AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
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}
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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.
Du, B.; Du, H.; Liu, H.; Niyato, D.; Xin, P.; Yu, J.; Qi, M.; Tang, Y.
YOLO-Based Semantic Communication with Generative AI-Aided Resource Allocation for Digital Twins Construction Journal Article
In: IEEE Internet of Things Journal, vol. 11, no. 5, pp. 7664–7678, 2024, ISSN: 23274662 (ISSN).
Abstract | Links | BibTeX | Tags: Cost reduction, Data transfer, Digital Twins, Edge detection, Image edge detection, Network layers, Object Detection, Object detectors, Objects detection, Physical world, Resource allocation, Resource management, Resources allocation, Semantic communication, Semantics, Semantics Information, Virtual Reality, Virtual worlds, Wireless communications
@article{du_yolo-based_2024,
title = {YOLO-Based Semantic Communication with Generative AI-Aided Resource Allocation for Digital Twins Construction},
author = {B. Du and H. Du and H. Liu and D. Niyato and P. Xin and J. Yu and M. Qi and Y. Tang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173060990&doi=10.1109%2fJIOT.2023.3317629&partnerID=40&md5=60507e2f6ce2b1c345248867a9c527a1},
doi = {10.1109/JIOT.2023.3317629},
issn = {23274662 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Internet of Things Journal},
volume = {11},
number = {5},
pages = {7664–7678},
abstract = {Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this article, we propose a semantic communication framework based on you only look once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the acrlong AI-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new efficient layer aggregation network-horNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services. © 2014 IEEE.},
keywords = {Cost reduction, Data transfer, Digital Twins, Edge detection, Image edge detection, Network layers, Object Detection, Object detectors, Objects detection, Physical world, Resource allocation, Resource management, Resources allocation, Semantic communication, Semantics, Semantics Information, Virtual Reality, Virtual worlds, Wireless communications},
pubstate = {published},
tppubtype = {article}
}
Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this article, we propose a semantic communication framework based on you only look once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the acrlong AI-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new efficient layer aggregation network-horNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services. © 2014 IEEE.