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

Christina Chaccour, Walid Saad, Merouane Debbah, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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. Within this framework, active reconfigurable intelligent surfaces (RISs) are incorporated as as pivotal elements, serving as enhanced base stations in the THz band to enhance Line-of-Sight (LoS) communication. The proposed framework consists of three main components. <italic>First</italic>, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting the THz channel sparsity. Essentially, THz band&#x2019;s quasi-opticality is exploited and the sensing parameters are extracted from the uplink communication signal, thereby allowing for the use of the <italic>same waveform, spectrum, and hardware for both communication and sensing functionalities</italic>. Then, the Cramer-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters. <italic>Second</italic>, 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 <italic>generalizable to fluctuations in both known and unforeseen user behaviors and environmental conditions</italic>. <italic>Third</italic>, a multi-agent deep recurrent hysteretic Q-neural network is developed to control the handover policy of 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.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Wireless Communications
StateAccepted/In press - 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • Artificial intelligence
  • Hardware
  • Sensors
  • Terahertz communications
  • Wireless communication
  • Wireless sensor networks
  • X reality
  • artificial intelligence (AI)
  • extended reality (XR)
  • joint sensing and communication
  • machine learning (ML)
  • reliability
  • resilience
  • terahertz (THz)


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