1.
Meng, Kaitao; Masouros, Christos; Wong, Kai-Kit; Petropulu, Athina P.; Hanzo, Lajos
Integrated Sensing and Communication Meets Smart Propagation Engineering: Opportunities and Challenges Journal Article
In: IEEE Network, vol. 39, no. 2, pp. 278–285, 2025, ISSN: 1558-156X.
Abstract | Links | BibTeX | Tags: Antennas, Channel estimation, fluid antennas, Fluids, Integrated sensing and communication, intelligent surfaces, Interference, Mobile antennas, Radio transmitters, smart propagation engineering, Trajectory, Transmitting antennas, Wireless communication
@article{meng_integrated_2025,
title = {Integrated Sensing and Communication Meets Smart Propagation Engineering: Opportunities and Challenges},
author = {Kaitao Meng and Christos Masouros and Kai-Kit Wong and Athina P. Petropulu and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/10833779},
doi = {10.1109/MNET.2025.3527130},
issn = {1558-156X},
year = {2025},
date = {2025-03-01},
urldate = {2025-10-08},
journal = {IEEE Network},
volume = {39},
number = {2},
pages = {278–285},
abstract = {Both smart propagation engineering as well as integrated sensing and communication (ISAC) constitute promising candidates for next-generation (NG) mobile networks. We provide a synergistic view of these technologies, and explore their mutual benefits. First, moving beyond just intelligent surfaces, we provide a holistic view of the engineering aspects of smart propagation environments. By delving into the fundamental characteristics of intelligent surfaces, fluid antennas, and unmanned aerial vehicles, we reveal that more efficient control of the pathloss and fading can be achieved, thus facilitating intrinsic integration and mutual assistance between sensing and communication functionalities. In turn, with the exploitation of the sensing capabilities of ISAC to orchestrate the efficient configuration of radio environments, both the computational effort and signaling overheads can be reduced. We present indicative simulation results, which verify that cooperative smart propagation environment design significantly enhances the ISAC performance. Finally, some promising directions are outlined for combining ISAC with smart propagation engineering.},
keywords = {Antennas, Channel estimation, fluid antennas, Fluids, Integrated sensing and communication, intelligent surfaces, Interference, Mobile antennas, Radio transmitters, smart propagation engineering, Trajectory, Transmitting antennas, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
Both smart propagation engineering as well as integrated sensing and communication (ISAC) constitute promising candidates for next-generation (NG) mobile networks. We provide a synergistic view of these technologies, and explore their mutual benefits. First, moving beyond just intelligent surfaces, we provide a holistic view of the engineering aspects of smart propagation environments. By delving into the fundamental characteristics of intelligent surfaces, fluid antennas, and unmanned aerial vehicles, we reveal that more efficient control of the pathloss and fading can be achieved, thus facilitating intrinsic integration and mutual assistance between sensing and communication functionalities. In turn, with the exploitation of the sensing capabilities of ISAC to orchestrate the efficient configuration of radio environments, both the computational effort and signaling overheads can be reduced. We present indicative simulation results, which verify that cooperative smart propagation environment design significantly enhances the ISAC performance. Finally, some promising directions are outlined for combining ISAC with smart propagation engineering.
2.
Meng, Kaitao; Han, Kawon; Masouros, Christos; Hanzo, Lajos
Network-level ISAC: An Analytical Study of Antenna Topologies Ranging from Massive to Cell-Free MIMO Journal Article
In: IEEE Transactions on Wireless Communications, pp. 1–1, 2025, ISSN: 1558-2248.
Abstract | Links | BibTeX | Tags: antenna allocation, Antenna arrays, Antenna measurements, Antennas, Array signal processing, cooperative sensing and communication, Geometry, Integrated sensing and communication, Location awareness, MIMO, multi-cell networks, network performance analysis, Resource management, stochastic geometry, Transmitting antennas
@article{meng_network-level_2025,
title = {Network-level ISAC: An Analytical Study of Antenna Topologies Ranging from Massive to Cell-Free MIMO},
author = {Kaitao Meng and Kawon Han and Christos Masouros and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/11030947},
doi = {10.1109/TWC.2025.3576432},
issn = {1558-2248},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Wireless Communications},
pages = {1–1},
abstract = {A cooperative architecture is proposed for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate how the allocation of antennas-to-base stations (BSs) affects cooperative sensing and cooperative communication performance. More explicitly, we balance the benefits of geographically concentrated antennas in the massive multiple input multiple output (MIMO) fashion, which enhance beamforming and coherent processing, against those of geographically distributed antennas towards cell-free transmission, which improve diversity and reduce service distances. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)- based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, for critically appraising their effects on ISAC network performance. Our analysis shows that in networks having N ISAC nodes following a Poisson point process, the localization accuracy of TOF-based methods follows a ln2 N scaling law (explicitly, the Cramér-Rao lower bound (CRLB) reduces with ln2 N). The AOA-based methods follow a ln N scaling law, while the hybrid methods scale as a ln2 N+b ln N, where a and b represent parameters related to TOF and AOA measurements, respectively. The difference between these scaling laws arises from the distinct ways in which measurement results are converted into the target location. Specifically, when converting AOA measurements to the target location, the localization error introduced during this conversion is inversely proportional to the distance between the BS and the target, leading to a more significant reduction in accuracy as the number of transceivers increases. In contrast, TOF-based localization avoids such distance dependent errors in the conversion process. In terms of communication performance, we derive a tractable expression for the communication data rate, considering various cooperative region sizes and antenna-to-BS allocation strategy. It is proved that higher path loss exponents favor distributed antenna allocation to reduce access distances, while lower exponents favor centralized antenna allocation to maximize beamforming gain. Simulations confirm that cooperative transmission and sensing in ISAC networks can effectively improve non-cooperative sensing and communication performance The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.},
keywords = {antenna allocation, Antenna arrays, Antenna measurements, Antennas, Array signal processing, cooperative sensing and communication, Geometry, Integrated sensing and communication, Location awareness, MIMO, multi-cell networks, network performance analysis, Resource management, stochastic geometry, Transmitting antennas},
pubstate = {published},
tppubtype = {article}
}
A cooperative architecture is proposed for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate how the allocation of antennas-to-base stations (BSs) affects cooperative sensing and cooperative communication performance. More explicitly, we balance the benefits of geographically concentrated antennas in the massive multiple input multiple output (MIMO) fashion, which enhance beamforming and coherent processing, against those of geographically distributed antennas towards cell-free transmission, which improve diversity and reduce service distances. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)- based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, for critically appraising their effects on ISAC network performance. Our analysis shows that in networks having N ISAC nodes following a Poisson point process, the localization accuracy of TOF-based methods follows a ln2 N scaling law (explicitly, the Cramér-Rao lower bound (CRLB) reduces with ln2 N). The AOA-based methods follow a ln N scaling law, while the hybrid methods scale as a ln2 N+b ln N, where a and b represent parameters related to TOF and AOA measurements, respectively. The difference between these scaling laws arises from the distinct ways in which measurement results are converted into the target location. Specifically, when converting AOA measurements to the target location, the localization error introduced during this conversion is inversely proportional to the distance between the BS and the target, leading to a more significant reduction in accuracy as the number of transceivers increases. In contrast, TOF-based localization avoids such distance dependent errors in the conversion process. In terms of communication performance, we derive a tractable expression for the communication data rate, considering various cooperative region sizes and antenna-to-BS allocation strategy. It is proved that higher path loss exponents favor distributed antenna allocation to reduce access distances, while lower exponents favor centralized antenna allocation to maximize beamforming gain. Simulations confirm that cooperative transmission and sensing in ISAC networks can effectively improve non-cooperative sensing and communication performance The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.
3.
Yan, Hua; Chen, Yunfei
Optimum Distance for In-Flight UAV-to-UAV Wireless Charging Journal Article
In: IEEE Access, vol. 13, pp. 143914–143924, 2025, ISSN: 2169-3536.
Abstract | Links | BibTeX | Tags: Aperture antennas, Autonomous aerial vehicles, Batteries, Energy Efficiency, Energy loss, far-field, Inductive charging, near-field, Receiving antennas, RF signals, Simultaneous wireless information and power transfer, Transmitting antennas, UAV communications, Wireless communication, Wireless communications, wireless power transfer (WPT)
@article{yan_optimum_2025,
title = {Optimum Distance for In-Flight UAV-to-UAV Wireless Charging},
author = {Hua Yan and Yunfei Chen},
url = {https://ieeexplore.ieee.org/document/11123803/},
doi = {10.1109/ACCESS.2025.3598733},
issn = {2169-3536},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Access},
volume = {13},
pages = {143914–143924},
abstract = {Wireless charging is a promising technology for communications using battery-powered unmanned aerial vehicles (UAVs). In this paper, the optimal distance for UAV-to-UAV in-flight wireless charging and communications is studied. Considering the practical applications, two schemes are proposed. In the first scheme, the discharging UAV (D-UAV) and the charged UAV (C-UAV) are aligned during charging, which requires the D-UAV and the C-UAV to remain relatively stationary. In the second scheme, the D-UAV and the C-UAV move during charging. For both schemes, we aim to maximize the received energy at the C-UAV under the condition that the minimum achievable rate for communications is met. Numerical results show that the optimal distance exists in the Fresnel zone. They also show that the optimal distance increases with the charging frequency. This work provides useful guidance for UAV in-flight wireless charging and communications system designs.},
keywords = {Aperture antennas, Autonomous aerial vehicles, Batteries, Energy Efficiency, Energy loss, far-field, Inductive charging, near-field, Receiving antennas, RF signals, Simultaneous wireless information and power transfer, Transmitting antennas, UAV communications, Wireless communication, Wireless communications, wireless power transfer (WPT)},
pubstate = {published},
tppubtype = {article}
}
Wireless charging is a promising technology for communications using battery-powered unmanned aerial vehicles (UAVs). In this paper, the optimal distance for UAV-to-UAV in-flight wireless charging and communications is studied. Considering the practical applications, two schemes are proposed. In the first scheme, the discharging UAV (D-UAV) and the charged UAV (C-UAV) are aligned during charging, which requires the D-UAV and the C-UAV to remain relatively stationary. In the second scheme, the D-UAV and the C-UAV move during charging. For both schemes, we aim to maximize the received energy at the C-UAV under the condition that the minimum achievable rate for communications is met. Numerical results show that the optimal distance exists in the Fresnel zone. They also show that the optimal distance increases with the charging frequency. This work provides useful guidance for UAV in-flight wireless charging and communications system designs.