Edge intelligence has emerged as a promising strategy to deliver low-latency and ubiquitous services for mobile devices. Recent advances in fine-tuning mechanisms of foundation models have enabled edge intelligence by integrating low-rank adaptation (LoRA) with federated learning. However, in wireless networks, the device heterogeneity and resource constraints on edge devices pose great threats to the performance of federated fine-tuning. To tackle these issues, we propose to optimize federated fine-tuning in heterogenous wireless networks via online learning. First, the framework of switching-based federated fine-tuning in wireless networks is provided. The edge devices switches to LoRA modules dynamically for federated fine-tuning with base station to jointly mitigate the impact of device heterogeneity and transmission unreliability. Second, a tractable upper bound on the inference risk gap is derived based on theoretical analysis. To improve the generalization capability, we formulate a non-convex mixed-integer programming problem with long-term constraints, and decouple it into model switching, transmit power control, and bandwidth allocation subproblems. An online optimization algorithm is developed to solve the problems with polynomial computational complexity. Finally, the simulation results on the SST-2 and QNLI data sets demonstrate the performance gains in test accuracy and energy efficiency.

Qualcomm just announced the Snapdragon X2 Elite and X2 Elite Extreme, anticipated sequels to its game-changing PC chips. Qualcomm has announced its next generation of PC processors: the Snapdragon X2. It might not sound very exciting, but these chips continue to bring Windows laptops up to par with Apple Silicon-powered MacBooks . The company took the PC world by storm last year with its Snapdragon X chips, breaking the long-held duopoly of Intel and AMD powering most Windows laptops. Nearly every major laptop manufacturer was on board, launching a bevy of Qualcomm-powered laptops.

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Networked tethered flying platforms' superior monitoring coverage compared to ground-based cameras make them well-suited for surveillance and long-range detection. Large-scale events, such as the Hajj, constitute complex logistical events that attract approximately two to three million pilgrims annually to Makkah, Saudi Arabia. The immense scale of these gatherings presents significant challenges related to crowd management, infrastructure, public safety, and connectivity which can adversely impact both the pilgrimage experience and their well-being. The Hajj presents significant challenges in mobility, security, and connectivity, impacting the pilgrimage experience and requiring improved solutions.

In this study, we evaluate the performance of classical and quantum-inspired sequential models in forecasting univariate time series of incoming SMS activity (SMS-in) using the Milan Telecommunication Activity Dataset. Due to data completeness limitations, we focus exclusively on the SMS-in signal for each spatial grid cell. We compare five models, LSTM (baseline), Quantum LSTM (QLSTM), Quantum Adaptive Self-Attention (QASA), Quantum Receptance Weighted Key-Value (QRWKV), and Quantum Fast Weight Programmers (QFWP), under varying input sequence lengths (4, 8, 12, 16, 32 and 64). All models are trained to predict the next 10-minute SMS-in value based solely on historical values within a given sequence window. Our findings indicate that different models exhibit varying sensitivities to sequence length, suggesting that quantum enhancements are not universally advantageous. Rather, the effectiveness of quantum modules is highly dependent on the specific task and architectural design, reflecting inherent trade-offs among model size, parameterization strategies, and temporal modeling capabilities.

With the rapid development of mobile communication technologies, future mobile networks will offer vast services and resources for commuting, production, daily life, and entertainment. Accurate and efficient forecasting of mobile data (e.g., cell traffic, user behavior, channel quality) helps operators monitor network state changes, orchestrate wireless resources, and schedule infrastructure and users, thereby improving supply efficiency and service quality. However, current forecasting paradigms rely on customized designs with tailored models for exclusive data types. Such approaches increase complexity and deployment costs under large-scale, heterogeneous networks involving base stations, users, and channels. In this paper, we design a foundation model for mobile data forecasting, MobiGPT, with a unified structure capable of forecasting three data types: base station traffic, user app usage, and channel quality. We propose a soft-prompt learning method to help the model understand features of different data types, and introduce a temporal masking mechanism to guide the model through three forecasting tasks: short-term prediction, long-term prediction, and distribution generation, supporting diverse optimization scenarios. Evaluations on real-world datasets with over 100,000 samples show that MobiGPT achieves accurate multi-type forecasting. Compared to existing models, it improves forecasting accuracy by 27.37%, 20.08%, and 7.27%, reflecting strong generalization. Moreover, MobiGPT exhibits superior zero/few-shot performance in unseen scenarios, with over 21.51% improvement, validating its strong transferability as a foundation model.

Accurate and efficient traffic classification is vital for wireless network management, especially under encrypted payloads and dynamic application behavior, where traditional methods such as port-based identification and deep packet inspection (DPI) are increasingly inadequate. This work explores the feasibility of using physical channel data collected from the air interface of 5G Standalone (SA) networks for traffic sensing. We develop a preprocessing pipeline to transform raw channel records into structured representations with customized feature engineering to enhance downstream classification performance. To jointly capture temporal dependencies and both local and global structural patterns inherent in physical channel records, we propose a novel hybrid architecture: BiLSTM-Conformer Network (BiLCNet), which integrates the sequential modeling capability of Bidirectional Long Short-Term Memory networks (BiLSTM) with the spatial feature extraction strength of Conformer blocks. Evaluated on a noise-limited 5G SA dataset, our model achieves a classification accuracy of 93.9%, outperforming a series of conventional machine learning and deep learning algorithms. Furthermore, we demonstrate its generalization ability under zero-shot transfer settings, validating its robustness across traffic categories and varying environmental conditions.

Network failure diagnosis is challenging yet critical for high-performance computing (HPC) systems. Existing methods cannot be directly applied to HPC scenarios due to data heterogeneity and lack of accuracy. This paper proposes a novel framework, called ClusterRCA, to localize culprit nodes and determine failure types by leveraging multimodal data. ClusterRCA extracts features from topologically connected network interface controller (NIC) pairs to analyze the diverse, multimodal data in HPC systems. To accurately localize culprit nodes and determine failure types, ClusterRCA combines classifier-based and graph-based approaches. A failure graph is constructed based on the output of the state classifier, and then it performs a customized random walk on the graph to localize the root cause. Experiments on datasets collected by a top-tier global HPC device vendor show ClusterRCA achieves high accuracy in diagnosing network failure for HPC systems. ClusterRCA also maintains robust performance across different application scenarios.

Abstract--This paper proposes a supervised training algorithm for learning stochastic resource allocation policies with generative diffusion models (GDMs). We formulate the allocation problem as the maximization of an ergodic utility function subject to ergodic Quality of Service (QoS) constraints. Given samples from a stochastic expert policy that yields a near-optimal solution to the constrained optimization problem, we train a GDM policy to imitate the expert and generate new samples from the optimal distribution. We achieve near-optimal performance through the sequential execution of the generated samples. T o enable generalization to a family of network configurations, we parameterize the backward diffusion process with a graph neural network (GNN) architecture.

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. A look at the Arab world's networking research into intelligent wireless connectivity and intelligent secure networking systems. The past decade has witnessed exponential growth in wireless networks, accompanied by increasing demands for higher data speeds and broader connectivity. As user expectations rise, the existing network infrastructure faces significant challenges related to resource limitations, connectivity quality, and spectrum congestion. These issues have led to performance degradation and have necessitated innovative solutions to ensure sustainable network growth.

Generative Artificial Intelligence (GenAI) has made significant advancements in fields such as computer vision (CV) and natural language processing (NLP), demonstrating its capability to synthesize high-fidelity data and improve generalization. Recently, there has been growing interest in integrating GenAI into wireless sensing systems. By leveraging generative techniques such as data augmentation, domain adaptation, and denoising, wireless sensing applications, including device localization, human activity recognition, and environmental monitoring, can be significantly improved. This survey investigates the convergence of GenAI and wireless sensing from two complementary perspectives. First, we explore how GenAI can be integrated into wireless sensing pipelines, focusing on two modes of integration: as a plugin to augment task-specific models and as a solver to directly address sensing tasks. Second, we analyze the characteristics of mainstream generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, and discuss their applicability and unique advantages across various wireless sensing tasks. We further identify key challenges in applying GenAI to wireless sensing and outline a future direction toward a wireless foundation model: a unified, pre-trained design capable of scalable, adaptable, and efficient signal understanding across diverse sensing tasks.