1.
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.
2.
Li, Qingchao; El-Hajjar, Mohammed; Cao, Kaijun; Xu, Chao; Haas, Harald; Hanzo, Lajos
Holographic Metasurface-Based Beamforming for Multi-Altitude LEO Satellite Networks Journal Article
In: IEEE Transactions on Wireless Communications, pp. 1–1, 2025, ISSN: 1558-2248, (arXiv:2501.04164 [cs]).
Abstract | Links | BibTeX | Tags: Array signal processing, Computer architecture, Downlink, holographic metasurface, hybrid beamforming, inter-satellite interference, Low Earth Orbit (LEO) satellite communication, Low earth orbit satellites, Metasurfaces, Precoding, Satellite broadcasting, Satellite communications, Satellites, stochastic geometry, Throughput
@article{li_holographic_2025,
title = {Holographic Metasurface-Based Beamforming for Multi-Altitude LEO Satellite Networks},
author = {Qingchao Li and Mohammed El-Hajjar and Kaijun Cao and Chao Xu and Harald Haas and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/abstract/document/10844052/?casa_token=5kb4rgy_qqAAAAAA:Qy4zV9IQ3FSfC6Cy7it5EvcxjQM2a675RSsbRiRKNPsADHjWFXZ0VHem5zJ_dVf5IBDhE6R2sg},
doi = {10.1109/TWC.2025.3527962},
issn = {1558-2248},
year = {2025},
date = {2025-01-01},
urldate = {2025-02-24},
journal = {IEEE Transactions on Wireless Communications},
pages = {1–1},
publisher = {arXiv},
abstract = {Low Earth Orbit (LEO) satellite networks are capable of improving the global Internet service coverage. In this context, we propose a hybrid beamforming design for holographic metasurface based terrestrial users in multi-altitude LEO satellite networks. Firstly, the holographic beamformer is optimized by maximizing the downlink channel gain from the serving satellite to the terrestrial user. Then, the digital beamformer is designed by conceiving a minimum mean square error (MMSE) based detection algorithm for mitigating the interference arriving from other satellites. To dispense with excessive overhead of full channel state information (CSI) acquisition of all satellites, we propose a low-complexity MMSE beamforming algorithm that only relies on the distribution of the LEO satellite constellation harnessing stochastic geometry, which can achieve comparable throughput to that of the algorithm based on the full CSI in the case of a dense LEO satellite deployment. Furthermore, it outperforms the maximum ratio combining (MRC) algorithm, thanks to its inter-satellite interference mitigation capacity. The simulation results show that our proposed holographic metasurface based hybrid beamforming architecture is capable of outperforming the state-of-the-art antenna array architecture in terms of its throughput, given the same physical size of the transceivers. Moreover, we demonstrate that the beamforming performance attained can be substantially improved by taking into account the mutual coupling effect, imposed by the dense placement of the holographic metasurface elements.},
note = {arXiv:2501.04164 [cs]},
keywords = {Array signal processing, Computer architecture, Downlink, holographic metasurface, hybrid beamforming, inter-satellite interference, Low Earth Orbit (LEO) satellite communication, Low earth orbit satellites, Metasurfaces, Precoding, Satellite broadcasting, Satellite communications, Satellites, stochastic geometry, Throughput},
pubstate = {published},
tppubtype = {article}
}
Low Earth Orbit (LEO) satellite networks are capable of improving the global Internet service coverage. In this context, we propose a hybrid beamforming design for holographic metasurface based terrestrial users in multi-altitude LEO satellite networks. Firstly, the holographic beamformer is optimized by maximizing the downlink channel gain from the serving satellite to the terrestrial user. Then, the digital beamformer is designed by conceiving a minimum mean square error (MMSE) based detection algorithm for mitigating the interference arriving from other satellites. To dispense with excessive overhead of full channel state information (CSI) acquisition of all satellites, we propose a low-complexity MMSE beamforming algorithm that only relies on the distribution of the LEO satellite constellation harnessing stochastic geometry, which can achieve comparable throughput to that of the algorithm based on the full CSI in the case of a dense LEO satellite deployment. Furthermore, it outperforms the maximum ratio combining (MRC) algorithm, thanks to its inter-satellite interference mitigation capacity. The simulation results show that our proposed holographic metasurface based hybrid beamforming architecture is capable of outperforming the state-of-the-art antenna array architecture in terms of its throughput, given the same physical size of the transceivers. Moreover, we demonstrate that the beamforming performance attained can be substantially improved by taking into account the mutual coupling effect, imposed by the dense placement of the holographic metasurface elements.