Due to the availability of wide bandwidths in the frequency range 2 (FR2) band, sixth-generation (6G) networks can provide both radar and communication services using shared spectrum and hardware, reducing costs and enhancing efficiency via integrated sensing and communication (ISAC) systems. Motivated by the advantages of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios, this paper proposes an iterative hybrid precoder design method and a radar tracking algorithm for OTFS-ISAC systems with aerial targets, i.e., drones. Information is sent to the ground user through communication links using the subarray hybrid precoder to balance radar and communication performance through a weighted summinimization framework. The cubature Kalman filter (CKF) is employed for radar prediction and tracking. Simulation results indicate that the proposed hybrid precoder design algorithm effectively balances radar and communication performance, while the CKF-based radar tracking method outperforms other nonlinear Kalman filters.

Deep learning (DL)-based models have been widely applied in wireless communication systems with excellent performance. However, most of these models are task- and scenario-specific, exhibiting limited generalization and contributing to increasing complexity and overhead with their deployment in systems. Inspired by the emergent capabilities and strong generalization exhibited by large models (LMs), represented by large language models (LLMs), this paper analyzes the differences between existing DL-based wireless communication models and LLMs, proposing a framework for designing LMs tailored to wireless communications. Building upon this framework, we integrate channel-related tasks of the physical layer into a unified pre-training task, i.e., channel completion, and propose a pre-trained masked channel model (MCM) with different parameter scales ranging from 5 million to 1 billion (B), enabling simultaneous solving of channel state information (CSI) feedback, prediction, and estimation. Additionally, scaling laws on these downstream tasks are derived to guide the design and deployment of MCMs. The formulated scaling laws indicate that the proposed MCM with 1B parameter not only shows no sign of performance saturation on the pre-trained task but also has the potential to enhance performance at larger model sizes. Simulation results demonstrate that the proposed MCM outperforms the existing algorithms across various downstream tasks while exhibiting superior cross-task and cross-scenario generalization capabilities in both simulated and realistic scenarios.

Research on sixth-generation (6G) wireless networks has gained significant attention as wireless communications technologies advance. In the upcoming 6G era, artificial intelligence (AI) is expected to play a significant role in enhancing mobile communications. In particular, the application of AI techniques in channel estimation can enable accurate channel state information, even in dynamic scenarios. However, the limited computational resources in user equipment often prevent the deployment of complex algorithms, necessitating adaptive channel estimation solutions, balancing the accuracy and complexity dynamically. Conventionally, AI-based channel estimation algorithms rely on explicitly stacking deep learning (DL) layers/blocks, making adaptation challenging. This paper proposes an adaptive Implicit DL Channel Estimation Network (ICENet) that employs a lightweight, implicit network design to achieve dynamic adaptability. Numerical results show that our approach can achieve the trade-off between algorithm complexity and channel estimation accuracy by adapting based on channel quality. Additionally, it offers reduced memory cost compared to explicit layer/block-stacked networks while maintaining or surpassing their estimation accuracy. Furthermore, we analyze key factors influencing forward and backward propagations in ICENet and regularize the Jacobian matrix to ensure stable convergence during the training process.

In the era of upcoming sixth-generation (6G) wireless systems, the intelligent integrated sensing and communication (ISAC) paradigm has emerged as a pivotal research domain, catalyzing advancement across a wide range of applications. In this paper, we investigate an ISAC-assisted anti-eavesdropping communication system, where an ISAC ground base station exploits its radar function to track potential aerial eavesdroppers and implements predictive beamforming to ensure secure communications with multiple ground users. We harness the powerful capability of the Transformer for time series prediction to establish a novel deep neural network, termed the ISACformer, for constructing predictive beamformers via exploiting previously estimated channel state information in an unsupervised manner. By eliminating the need for explicit channel prediction, our proposed framework effectively reduces signaling overhead and complexity. In addition, by formulating a weighted objective function, our design meticulously balances the trade-off between the ergodic achievable worst-case secrecy rate for ground users and the ergodic Cramér-Rao lower bound for the kinematic parameters of potential aerial eavesdroppers. Simulation results demonstrate that the proposed ISACformer can deliver the desired predictive beamforming for harmonizing radar and communication functionalities effectively. Moreover, our method achieves performance approaching the theoretical upper bound obtained by ignoring multi-user interference, thereby highlighting the robustness of the proposed approach.

We present a multi-cloud based storage architecture that can effectively protect users' privacy and data security by implementing the concept of Oblivious RAM in a logical layer. It allows users to conceal the reading/writing operation types and access sequences to remote cloud storage in order to prevent the leakage of access patterns, which is a considerable threat to data security. We also use an anonymity preserving mechanism to make it more difficult to track users' data or confirm users' identities. Theoretical analysis and experimental results show that our architecture provides a secure and anonymous way to use untrusted cloud storages with high performance and reliability.