In this paper a novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems. The proposed framework consists of three main components. First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting then THz channel sparsity. Essentially, THz band's quasi-opticality is exploited and the sensing parameters are extracted from the uplink communication signal, thereby allowing for the use of the same waveform, spectrum, and hardware for both communication and sensing functionalities. Then, the Cramer-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters. Second, a non-autoregressive multi-resolution generative artificial intelligence (AI) framework integrated with an adversarial transformer is proposed to predict missing and future sensing information. The proposed framework offers robust and comprehensive historical sensing information and anticipatory forecasts of future environmental changes, which are generalizable to fluctuations in both known and unforeseen user behaviors and environmental conditions. Third, a multi-agent deep recurrent hysteretic Q-neural network is developed to control the handover policy of reconfigurable intelligent surface (RIS) subarrays, leveraging the informative nature of sensing information to minimize handover cost, maximize the individual quality of personal experiences (QoPEs), and improve the robustness and resilience of THz links. Simulation results show a high generalizability of the proposed unsupervised generative AI framework to fluctuations in user behavior and velocity, leading to a 61 % improvement in instantaneous reliability compared to schemes with known channel state information.

翻译：本文提出了一种新型的联合感测、通信与人工智能（AI）框架，旨在优化太赫兹（THz）无线系统上扩展现实（XR）的用户体验。该框架包含三个核心组成部分。首先，提出了一种张量分解框架，通过利用THz信道的稀疏性提取XR用户及其环境的独特感测参数。具体而言，利用太赫兹波段的准光学特性，从上行通信信号中提取感测参数，从而允许通信和感测功能共用相同的波形、频谱和硬件。同时，推导了克拉美-罗下界以评估估计感测参数的精度。其次，提出了一种结合对抗式Transformer的非自回归多分辨率生成式人工智能（AI）框架，用于预测缺失和未来的感测信息。该框架能够提供稳健且全面的历史感测信息以及环境变化的预期预测，且对已知和未知的用户行为及环境条件波动均具有泛化能力。第三，开发了一种多智能体深度递归滞后Q神经网络，用于控制可重构智能表面（RIS）子阵的切换策略，利用感测信息的丰富性最小化切换成本、最大化个人体验质量（QoPE），并提升太赫兹链路的稳健性和弹性。仿真结果表明，所提出的无监督生成式AI框架对用户行为及速度波动具有高度泛化性，与已知信道状态信息的方案相比，瞬时可靠性提升了61%。