In this paper we develop a method to coordinate the deployment of a multi-robot team to reach some locations of interest, so-called primary goals, and to transmit the information from these positions to a static Base Station (BS), under connectivity constraints. The relay positions have to be established for some robots to maintain the connectivity at the moment in which the other robots visit the primary goals. Once every robot reaches its assigned goal, they are again available to cover new goals, dynamically re-distributing the robots to the new tasks. The contribution of this work is a two stage method to deploy the team. Firstly, clusters of relay and primary positions are computed, obtaining a tree formed by chains of positions that have to be visited. Secondly, the order for optimally assigning and visiting the goals in the clusters is computed. We analyze di ff erent heuristics for sequential and parallel deployment in the clusters, obtaining sub-optimal solutions in short time for di ff erent number of robots and for a large amount of goals.

Malaysia declared it'll build a first-of-its-kind AI system powered by Huawei Technologies chips, only to distance itself from that statement a day later, underscoring the Asian nation's delicate position in the U.S.-Chinese AI race. Deputy Minister of Communications Teo Nie Ching said in a speech Monday her country would be the first to activate an unspecified class of Huawei "Ascend GPU-powered AI servers at national scale." Malaysia would deploy 3,000 units of Huawei's primary AI offering by 2026, she said in prepared remarks reviewed by Bloomberg News. Chinese startup DeepSeek would also make one of its AI models available to the Southeast Asian country, the official added.

Abstract--Despite recent advances in applying large language models (LLMs) and machine learning (ML) techniques to open radio access network (O-RAN), critical challenges remain, such as insufficient cooperation between radio access network (R AN) intelligent controllers (RICs), high computational deman ds hindering real-time decisions, and the lack of domain-specific fine-tuning. Therefore, this article introduces the LLM-empowe red hierarchical RIC (LLM-hRIC) framework to improve the collaboration between RICs in O-RAN. The RL-empowered near-RT RIC acts as an implementer, combining this guidance with local real-time data to make near-RT decisions. We evaluate the feasibility and performance of the LLM-hRIC framework in an integrated access and backhaul (IAB) network setting, and finally, disc uss the open challenges of the LLM-hRIC framework for O-RAN. The open radio access network (O-RAN) has recently gained significant attention for its ability to prompt inter op-erability and flexibility.

Huawei Technologies debuted its first in-house operating system for personal computers, offering an alternative to Microsoft's Windows as China pushes to replace American technologies amid rising geopolitical tensions. The company's HarmonyOS is now ready to run on the MateBook Fold, its new foldable laptop, said the head of its consumer business, Richard Yu, on Monday. Huawei is working to make its operating system compatible with more than 2,000 apps, including the popular WeChat and QQ messaging platforms, he said. The laptop features an 18-inch organic light-emitting diode screen when opened flat. Along with the Huawei MateBook Pro and MateBook Fold Ultimate Design laptops, it will be available from June 6.

In this paper, we incorporate physical knowledge into learning-based high-precision target sensing using the multi-view channel state information (CSI) between multiple base stations (BSs) and user equipment (UEs). Such kind of multi-view sensing problem can be naturally cast into a conditional generation framework. To this end, we design a bipartite neural network architecture, the first part of which uses an elaborately designed encoder to fuse the latent target features embedded in the multi-view CSI, and then the second uses them as conditioning inputs of a powerful generative model to guide the target's reconstruction. Specifically, the encoder is designed to capture the physical correlation between the CSI and the target, and also be adaptive to the numbers and positions of BS-UE pairs. Therein the view-specific nature of CSI is assimilated by introducing a spatial positional embedding scheme, which exploits the structure of electromagnetic(EM)-wave propagation channels. Finally, a conditional diffusion model with a weighted loss is employed to generate the target's point cloud from the fused features. Extensive numerical results demonstrate that the proposed generative multi-view (Gen-MV) sensing framework exhibits excellent flexibility and significant performance improvement on the reconstruction quality of target's shape and EM properties.

Congestion control (CC) crucially impacts user experience across Internet services like streaming, gaming, AR/VR, and connected cars. Traditionally, CC algorithm design seeks universal control rules that yield high performance across diverse application domains and networks. However, varying service needs and network conditions challenge this approach. We share operational experience with a system that automatically customizes congestion control logic to service needs and network conditions. We discuss design, deployment challenges, and solutions, highlighting performance benefits through case studies in streaming, gaming, connected cars, and more. Our system leverages PCC Vivace, an online-learning based congestion control protocol developed by researchers. Hence, along with insights from customizing congestion control, we also discuss lessons learned and modifications made to adapt PCC Vivace for real-world deployment.

--The pursuit of rate maximization in wireless communication frequently encounters substantial challenges associated with user fairness. The proposed approach incorporates not only channel state information but also factors such as packet delay, the number of backlogged packets, and the number of transmitted packets into the components of the state information. We adopt a centralized RL method, where a central controller collects and processes the state information. The central controller functions as an agent trained using the proximal policy optimization (PPO) algorithm. T o better utilize topology information in the communication network and enhance the generalization of the proposed method, we embed GNN layers into both the actor and critic networks of the PPO algorithm. This integration allows for efficient parameter updates of GNNs and enables the state information to be pa-rameterized as a low-dimensional embedding, which is leveraged by the agent to optimize power allocation strategies. Simulation results demonstrate that the proposed method effectively reduces average delay while ensuring user fairness, outperforms baseline methods, and exhibits scalability and generalization capability. EVICE-TO-DEVICE (D2D) communication, which enables the direct data exchange between devices without the involvement of base stations or relay devices, can occur both within and independently of cellular network coverage [1]. This communication mode is particularly significant in 5G networks due to its potential to enhance communication efficiency, reduce delay, and increase network capacity [2]. Hao Fang, Kai Huang, Xiao Li, and Shi Jin are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (e-mail: fhao seu@seu.edu.cn; Chongtao Guo is with the College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China (e-mail: ct-guo@szu.edu.cn). Le Liang is with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China, and also with Purple Mountain Laboratories, Nanjing 211111, China (e-mail: lliang@seu.edu.cn). Hao Y e is with the Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA 95064, USA (e-mail: yehao@ucsc.edu). These include scenarios like autonomous driving, holographic communication, and extended reality, which impose extremely stringent reliability and delay requirements.

The 6G mobile network is the next evolutionary step after 5G, with a prediction of an explosive surge in mobile traffic. It provides ultra-low latency, higher data rates, high device density, and ubiquitous coverage, positively impacting services in various areas. Energy saving is a major concern for new systems in the telecommunications sector because all players are expected to reduce their carbon footprints to contribute to mitigating climate change. Network slicing is a fundamental enabler for 6G/5G mobile networks and various other new systems, such as the Internet of Things (IoT), Internet of Vehicles (IoV), and Industrial IoT (IIoT). However, energy-saving methods embedded in network slicing architectures are still a research gap. This paper discusses how to embed energy-saving methods in network-slicing architectures that are a fundamental enabler for nearly all new innovative systems being deployed worldwide. This paper's main contribution is a proposal to save energy in network slicing. That is achieved by deploying ML-native agents in NS architectures to dynamically orchestrate and optimize resources based on user demands. The SFI2 network slicing reference architecture is the concrete use case scenario in which contrastive learning improves energy saving for resource allocation.

This thesis delves into the forefront of wireless communication by exploring the synergistic integration of three transformative technologies: STAR-RIS, CoMP, and NOMA. Driven by the ever-increasing demand for higher data rates, improved spectral efficiency, and expanded coverage in the evolving landscape of 6G development, this research investigates the potential of these technologies to revolutionize future wireless networks. The thesis analyzes the performance gains achievable through strategic deployment of STAR-RIS, focusing on mitigating inter-cell interference, enhancing signal strength, and extending coverage to cell-edge users. Resource sharing strategies for STAR-RIS elements are explored, optimizing both transmission and reflection functionalities. Analytical frameworks are developed to quantify the benefits of STAR-RIS assisted CoMP-NOMA networks under realistic channel conditions, deriving key performance metrics such as ergodic rates and outage probabilities. Additionally, the research delves into energy-efficient design approaches for CoMP-NOMA networks incorporating RIS, proposing novel RIS configurations and optimization algorithms to achieve a balance between performance and energy consumption. Furthermore, the application of Deep Reinforcement Learning (DRL) techniques for intelligent and adaptive optimization in aerial RIS-assisted CoMP-NOMA networks is explored, aiming to maximize network sum rate while meeting user quality of service requirements. Through a comprehensive investigation of these technologies and their synergistic potential, this thesis contributes valuable insights into the future of wireless communication, paving the way for the development of more efficient, reliable, and sustainable networks capable of meeting the demands of our increasingly connected world.

--Nowadays, Generative AI (GenAI) reshapes numerous domains by enabling machines to create content across modalities. As GenAI evolves into autonomous agents capable of reasoning, collaboration, and interaction, they are increasingly deployed on network infrastructures to serve humans automatically. This emerging paradigm, known as the agentic network, presents new optimization challenges due to the demand to incorporate subjective intents of human users expressed in natural language. Traditional generic Deep Reinforcement Learning (DRL) struggles to capture intent semantics and adjust policies dynamically, thus leading to suboptimality. First, we propose Intent-oriented Knowledge Distillation (IoKD), which efficiently distills intent-understanding capabilities from resource-intensive LAMs to lightweight edge LAMs (E-LAMs) to serve end users. Second, we develop Symbiotic Reinforcement Learning (SRL), integrating E-LAMs with a policy-based DRL framework. In SRL, E-LAMs translate natural language user intents into structured preference vectors that guide both state representation and reward design. The DRL, in turn, optimizes the generative service function chain composition and E-LAM selection based on real-time network conditions, thus optimizing the subjective Quality-of-Experience (QoE). Extensive experiments conducted in an agentic network with 81 agents demonstrate that IoKD reduces mean squared error in intent prediction by up to 22.5%, while SRL outperforms conventional generic DRL by up to 23.5% in maximizing intent-aware QoE. Generative AI (GenAI) has revolutionized the technological landscape, enabling machines to create content across multiple modalities, including text, images, and videos [1]. Moreover, GenAI is rapidly evolving from basic content generation to complex reasoning and decision-making, transforming how machines interact with and serve humans. G. Sun is with the College of Computer Science and Technology, Jilin University, China, and also with the College of Computing and Data Science, Nanyang Technological University, Singapore (e-mail: sungeng@jlu.edu.cn).