AHCI RESEARCH GROUP

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2024

Chaccour, C.; Saad, W.; Debbah, M.; Poor, H. V.

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

In: IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11444–11460, 2024, ISSN: 15361276 (ISSN).

Abstract | Links | BibTeX | Tags: Artificial intelligence, artificial intelligence (AI), Behavioral Research, Channel state information, Computer hardware, Cramer-Rao bounds, Extended reality (XR), Hardware, Joint sensing and communication, Learning systems, machine learning, machine learning (ML), Machine-learning, Multi agent systems, reliability, Resilience, Sensor data fusion, Tera Hertz, Terahertz, terahertz (THz), Terahertz communication, Wireless communications, Wireless sensor networks, X reality

@article{chaccour_joint_2024,

title = {Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences},

author = {C. Chaccour and W. Saad and M. Debbah and H. V. Poor},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190170739&doi=10.1109%2fTWC.2024.3382192&partnerID=40&md5=da12c6f31faacaa08118b26e4570843f},

doi = {10.1109/TWC.2024.3382192},

issn = {15361276 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Transactions on Wireless Communications},

volume = {23},

number = {9},

pages = {11444–11460},

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. Within this framework, active reconfigurable intelligent surfaces (RISs) are incorporated 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. First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting the THz channel sparsity. Essentially, the 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 Cramér-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters. Second, a non-autoregressive multi-resolution generative 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 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 artificial intelligence (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. © 2002-2012 IEEE.},

keywords = {Artificial intelligence, artificial intelligence (AI), Behavioral Research, Channel state information, Computer hardware, Cramer-Rao bounds, Extended reality (XR), Hardware, Joint sensing and communication, Learning systems, machine learning, machine learning (ML), Machine-learning, Multi agent systems, reliability, Resilience, Sensor data fusion, Tera Hertz, Terahertz, terahertz (THz), Terahertz communication, Wireless communications, Wireless sensor networks, X reality},

pubstate = {published},

tppubtype = {article}

}

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 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. First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting the THz channel sparsity. Essentially, the 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 Cramér-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters. Second, a non-autoregressive multi-resolution generative 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 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 artificial intelligence (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. © 2002-2012 IEEE.