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.
Lu, Zhizheng; Han, Yu; Jin, Shi; Matthaiou, Michail
Near-Field Localization and Channel Reconstruction for ELAA Systems Journal Article
In: IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 6938–6953, 2024, ISSN: 1558-2248.
Abstract | Links | BibTeX | Tags: Antenna arrays, Channel estimation, Channel reconstruction, localization, Location awareness, Mobile antennas, near-field, PEB, Precoding, Radio frequency, Receiving antennas, subarray hybrid ELAA systems
@article{lu_near-field_2024,
title = {Near-Field Localization and Channel Reconstruction for ELAA Systems},
author = {Zhizheng Lu and Yu Han and Shi Jin and Michail Matthaiou},
url = {https://ieeexplore.ieee.org/document/10345492},
doi = {10.1109/TWC.2023.3336328},
issn = {1558-2248},
year = {2024},
date = {2024-07-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Wireless Communications},
volume = {23},
number = {7},
pages = {6938–6953},
abstract = {In this paper, an efficient near-field channel reconstruction and user equipment (UE) localization scheme is proposed for extremely large antenna array (ELAA) systems using a subarray hybrid precoding architecture. Considering the non-negligible signal amplitude and phase variations across the different receive antennas, a more realistic channel model is adopted. The channel environment, with an approximate smooth ground surface, is modeled. In fact, the channel can be divided into a line-of-sight (LoS) path, a reflection path and some non-LoS (NLoS) paths. Based on the sparsity of the channel in the spatial domain, the damped Newtonized orthogonal matching pursuit (DNOMP) algorithm is also proposed to accurately estimate the multipaths, and reconstruct the channel. Then, a UE localization algorithm is proposed, which can detect the existence of the LoS path and locate the UE. A joint localization algorithm is also devised to further increase the positioning reliability. Simulation results verify that the DNOMP algorithm can reconstruct the channel with better NMSE performance than other schemes. The localization algorithm can locate the UE with low error whenever the LoS path exists or not, with an accuracy close enough to the position error bound (PEB), while the joint localization algorithm can further enhance the positioning reliability.},
keywords = {Antenna arrays, Channel estimation, Channel reconstruction, localization, Location awareness, Mobile antennas, near-field, PEB, Precoding, Radio frequency, Receiving antennas, subarray hybrid ELAA systems},
pubstate = {published},
tppubtype = {article}
}
In this paper, an efficient near-field channel reconstruction and user equipment (UE) localization scheme is proposed for extremely large antenna array (ELAA) systems using a subarray hybrid precoding architecture. Considering the non-negligible signal amplitude and phase variations across the different receive antennas, a more realistic channel model is adopted. The channel environment, with an approximate smooth ground surface, is modeled. In fact, the channel can be divided into a line-of-sight (LoS) path, a reflection path and some non-LoS (NLoS) paths. Based on the sparsity of the channel in the spatial domain, the damped Newtonized orthogonal matching pursuit (DNOMP) algorithm is also proposed to accurately estimate the multipaths, and reconstruct the channel. Then, a UE localization algorithm is proposed, which can detect the existence of the LoS path and locate the UE. A joint localization algorithm is also devised to further increase the positioning reliability. Simulation results verify that the DNOMP algorithm can reconstruct the channel with better NMSE performance than other schemes. The localization algorithm can locate the UE with low error whenever the LoS path exists or not, with an accuracy close enough to the position error bound (PEB), while the joint localization algorithm can further enhance the positioning reliability.