Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication networks. It supports governmental efforts, particularly those led by the international telecommunication union (ITU), to establish fair spectrum allocation policies, improve auction mechanisms, and meet the requirements of emerging technologies such as advanced 5G, forthcoming 6G, and the internet of things (IoT). This paper presents an effective spatio-temporal prediction framework that leverages crowdsourced user-side key performance indicators (KPIs) and regulatory datasets to model and forecast spectrum demand. The proposed methodology achieves superior prediction accuracy and cross-regional generalizability by incorporating advanced feature engineering, comprehensive correlation analysis, and transfer learning techniques. Unlike traditional ITU models, which are often constrained by arbitrary inputs and unrealistic assumptions, this approach exploits granular, data-driven insights to account for spatial and temporal variations in spectrum utilization. Comparative evaluations against ITU estimates, as the benchmark, underscore our framework's capability to deliver more realistic and actionable predictions. Experimental results validate the efficacy of our methodology, highlighting its potential as a robust approach for policymakers and regulatory bodies to enhance spectrum management and planning.

Accurate environment mapping is an important computing task for a wide range of smart city applications, including autonomous navigation, wireless network operations and extended reality environments. On the one hand, conventional smart city mapping techniques, such as satellite imagery, LiDAR scans, and manual annotations, often su ff er from limitations related to cost, accessibility and accuracy. On the other hand, open-source mapping platforms, such as OpenStreetMap, have been widely utilized in artificial intelligence (AI) applications for environment mapping, serving as a source of ground truth. However, human errors and the evolving nature of real-world environments introduce biases that can negatively impact the performance of neural networks trained on such data. In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations. Unlike prior methods, our approach leverages a vision transformer-based architecture to jointly process both RF and map modalities within a unified framework, e ffectively capturing spatial dependencies and structural priors for enhanced mapping accuracy. For the evaluation purposes, we employ a synthetic dataset co-produced by Huawei. To address the challenges associated with real-world data imperfections, we introduce controlled noise to its RF data so as to simulate real-world conditions. Additionally, we develop and train a model that leverages only aggregated path loss information to tackle the mapping problem. We measure the results according to three performance metrics which capture di fferent qualities: (i) The Jaccard index, also known as intersection over union (IoU), (ii) the Hausdor ff distance, and (iii) the Chamfer distance. Our design achieves a macro IoU of 65.3%, significantly surpassing (i) the erroneous maps baseline, which yields 40.1%, (ii) an RF-only method from the literature, which yields 37.3%, and (iii) a non-AI fusion baseline that we designed which yields 42.2%. The comparative evaluation highlights the limitations of relying solely on RF data or on spatial data, as well as the e ff ectiveness that AI can have on fusing data towards enhancing smart city mapping accuracy. Introduction Smart cities, characterized by their pervasive integration of digital technologies [8] and interconnected systems [6], face unique challenges in accurately capturing and updating the physical and dynamic characteristics of urban spaces.

The advent of large-scale Low Earth Orbit (LEO) satellite constellations, spearheaded by initiatives such as SpaceX's Starlink, Amazon's Project Kuiper, and OneWeb, is poised to revolutionize global connectivity Saeed et al. (2020). By deploying thousands of interconnected satellites, these networks promise to deliver high-speed, low-latency internet access to every corner of the globe, including remote and underserved regions Reddy et al. (2023). However, the very characteristics that enable this new paradigm - namely, the massive scale and high orbital velocity of the satellites - introduce unprecedented challenges in network management Hu (2023). The network topology is in a constant state of flux, with inter-satellite links (ISLs) being established and terminated on a timescale of seconds, creating a highly dynamic and complex operational environment Bhattacharjee et al. (2024). At the heart of managing these constellations lies the network routing problem: determining the optimal path for data packets to travel from a source to a destination Zhang et al. (2025); Chen et al. (2021). In this dynamic context, the routing problem is far more complex than in terrestrial networks. It must account for time-varying latencies, intermittent link availability, and vast state spaces.

--With the growing complexity and dynamics of the mobile communication networks, accurately predicting key system parameters, such as channel state information (CSI), user location, and network traffic, has become essential for a wide range of physical (PHY)-layer and medium access control (MAC)-layer tasks. Although traditional deep learning (DL)- based methods have been widely applied to such prediction tasks, they often struggle to generalize across different scenarios and tasks. In response, we propose a unified foundation model for multi-task prediction in wireless networks that supports diverse prediction intervals. The proposed model enforces univariate decomposition to unify heterogeneous tasks, encodes granularity for interval awareness, and uses a causal Transformer backbone for accurate predictions. Additionally, we introduce a patch masking strategy during training to support arbitrary input lengths. After trained on large-scale datasets, the proposed foundation model demonstrates strong generalization to unseen scenarios and achieves zero-shot performance on new tasks that surpass traditional full-shot baselines. HE advent of 6G communications [1] has made wireless systems more intricate, featuring ultra-dense deployments, diverse service demands, and highly dynamic environments. Efficient execution of physical (PHY) and medium access control (MAC)-layer tasks require accurate and timely knowledge of the surrounding communication environment. Key parameters of interest include channel state information (CSI) [2], user locations [3], mobile traffic at the base station (BS) [4], etc. However, these parameters fluctuate rapidly over time, making real-time estimation and feedback particularly challenging. As a result, accurately predicting these variables has become essential for enabling a wide range of downstream communication tasks.

The integration of satellite communication into mobile devices represents a paradigm shift in connectivity, yet the performance characteristics under motion and environmental occlusion remain poorly understood. We present the Starlink Robot, the first mobile robotic platform equipped with Starlink satellite internet, comprehensive sensor suite including upward-facing camera, LiDAR, and IMU, designed to systematically study satellite communication performance during movement. Our multi-modal dataset captures synchronized communication metrics, motion dynamics, sky visibility, and 3D environmental context across diverse scenarios including steady-state motion, variable speeds, and different occlusion conditions. This platform and dataset enable researchers to develop motion-aware communication protocols, predict connectivity disruptions, and optimize satellite communication for emerging mobile applications from smartphones to autonomous vehicles. In this work, we use LEOViz for real-time satellite tracking and data collection. The starlink robot project is available at https://github.com/StarlinkRobot.

--In the user-centric cell-free massive MIMO (UC-mMIMO) network scheme, user mobility necessitates updating the set of serving access points to maintain the user-centric clustering. Such updates are typically performed through handoff (HO) operations; however, frequent HOs lead to overheads associated with the allocation and release of resources. This paper presents a deep reinforcement learning (DRL)-based solution to predict and manage these connections for mobile users. Our solution employs the Soft Actor-Critic algorithm, with continuous action space representation, to train a deep neural network to serve as the HO policy. We present a novel proposition for a reward function that integrates a HO penalty in order to balance the attainable rate and the associated overhead related to HOs. We develop two variants of our system; the first one uses mobility direction-assisted (DA) observations that are based on the user movement pattern, while the second one uses history-assisted (HA) observations that are based on the history of the large-scale fading (LSF). Simulation results show that our DRL-based continuous action space approach is more scalable than discrete space counterpart, and that our derived HO policy automatically learns to gather HOs in specific time slots to minimize the overhead of initiating HOs. Our solution can also operate in real time with a response time less than 0 . Index T erms --Mobility, handoff, handover, user-centric, cell-free massive MIMO, distributed MIMO, deep-reinforcement learning, soft actor critic, machine learning, channel aging. User-centric cell-free massive MIMO (UC-mMIMO) is a wireless network architecture where each user is served by a custom group of neighboring access points (APs) which are connected to a central unit (CU) via fronthaul links [1]. Unlike the current cellular system that is based on macro base stations, UC-mMIMO deploys cooperative APs that jointly serve users without relying on a traditional cellular boundaries. UC-mMIMO helps to achieve reliable wireless connectivity and provides uniform performance throughout the network [1], [2]. However, this beyond-5G mobile wireless network architecture introduces the key challenge of determining the connections between the APs and the users when moving through the network [3].

Advanced intelligent automation becomes an important feature to deal with the increased complexity in managing wireless networks. This paper proposes a novel automation approach of intent-based network for Radio Access Networks (RANs) management by leveraging Large Language Models (LLMs). The proposed method enhances intent translation, autonomously interpreting high-level objectives, reasoning over complex network states, and generating precise configurations of the RAN by integrating LLMs within an agentic architecture. We propose a structured prompt engineering technique and demonstrate that the network can automatically improve its energy efficiency by dynamically optimizing critical RAN parameters through a closed-loop mechanism. It showcases the potential to enable robust resource management in RAN by adapting strategies based on real-time feedback via LLM-orchestrated agentic systems.

The rise of Generative AI (GenAI) in recent years has catalyzed transformative advances in wireless communications and networks. Among the members of the GenAI family, Diffusion Models (DMs) have risen to prominence as a powerful option, capable of handling complex, high-dimensional data distribution, as well as consistent, noise-robust performance. In this survey, we aim to provide a comprehensive overview of the theoretical foundations and practical applications of DMs across future communication systems. We first provide an extensive tutorial of DMs and demonstrate how they can be applied to enhance optimizers, reinforcement learning and incentive mechanisms, which are popular approaches for problems in wireless networks. Then, we review and discuss the DM-based methods proposed for emerging issues in future networks and communications, including channel modeling and estimation, signal detection and data reconstruction, integrated sensing and communication, resource management in edge computing networks, semantic communications and other notable issues. We conclude the survey with highlighting technical limitations of DMs and their applications, as well as discussing future research directions.

--Emerging real-time computer vision (CV) applications on wireless edge devices demand energy-efficient and privacy-preserving learning. Federated learning (FL) enables on-device training without raw data sharing, yet remains challenging in resource-constrained environments due to energy-intensive computation and communication, as well as limited and non-i.i.d. We propose FedDPQ, an ultra energy-efficient FL framework for real-time CV over unreliable wireless networks. FedDPQ integrates diffusion-based data augmentation, model pruning, communication quantization, and transmission power control to enhance training efficiency. It expands local datasets using synthetic data, reduces computation through pruning, compresses updates via quantization, and mitigates transmission outages with adaptive power control. We further derive a closed-form energy-convergence model capturing the coupled impact of these components, and develop a Bayesian optimization(BO)- based algorithm to jointly tune data augmentation strategy, pruning ratio, quantization level, and power control. This work of Xiangwang Hou was supported by the National Natural Science Foundation of China under grant No. 623B2060. This work of Jingjing Wang was partly supported by the National Natural Science Foundation of China under Grant No. 62222101 and No. U24A20213, partly supported by the Beijing Natural Science Foundation under Grants No. L232043 and No. L222039, partly supported by the Natural Science Foundation of Zhejiang Province under Grant No. LMS25F010007 and partly supported by the Fundamental Research Funds for the Central Universities. This work of Jun Du was partly supported by the National Natural Science Foundation China under Grants No. 62422109 and No.U23A20281.

The hierarchical architecture of Open Radio Access Network (O-RAN) has enabled a new Federated Learning (FL) paradigm that trains models using data from non- and near-real-time (near-RT) Radio Intelligent Controllers (RICs). However, the ever-increasing model size leads to longer training time, jeopardizing the deadline requirements for both non-RT and near-RT RICs. To address this issue, split federated learning (SFL) offers an approach by offloading partial model layers from near-RT-RIC to high-performance non-RT-RIC. Nonetheless, its deployment presents two challenges: (i) Frequent data/gradient transfers between near-RT-RIC and non-RT-RIC in SFL incur significant communication cost in O-RAN. (ii) Proper allocation of computational and communication resources in O-RAN is vital to satisfying the deadline and affects SFL convergence. Therefore, we propose SplitMe, an SFL framework that exploits mutual learning to alternately and independently train the near-RT-RIC's model and the non-RT-RIC's inverse model, eliminating frequent transfers. The ''inverse'' of the inverse model is derived via a zeroth-order technique to integrate the final model. Then, we solve a joint optimization problem for SplitMe to minimize overall resource costs with deadline-aware selection of near-RT-RICs and adaptive local updates. Our numerical results demonstrate that SplitMe remarkably outperforms FL frameworks like SFL, FedAvg and O-RANFed regarding costs and convergence.