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In this paper a novel joint sensing, communication, and artificialintelligence (AI) framework is proposed so as to optimize extended reality (XR)experiences over terahertz (THz) wireless systems. The proposed frameworkconsists of three main components. First, a tensor decomposition framework isproposed to extract unique sensing parameters for XR users and theirenvironment by exploiting then THz channel sparsity. Essentially, THz band'squasi-opticality is exploited and the sensing parameters are extracted from theuplink 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 theestimated sensing parameters. Second, a non-autoregressive multi-resolutiongenerative artificial intelligence (AI) framework integrated with anadversarial transformer is proposed to predict missing and future sensinginformation. The proposed framework offers robust and comprehensive historicalsensing information and anticipatory forecasts of future environmental changes,which are generalizable to fluctuations in both known and unforeseen userbehaviors and environmental conditions. Third, a multi-agent deep recurrenthysteretic Q-neural network is developed to control the handover policy ofreconfigurable intelligent surface (RIS) subarrays, leveraging the informativenature of sensing information to minimize handover cost, maximize theindividual quality of personal experiences (QoPEs), and improve the robustnessand resilience of THz links. Simulation results show a high generalizability ofthe proposed unsupervised generative AI framework to fluctuations in userbehavior and velocity, leading to a 61 % improvement in instantaneousreliability compared to schemes with known channel state information.

Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences