1.
Li, Qingchao; El-Hajjar, Mohammed; Xu, Chao; Zhang, Chao; Hanzo, Lajos
XL-MIMO Based Hierarchical Receive Beamforming Subject to Hardware Impairments in the Uplink of Cell-Free Networks Journal Article
In: IEEE Transactions on Vehicular Technology, pp. 1–11, 2025, ISSN: 1939-9359.
Abstract | Links | BibTeX | Tags: Antenna arrays, Antennas, Array signal processing, cell-free network, Central Processing Unit, Computer architecture, Estimation, Extremely large-scale multiple-input and multiple-output (XL-MIMO), Hardware, hardware impairment (HWI), hierarchical detection, near-field, Signal processing algorithms, Signal to noise ratio, Vectors
@article{li_xl-mimo_2025,
title = {XL-MIMO Based Hierarchical Receive Beamforming Subject to Hardware Impairments in the Uplink of Cell-Free Networks},
author = {Qingchao Li and Mohammed El-Hajjar and Chao Xu and Chao Zhang and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/11091536},
doi = {10.1109/TVT.2025.3592149},
issn = {1939-9359},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Vehicular Technology},
pages = {1–11},
abstract = {Extremely large-scale multiple-input and multiple-output (XL-MIMO) exhibit substantial spatial multiplexing capabilities owing to their high degree of freedom. As the number of antenna elements increases, it becomes more practically suitable to utilize cost-effective antennas equipped with low-resolution RF chains. However, hardware impairments (HWIs) associated with these cost-effective antennas lead to performance saturation in the high signal-to-noise ratio (SNR) region, which cannot be mitigated by merely increasing the transmit power. To address these challenges, we propose a hierarchical receive beamforming method for XL-MIMO based near-field cell-free networks with HWIs. Specifically, the antenna array of each access point (AP) is partitioned into multiple sub-arrays, with each sub-array independently harnessing the minimum mean-square error (MMSE) receive beamforming algorithm. The local data estimates at each AP are then optimized using the results from all sub-arrays, and the central processing unit (CPU) performs its final information recovery by integrating these local estimates. Our theoretical analysis shows that the proposed hierarchical receive beamforming method achieves a higher ergodic sum-rate than the state-of-the-art (SoA) scheme in XL-MIMO systems in the face of HWIs.},
keywords = {Antenna arrays, Antennas, Array signal processing, cell-free network, Central Processing Unit, Computer architecture, Estimation, Extremely large-scale multiple-input and multiple-output (XL-MIMO), Hardware, hardware impairment (HWI), hierarchical detection, near-field, Signal processing algorithms, Signal to noise ratio, Vectors},
pubstate = {published},
tppubtype = {article}
}
Extremely large-scale multiple-input and multiple-output (XL-MIMO) exhibit substantial spatial multiplexing capabilities owing to their high degree of freedom. As the number of antenna elements increases, it becomes more practically suitable to utilize cost-effective antennas equipped with low-resolution RF chains. However, hardware impairments (HWIs) associated with these cost-effective antennas lead to performance saturation in the high signal-to-noise ratio (SNR) region, which cannot be mitigated by merely increasing the transmit power. To address these challenges, we propose a hierarchical receive beamforming method for XL-MIMO based near-field cell-free networks with HWIs. Specifically, the antenna array of each access point (AP) is partitioned into multiple sub-arrays, with each sub-array independently harnessing the minimum mean-square error (MMSE) receive beamforming algorithm. The local data estimates at each AP are then optimized using the results from all sub-arrays, and the central processing unit (CPU) performs its final information recovery by integrating these local estimates. Our theoretical analysis shows that the proposed hierarchical receive beamforming method achieves a higher ergodic sum-rate than the state-of-the-art (SoA) scheme in XL-MIMO systems in the face of HWIs.
2.
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.
3.
Xiao, Yun; Wang, Enhao; Chen, Yunfei
Integrated Sensing and Communications With Multiple Targets and Multiple Users in Mixed Field Proceedings Article
In: 2024 IEEE 24th International Conference on Communication Technology (ICCT), pp. 1288–1292, 2024, ISSN: 2576-7828, (ISSN: 2576-7828).
Abstract | Links | BibTeX | Tags: Antenna arrays, Array signal processing, Beamforming, far-filed, Integrated sensing and communication, integrated sensing and communications, Interference, mixed field, model mismatch, multiple-target, near-field, Next generation networking, Numerical models, Optimization, Propagation losses, Signal to noise ratio, Wireless communication
@inproceedings{xiao_integrated_2024,
title = {Integrated Sensing and Communications With Multiple Targets and Multiple Users in Mixed Field},
author = {Yun Xiao and Enhao Wang and Yunfei Chen},
url = {https://ieeexplore.ieee.org/document/10946468},
doi = {10.1109/ICCT62411.2024.10946468},
issn = {2576-7828},
year = {2024},
date = {2024-10-01},
urldate = {2025-10-08},
booktitle = {2024 IEEE 24th International Conference on Communication Technology (ICCT)},
pages = {1288–1292},
abstract = {Integrated sensing and communications (ISAC) plays a crucial role in the next-generation wireless systems. Owing to the deployment of high carrier frequencies and/or large-scale antenna arrays, targets and communications users in the ISAC systems may follow different propagation models. However, most existing works assume the same propagation model for both communications and sensing. This work considers a practical scenario where multiple targets and communications users are in different fields. Beamforming design is proposed to optimize the sensing signal-to-clutter-plus-noise ratio (SCNR) of each target. Specifically, a sensing performance fairness profile optimization (FPO) problem is formulated, and a Dinkelbach-type algorithm is proposed to solve the problem. Numerical results show the tradeoff between mixed-field communications and sensing, the effects of antenna size and model mismatch between near field and far field on the sensing performance of the mixed-field ISAC.},
note = {ISSN: 2576-7828},
keywords = {Antenna arrays, Array signal processing, Beamforming, far-filed, Integrated sensing and communication, integrated sensing and communications, Interference, mixed field, model mismatch, multiple-target, near-field, Next generation networking, Numerical models, Optimization, Propagation losses, Signal to noise ratio, Wireless communication},
pubstate = {published},
tppubtype = {inproceedings}
}
Integrated sensing and communications (ISAC) plays a crucial role in the next-generation wireless systems. Owing to the deployment of high carrier frequencies and/or large-scale antenna arrays, targets and communications users in the ISAC systems may follow different propagation models. However, most existing works assume the same propagation model for both communications and sensing. This work considers a practical scenario where multiple targets and communications users are in different fields. Beamforming design is proposed to optimize the sensing signal-to-clutter-plus-noise ratio (SCNR) of each target. Specifically, a sensing performance fairness profile optimization (FPO) problem is formulated, and a Dinkelbach-type algorithm is proposed to solve the problem. Numerical results show the tradeoff between mixed-field communications and sensing, the effects of antenna size and model mismatch between near field and far field on the sensing performance of the mixed-field ISAC.
4.
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.