to-end latency when backed by GPT-4.1. Thus, it matches human-expert performance, validating its practicality in real settings. We publicly release the agent graph, prompts, and evaluation harness to accelerate open research on AI-native RANs. A live demo is presented here: https://www.youtube.com/watch?v=CEIya7988Ug&t=285s&ab_channel=BubbleRAN

--One of the primary research interests adhering to next-generation fiber-wireless access networks is human-to-machine/robot (H2M/R) collaborative communications facilitating Industry 5.0. This paper discusses scalable H2M/R communications across large geographical distances that also allow rapid onboarding of new machines/robots as 72% training time is saved through global-local coordinated learning. In recent years, several inter-disciplinary technical paradigms like cyber-physical systems, Industrial IoT, robotics, big data, cloud/edge and cognitive computing, and virtual/augmented reality (VR/AR) have received significant attention from both industry and academia. The primary reason behind this development is the inclusion of industry vertical scenarios like Industry 4.0 in the fifth and beyond-fifth generation mobile technologies [1]. Although Industry 4.0 primarily involved connectivity among cyber-physical systems, Industry 5.0 will focus on the "human and machine/robots/cobots" relationship [2] to ensure real-time monitoring of products' condition, use, and the environment through sensors and external data sources, dynamic control of product functions and personalized user experience through embedded software in the products, optimization of use and performance of products, and autonomous delivery of products through coordinated operations with other products and systems.

In wireless networks, applying deep learning models to solve matching problems between different entities has become a mainstream and effective approach. However, the complex network topology in 6G multiple access presents significant challenges for the real-time performance and stability of matching generation. Generative artificial intelligence (GenAI) has demonstrated strong capabilities in graph feature extraction, exploration, and generation, offering potential for graph-structured matching generation. In this paper, we propose a GenAI-enabled matching generation framework to support 6G multiple access. Specifically, we first summarize the classical matching theory, discuss common GenAI models and applications from the perspective of matching generation. Then, we propose a framework based on generative diffusion models (GDMs) that iteratively denoises toward reward maximization to generate a matching strategy that meets specific requirements. Experimental results show that, compared to decision-based AI approaches, our framework can generate more effective matching strategies based on given conditions and predefined rewards, helping to solve complex problems in 6G multiple access, such as task allocation.

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

The Internet of Things (IoT) is a term that is becoming more and more popular as time goes on. But what exactly is it, and what implications does it have for our lives? In this blog post, we'll take a look at what the Internet of Things is, how it works, and some of the potential benefits involved. The Internet of Things (IoT) is a network of physical objects connected to the internet and can collect and exchange data. This data can be used to monitor and control the objects and automate processes and tasks, reducing the need for human intervention. In other words, the IoT is a way of connecting devices and objects to the internet so that they can communicate with each other. An IoT device can be anything from simple sensor technology to more complex devices like connected cars or smart appliances.

The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. However, distinct network slices must be dynamically controlled to avoid service level agreement (SLA) variation caused by rapid changes in the environment. Therefore, this paper introduces a novel framework able to manage the network slices through provisioned resources intelligently. Due to diverse heterogeneous environments, intelligent machine learning approaches require sufficient exploration to handle the harshest situations in a wireless network and accelerate convergence. To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules. To this end, the O-RAN slicing is represented as a Markov decision process (MDP) which is then solved optimally for resource allocation to meet service demand using the EDRL approach. In terms of reaching service demands, simulation results show that the proposed approach outperforms the DRL baseline by 62.2%.

We, and our customers, have long understood that networks are most valuable when they do more than support their own weight. They become assets only when they enable the growth of business strategies. To get networks to work at this level, we have to get a higher level of performance and agility from them. That's why we launched a series of products and services based on intent-based networking two years ago. Our goal was to reinvent access networking to serve business needs.

Seattle-based Opanga Networks says it has found a way to minimize wireless network congestion and improve traffic flow by using machine learning algorithms in the network core. The company's software is being used today in 3G and 4G networks to make them more efficient without requiring more spectrum Basically, Opanga's software platform is deployed in the packet core. The software, which can run over existing hardware or new COTS hardware, uses machine learning to identify what Opanga calls "elephant flows." These are large users of capacity. According to Opanga CEO Dave Gibbons, about 3 percent of data sessions on a wireless network generate about 60 percent of traffic.

Artificial intelligence (AI) and machine learning are two of the latest networking buzzwords being thrown around the industry. The problem is many enterprise network managers remain confused about the real value of these vastly useful technologies. Emerging network analytics services, powered by AI and machine learning promise to transform traditional infrastructure management models by simplifying operations, lowering costs, and giving unprecedented insights into the user experience – improving the productivity of both IT professionals and their users. For network staff, the concept and value of these technologies is extremely powerful if applied to the right problems. One big problem is today's operational challenge in dealing with the mass of user, device, application and network service data traversing the enterprise access infrastructure.

The Internet of Things is expected to grow quickly to tens of billions of connected devices, from smart refrigerators to smart showers to smart cruise ships. And pretty soon, it's going to extend to smart cars, Intel demonstrated at its recent autonomous cars event in San Jose, Calif. But Intel knows that we'll have to get data in and out of those cars at rates that are much faster than today's LTE mobile networks can handle. And that's why Rob Topol, general manager of Intel's 5G business and technology, believes that 5G wireless networking will be like the "oxygen" for self-driving cars. Intel is making 5G modem chips to transfer data at gigabits a second over wireless networks in the future, perhaps as early as 2020. Topol believes this wireless networking will enable self-driving cars to communicate with connected infrastructure. That infrastructure will help the cars process sensor, safety, and information for the car and return the results quickly to the cars.