In this paper, we investigate the problem of predictive beamforming design for tracking vehicles in an integrated sensing and communication (ISAC)-based near-field vehicle-toinfrastructure (V2I) system. The waveform design in near-field scenarios requires the joint consideration of both range and angle dimensions, posing new challenges to conventional beamforming and tracking strategies. To address this issue, we propose a predictive beamforming framework leveraging a large language model (LLM)-based neural network (LNN), which exploits historical channel state information (CSI) to facilitate accurate future beamforming decisions. Cramér–Rao bounds (CRBs) for angle and distance estimation, along with the achievable sum-rate, are applied as key metrics to evaluate the sensing and communication performance of the V2I system, respectively. Capitalizing on the derived performance metrics, we formulate the optimization problems aiming either to maximize the sum-rate subject to CRB constraints or to minimize the CRB while ensuring a required communication rate, thereby accommodating different design requirements. Moreover, to effectively capture the stochastic nature of vehicle driving behavior, the performance metrics are further expressed in expectation form over the distribution of possible driving states. Consequently, a data-driven optimization approach based on the LNN is adopted to handle the resulting intractable analytical expressions, and the underlying LNN is trained with task-specific loss functions. During the training process, low-rank adaptation (LoRA) is incorporated to fine-tune the pre-trained LLM, which significantly reduces the number of trainable parameters. Simulation results demonstrate that the proposed framework accurately predicts future vehicle kinematic parameters and effectively optimizes the power allocation across transmit links. As a result, it achieves superior and robust performance in both communication and sensing tasks, highlighting its potential as a vital solution for next-generation near-field V2I systems.

Semantic communication is increasingly recognized as a promising paradigm for enhancing the communication capabilities of wireless systems in the 6G era. Existing deep learning (DL)-based semantic methods typically enhance system robustness through module-centric strategies, where additional components are integrated into the model. Due to the significant computational overhead, these strategies are often unsuitable for resource-limited systems, such as the emerging low-altitude wireless networks (LAWNs). Moreover, most existing methods are optimized for fixed channel models and lack architectural adaptability across diverse environments, leading to repeated retraining and increased maintenance complexity. To address these challenges, we propose a novel Dual-Branch Architecture (DBA) for semantic communication. DBA employs a training-only auxiliary decoder branch to provide noise-free supervision for the main decoder, thereby enhancing robustness without adding deployment or inference overhead. Specifically, we introduce a contrastive learning mechanism to align the outputs of the noisy and noise-free branches, reinforcing semantic consistency. Building on this, we further propose the Extensible Multi-Channel Architecture (EMCA), a multi-branch design that incorporates multiple decoder branches optimized for different channel models and jointly trains them with a shared encoder and auxiliary branch, improving scalability without duplicating model parameters. Simulation results demonstrate that DBA and EMCA consistently outperform existing baselines in terms of semantic fidelity across additive white Gaussian noise (AWGN), Rayleigh, and Rician fading channels, without incurring any additional inference cost. Additionally, experiments conducted using a software-defined radio (SDR)-based platform with universal software radio peripheral (USRP) further validate the robustness and practicality of the proposed methods in practical environmental applications.

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, physical-layer system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S&C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution physical-layer ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.

This paper explores an orthogonal time frequency space (OTFS)-assisted integrated sensing and communication (ISAC) system in vehicular networks. We present a deep learning (DL)-based framework for the OTFS-assisted ISAC system, leveraging the advantages offered by the Delay-Doppler representation of the time-variant channel. The communication channel matrix is utilized within the framework to infer motion parameters, thereby enabling the establishment of an effective transmission protocol. Therefore, it is crucial to design a channel estimation method that simultaneously fulfills both sensing and communication performance requirements. To this end, a DL-based channel estimation approach is designed to obtain accurate channel state information (CSI), due to the powerful capability of neural networks [1]. Specifically, we model the channel estimation as a denoising problem from the embedded pilot scheme and employ a self-adaptive threshold submodule to eliminate irrelevant features. Finally, simulation results demonstrate that our proposed method can obtain accurate CSI with the available sensing performance.

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. Specifically, adversary is able to uniquely re-identify over 52~66 percent users in other anonymous mobility datasets and 60~80 percent users have more than 60 percent probability leaking unreported mobility records. In addition, we further demonstrate that the privacy sensitivity of check-in records can be more accurately measured by including the information of additional mobility data compared with only looking at check-ins. Based on this observation, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion-kτ,l-anonymity. It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24~57 percent while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.services.

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. To address this problem, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion - kt, l-anonymity. It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24% ~ 57% while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.