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

TL;DR

This paper introduces a comprehensive framework combining sensing, communication, and AI to enhance THz wireless XR experiences, utilizing tensor decomposition, generative AI, and deep reinforcement learning for improved reliability and resilience.

Contribution

It presents a novel integrated approach that exploits THz channel sparsity, employs advanced AI for environmental prediction, and optimizes handover policies, advancing the state-of-the-art in THz XR systems.

Findings

Achieved 61% improvement in instantaneous reliability.

Demonstrated robustness of AI predictions against environmental fluctuations.

Validated the effectiveness of the integrated framework through simulations.

Abstract

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.