Large AI models have been widely adopted in wireless communications for channel modeling, beamforming, and resource optimization. However, most existing efforts remain limited to single-modality inputs and channel-specific objec- tives, overlooking the broader potential of large foundation models for unified wireless sensing. To bridge this gap, we propose MMSense, a multi-modal, multi-task foundation model that jointly addresses channel-centric, environment-aware, and human-centered sensing. Our framework integrates image, radar, LiDAR, and textual data by transforming them into vision- compatible representations, enabling effective cross-modal align- ment within a unified feature space. A modality gating mecha- nism adaptively fuses these representations, while a vision-based large language model backbone enables unified feature align- ment and instruction-driven task adaptation. Furthermore, task- specific sequential attention and uncertainty-based loss weighting mechanisms enhance cross-task generalization. Experiments on real wireless scenario datasets show that our approach outper- forms both task-specific and large-model baselines, confirming its strong generalization across heterogeneous sensing tasks.

Integrated sensing and communication systems face critical challenges, including limited bandwidth, power constraints, and varying communication conditions, which demand efficient data transmission and processing strategies. This paper introduces, ByteTrans, a novel joint optimization framework that integrates byte-level predictive modeling with adaptive model scheduling to maximize data transmission efficiency while adhering to communication and computational constraints. The proposed framework employs Transformer-based models to predict and compress data packets losslessly, leveraging the inherent redundancy in multi-modal network data. Such a unified data compression approach predicts occurring byte probabilities, encodes them as ranks using lossless entropy coding, and efficiently reduces data size and entropy across diverse modalities. Then, a dynamic adaptation strategy selects the optimal compression model based on packet characteristics and channel conditions, ensuring efficient operation across heterogeneous sensor environments. Experimental results validate that our scheme achieves compression rates exceeding 50%, while showcasing substantial reductions in communication time and bandwidth usage under both normal and adverse channel conditions. Furthermore, we effectively implement these models across various real-world edge sensors and servers, showcasing their practicality and efficiency in various network applications. By addressing the trade-offs between achieving lower compression ratios and limiting computational and energy consumption, this work establishes a scalable and robust solution for data management in multi-modal communication systems.

WiFi-based indoor positioning plays a crucial role in a variety of location-based services due to its widespread avail ability and cost-effectiveness. However, most existing indoor positioning systems predominantly utilize channel state information (CSI) to learn channel characteristics and apply fingerprinting for position estimation. Unfortunately, CSI can only be extracted from a limited set of commercial WiFi devices, hindering its widespread application in practice. In this work, we introduce BFMLoc, a novel indoor positioning framework that exploits the beamforming feedback matrix (BFM), which is readily available on commercial WiFi devices. Although BFM provides broader sensing coverage, it sacrifices detailed channel information due to the data compression applied to reduce feedback overhead. To address this limitation, we explore the feasibility of using BFM derivatives for indoor positioning and propose a U-net model to reconstruct the angle-delay profiles (ADP) from the compressed BFM data, thereby enhancing positioning accuracy. A Vision Transformer (ViT) model is then developed to extract spatial features from the predicted ADP maps to perform localization. Additionally, we design a model adaptation module based on transfer learning, integrated into the overall framework. This allows the positioning model to be easily deployed and adapted to various indoor environments with minimal retraining overhead. Extensive evaluations and validation on a digital twin testbed demonstrate that our framework achieves high positioning ac curacy and enhanced robustness compared to state-of-the-art methods.

WiFi-based indoor positioning plays a crucial role in a variety of location-based services due to its widespread availability and cost-effectiveness. However, most existing indoor positioning systems predominantly utilize channel state information (CSI) to learn channel characteristics and apply fingerprinting for position estimation. Unfortunately, CSI can only be extracted from a limited set of commercial WiFi devices, hindering its widespread application in practice. In this work, we introduce BFMLoc, a novel indoor positioning framework that exploits the beamforming feedback matrix (BFM), which is readily available on commercial WiFi devices. Although BFM provides broader sensing coverage, it sacrifices detailed channel information due to the compression applied to reduce feedback overhead. To address this limitation, we explore the feasibility of using BFM derivatives for indoor positioning and propose a U-net model to reconstruct the angle-delay profiles (ADP) from the compressed BFM data, thereby enhancing positioning accuracy. A Vision Transformer (ViT) model is then developed to extract spatial features from the predicted ADP maps to perform localization. Extensive evaluation results demonstrate that our framework achieves high positioning accuracy and improved robustness compared to state-of-the-art methods.

Network log data analysis plays a critical role in detecting security threats and operational anomalies. Traditional log analysis methods for anomaly detection and root cause analysis rely heavily on expert knowledge or fully supervised learning models, both of which require extensive labeled data and significant human effort. To address these challenges, we propose ALPHA, the first Active Learning Pipeline for Human-free log Analysis. ALPHA integrates semantic embedding, clustering-based representative sampling, and large language model (LLM)assisted few-shot annotation to automate the anomaly detection process. The LLM annotated labels are propagated across clusters, enabling large-scale training of an anomaly detector with minimal supervision. To enhance the annotation accuracy, we propose a two-step few-shot refinement strategy that adaptively selects informative prompts based on the LLM's observed error patterns. Extensive experiments11The source code of our proposed ALPHA framework is available at https://github.com/Xuanhao-Luo/ALPHA. on real-world log datasets demonstrate that ALPHA achieves detection accuracy comparable to fully supervised methods while mitigating human efforts in the loop. ALPHA also supports interpretable analysis through LLM-driven root cause explanations in the post-detection stage. These capabilities make ALPHA a scalable and cost-efficient solution for truly automated log-based anomaly detection.

The rapid growth in low-Earth-orbit satellites enables providing Earth observation applications to public users via a shared platform. However, the limited satellite-ground communication resources present a major challenge in downloading and fully utilizing satellite-captured Earth observation data on the ground. As a new edge computing paradigm, orbital edge computing allows satellites to host deep learning models with on-board computing resources for in-orbit data analysis, reducing downlink data volume and response time. However, the limited generalizability of in-orbit models and data distribution shifts across geographical locations severely impact the accuracy of in-orbit analytics. In this work, we design a framework, AdaOrb, which dynamically schedules online model retraining for location-specific Earth observation tasks. Scheduling decisions are made with a model predictive control-based algorithm that allocates limited satellite downlink capacity among onboard tasks to download model retraining data. By developing and using a hardware-in-the-loop orbital edge computing testbed, we show that our method achieves superior overall accuracy of in-orbit analytics tasks compared to alternative methods.

The rapid increase in networked systems and data transmission requires advanced data compression solutions to optimize bandwidth utilization and enhance network performance. This study introduces a novel byte-level predictive model using Transformer architecture, capable of handling the redundancy and diversity of data types in network traffic as byte sequences. Unlike traditional methods that require separate compressors for different data types, this unified approach sets new benchmarks and simplifies predictive modeling across various data modalities such as video, audio, images, and text, by processing them at the byte level. This is achieved by predicting subsequent byte probability distributions, encoding them into a sparse rank sequence using lossless entropy coding, and significantly reducing both data size and entropy. Experimental results1show that our model achieves compression ratios below 50%, while offering models of various sizes tailored for different communication devices. Additionally, we successfully deploy these models on a range of edge devices and servers, demonstrating their practical applicability and effectiveness in real-world network scenarios. This approach significantly enhances data throughput and reduces bandwidth demands, making it particularly valuable in resource-constrained environments like the Internet of Things sensor networks.

Due to the exponential increase in wireless devices and a diversification of network services, unprecedented challenges, such as managing heterogeneous data traffic and massive access demands, have arisen in next-generation wireless networks. To address these challenges, there is a pressing need for the evolution of multiple access schemes with advanced transceivers. Millimeter-wave (mmWave) communication emerges as a promising solution by offering substantial bandwidth and accommodating massive connectivities. Nevertheless, the inherent signaling directionality and susceptibility to blockages pose significant challenges for deploying multiple transceivers with narrow antenna beams. Consequently, beam management becomes imperative for practical network implementations to identify and track the optimal transceiver beam pairs, ensuring maximum received power and maintaining high-quality access service. In this context, we propose a Contextual Combinatorial Beam Management (CCBM) framework tailored for mmWave wireless networks. By leveraging advanced online probing techniques and integrating predicted contextual information, such as dynamic link qualities in spatial-temporal domain, CCBM aims to jointly optimize transceiver pairing and beam selection while balancing the network load. This approach not only facilitates multiple access effectively but also enhances bandwidth utilization and reduces computational overheads for real-time applications. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions.

Millimeter-wave (mmWave) communication, a cor-nerstone in the evolution of next-generation wireless networks, offers substantial bandwidth and plays a crucial role in advancing wireless connectivity capabilities. Nevertheless, the inherent directionality and susceptibility to blockages pose significant challenges for a cost-effective beam management in densely deployed networks. This paper presents a Contextual Combina-torial Beam Management (CCBM) framework, leveraging both location-aware link qualities and beam correlation to tackle the joint access point (AP) and beam selection problem in mmWave networks, with a specific focus on mitigating coordination overhead and balancing the load across APs. Built upon a formulated multi-armed bandit problem, CCBM significantly reduces the uncertainty during online probing process by employing early stopping and attention-based selection mechanisms. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions.

Abstract One of the most important problems in traffic safety is providing effective collision warnings in intersection areas. In this paper, we propose a Clothoid Curve-based Intersection Collision Warning scheme (CICW) in the Internet of Vehicles. In CICW, we first present a clothoid curve-based vehicle trajectory prediction model. In this model, vehicles can establish the trajectory prediction equations by themselves. Each vehicle solves the equations based on its internal state information, electronic map, GPS data and neighbour vehicles’ state information derived from periodical beacons. The vehicle then predicates the crossing points of the predicted trajectory between itself and the neighbour vehicles. Based on the reference points, it further obtains the earliest possible collision location and then issues a warning. Extensive simulation results show that the performance of the proposed scheme achieves higher collision warning accuracy and a lower error warning ratio compared to existing schemes.