1.
Jafri, Meesam; Kumar, Pankaj; Srivastava, Suraj; Jagannatham, Aditya K.; Hanzo, Lajos
Robust Hybrid Beamforming in Cooperative Cell-Free mmWave MIMO Networks Relying on Imperfect CSI Journal Article
In: IEEE Transactions on Vehicular Technology, vol. 74, no. 8, pp. 12590–12602, 2025, ISSN: 1939-9359.
Abstract | Links | BibTeX | Tags: Antenna arrays, Array signal processing, cell-free networks, Channel estimation, cooperative beamforming, CSI uncertainty, Downlink, Millimeter wave communication, MIMO, mmWave, Radio frequency, robust beamforming, Uncertainty, Uplink, Vectors
@article{jafri_robust_2025,
title = {Robust Hybrid Beamforming in Cooperative Cell-Free mmWave MIMO Networks Relying on Imperfect CSI},
author = {Meesam Jafri and Pankaj Kumar and Suraj Srivastava and Aditya K. Jagannatham and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/10945645},
doi = {10.1109/TVT.2025.3555484},
issn = {1939-9359},
year = {2025},
date = {2025-08-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Vehicular Technology},
volume = {74},
number = {8},
pages = {12590–12602},
abstract = {A low-complexity robust cooperative hybrid beamformer is designed for both the downlink and uplink of cell-free millimeter wave (mmWave) multiple-input-multiple-output (MIMO) networks, while considering realistic imperfect channel state information (CSI). To begin with, a second-order cone program (SOCP)-based robust fully-digital beamformer (FDBF) is designed for minimizing the worst-case interference for the downlink of multiple-input-single-output (MISO) systems. Subsequently, we develop a Bayesian learning (BL) framework for determining both the analog and digital components of the hybrid transmit precoder (TPC) from the FDBF. The above designs are subsequently extended to employing eigenvector perturbation theory for constructing the robust TPC for the cell-free mmWave MIMO downlink, where the users have multiple receive antennas (RAs). Furthermore, the multi-dimensional covariance fitting (MCF) framework is harnessed for designing the robust TPC of the corresponding uplink. Finally, the efficiency of the proposed TPC designs is evaluated by simulation results both in terms of their ability to mitigate the multi-user interference (MUI), and improving the spectral efficiency achieved. Additionally, the proposed designs are shown to be computationally efficient and equivalent to a minimum variance hybrid beamformer.},
keywords = {Antenna arrays, Array signal processing, cell-free networks, Channel estimation, cooperative beamforming, CSI uncertainty, Downlink, Millimeter wave communication, MIMO, mmWave, Radio frequency, robust beamforming, Uncertainty, Uplink, Vectors},
pubstate = {published},
tppubtype = {article}
}
A low-complexity robust cooperative hybrid beamformer is designed for both the downlink and uplink of cell-free millimeter wave (mmWave) multiple-input-multiple-output (MIMO) networks, while considering realistic imperfect channel state information (CSI). To begin with, a second-order cone program (SOCP)-based robust fully-digital beamformer (FDBF) is designed for minimizing the worst-case interference for the downlink of multiple-input-single-output (MISO) systems. Subsequently, we develop a Bayesian learning (BL) framework for determining both the analog and digital components of the hybrid transmit precoder (TPC) from the FDBF. The above designs are subsequently extended to employing eigenvector perturbation theory for constructing the robust TPC for the cell-free mmWave MIMO downlink, where the users have multiple receive antennas (RAs). Furthermore, the multi-dimensional covariance fitting (MCF) framework is harnessed for designing the robust TPC of the corresponding uplink. Finally, the efficiency of the proposed TPC designs is evaluated by simulation results both in terms of their ability to mitigate the multi-user interference (MUI), and improving the spectral efficiency achieved. Additionally, the proposed designs are shown to be computationally efficient and equivalent to a minimum variance hybrid beamformer.
2.
Linfu, Zou; Zhiwen, Pan; El-Hajjar, Mohammed
Graph Neural Network Aided Beamforming for Holographic Millimeter Wave MIMO Systems Journal Article
In: IEEE Transactions on Vehicular Technology, vol. 74, no. 7, pp. 10582–10595, 2025, ISSN: 1939-9359.
Abstract | Links | BibTeX | Tags: Accuracy, Array signal processing, Beamforming, Channel estimation, Downlink, Estimation, graph neural network, Graph neural networks, holographic MIMO, millimeter wave, Millimeter wave communication, OFDM, Optimization, Training
@article{linfu_graph_2025,
title = {Graph Neural Network Aided Beamforming for Holographic Millimeter Wave MIMO Systems},
author = {Zou Linfu and Pan Zhiwen and Mohammed El-Hajjar},
url = {https://ieeexplore.ieee.org/document/10896848},
doi = {10.1109/TVT.2025.3544063},
issn = {1939-9359},
year = {2025},
date = {2025-07-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Vehicular Technology},
volume = {74},
number = {7},
pages = {10582–10595},
abstract = {Holographic multiple-input multiple-output (HMIMO) systems are considered as one of the potential techniques to meet the demands of next-generation communications by replacing costly and power-hungry devices with sub-half-wavelength antenna elements. However, optimizing the beamforming matrix in the base station (BS) for HMIMO systems is challenging, given the prohibitive overhead of directly estimating the channels between the BS and the user equipment. Instead of following the traditional method of channel estimation and beamforming optimization, in this paper we employ a deep-learning technique to optimize the beamformers at the BS based on a loss function. Specifically, in this paper we introduce a graph neural network (GNN) designed to map the received pilot signals to optimized beamforming matrices and to model interactions among user equipment within the network. The simulation results show that our deep-learning method effectively maximizes the sum-rate objective while using reduced number of pilots than traditional channel estimation and beamforming optimization techniques.},
keywords = {Accuracy, Array signal processing, Beamforming, Channel estimation, Downlink, Estimation, graph neural network, Graph neural networks, holographic MIMO, millimeter wave, Millimeter wave communication, OFDM, Optimization, Training},
pubstate = {published},
tppubtype = {article}
}
Holographic multiple-input multiple-output (HMIMO) systems are considered as one of the potential techniques to meet the demands of next-generation communications by replacing costly and power-hungry devices with sub-half-wavelength antenna elements. However, optimizing the beamforming matrix in the base station (BS) for HMIMO systems is challenging, given the prohibitive overhead of directly estimating the channels between the BS and the user equipment. Instead of following the traditional method of channel estimation and beamforming optimization, in this paper we employ a deep-learning technique to optimize the beamformers at the BS based on a loss function. Specifically, in this paper we introduce a graph neural network (GNN) designed to map the received pilot signals to optimized beamforming matrices and to model interactions among user equipment within the network. The simulation results show that our deep-learning method effectively maximizes the sum-rate objective while using reduced number of pilots than traditional channel estimation and beamforming optimization techniques.
3.
Singh, Jitendra; Srivastava, Suraj; Yadav, Surya P.; Jagannatham, Aditya K.; Hanzo, Lajos
Joint Hybrid Transceiver and Reflection Matrix Design for RIS-Aided mmWave MIMO Cognitive Radio Systems Journal Article
In: IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 1, pp. 391–407, 2025, ISSN: 2332-7731.
Abstract | Links | BibTeX | Tags: Array signal processing, cognitive radio, Downlink, hybrid beamforming, Interference, Millimeter wave communication, MIMO communication, mmWave, Quality of service, Radio frequency, Riemannian conjugate gradient, RIS
@article{singh_joint_2025,
title = {Joint Hybrid Transceiver and Reflection Matrix Design for RIS-Aided mmWave MIMO Cognitive Radio Systems},
author = {Jitendra Singh and Suraj Srivastava and Surya P. Yadav and Aditya K. Jagannatham and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/10561503},
doi = {10.1109/TCCN.2024.3415620},
issn = {2332-7731},
year = {2025},
date = {2025-02-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Cognitive Communications and Networking},
volume = {11},
number = {1},
pages = {391–407},
abstract = {In this work, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) cognitive radio (CR) downlink operating in the underlay mode is investigated. The cognitive base station (CBS) communicates with multiple secondary users (SUs), each having multiple RF chains in the presence of a primary user (PU). We conceive a joint hybrid transmit precoder (TPC), receiver combiner (RC), and RIS reflection matrix (RM) design, which maximizes the sum spectral efficiency (SE) of the secondary system while maintaining the interference induced at the PU below a specified threshold. To this end, we formulate the sum-SE maximization problem considering the total transmit power (TP), the interference power (IP), and the non-convex unity modulus constraints of the RF TPC, RF RC, and RM. To solve this highly non-convex problem, we propose a two-stage hybrid transceiver design in conjunction with a novel block coordinate descent (BCD)-successive Riemannian conjugate gradient (SRCG) algorithm. We initially decompose the RF TPC, RC, and RM optimization problem into a series of sub-problems and subsequently design pairs of RF TPC and RC vectors, followed by successively optimizing the elements of the RM using the iterative BCD-SRCG algorithm. Furthermore, based on the effective baseband (BB) channel, the BB TPC and BB RC are designed using the proposed direct singular value decomposition (D-SVD) and projection based SVD (P-SVD) methods. Subsequently, the proportional water-filling solution is proposed for optimizing the power, which maximizes the weighted sum-SE of the system. Finally, simulation results are provided to compare our proposed schemes to several benchmarks and quantify the impact of other parameters on the sum-SE of the system.},
keywords = {Array signal processing, cognitive radio, Downlink, hybrid beamforming, Interference, Millimeter wave communication, MIMO communication, mmWave, Quality of service, Radio frequency, Riemannian conjugate gradient, RIS},
pubstate = {published},
tppubtype = {article}
}
In this work, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) cognitive radio (CR) downlink operating in the underlay mode is investigated. The cognitive base station (CBS) communicates with multiple secondary users (SUs), each having multiple RF chains in the presence of a primary user (PU). We conceive a joint hybrid transmit precoder (TPC), receiver combiner (RC), and RIS reflection matrix (RM) design, which maximizes the sum spectral efficiency (SE) of the secondary system while maintaining the interference induced at the PU below a specified threshold. To this end, we formulate the sum-SE maximization problem considering the total transmit power (TP), the interference power (IP), and the non-convex unity modulus constraints of the RF TPC, RF RC, and RM. To solve this highly non-convex problem, we propose a two-stage hybrid transceiver design in conjunction with a novel block coordinate descent (BCD)-successive Riemannian conjugate gradient (SRCG) algorithm. We initially decompose the RF TPC, RC, and RM optimization problem into a series of sub-problems and subsequently design pairs of RF TPC and RC vectors, followed by successively optimizing the elements of the RM using the iterative BCD-SRCG algorithm. Furthermore, based on the effective baseband (BB) channel, the BB TPC and BB RC are designed using the proposed direct singular value decomposition (D-SVD) and projection based SVD (P-SVD) methods. Subsequently, the proportional water-filling solution is proposed for optimizing the power, which maximizes the weighted sum-SE of the system. Finally, simulation results are provided to compare our proposed schemes to several benchmarks and quantify the impact of other parameters on the sum-SE of the system.
4.
Fu, Jiafei; Mobini, Zahra; Ngo, Hien Quoc; Zhu, Pengcheng; Matthaiou, Michail
WMMSE-Based Processing in Cell-Free Massive MIMO Systems Journal Article
In: IEEE Wireless Communications Letters, vol. 14, no. 2, pp. 330–334, 2025, ISSN: 2162-2345.
Abstract | Links | BibTeX | Tags: Approximation algorithms, Cell-free massive MIMO, Channel estimation, Data communication, Downlink, Optimization, Power control, Precoding, Quality of service, Uplink, Vectors, weighted minimum mean square error, weighted sum-rate maximization
@article{fu_wmmse-based_2025,
title = {WMMSE-Based Processing in Cell-Free Massive MIMO Systems},
author = {Jiafei Fu and Zahra Mobini and Hien Quoc Ngo and Pengcheng Zhu and Michail Matthaiou},
url = {https://ieeexplore.ieee.org/document/10755172},
doi = {10.1109/LWC.2024.3501156},
issn = {2162-2345},
year = {2025},
date = {2025-02-01},
urldate = {2025-10-08},
journal = {IEEE Wireless Communications Letters},
volume = {14},
number = {2},
pages = {330–334},
abstract = {In this letter, we address the weighted sum-rate maximization problem in a cell-free massive multi-input multi-output (CF-mMIMO) system, subject to constraints on the minimum individual quality of service (QoS), maximum power consumption at each access point (AP), and maximum fronthaul capacity. By harnessing the low computational complexity weighted minimum mean square error (WMMSE) framework, two algorithms are proposed to solve the formulated mixed integer nonlinear programming (MINLP) problems with instantaneous/statistical channel state information (CSI). Our instantaneous CSI-based approach can be applied to jointly optimize the power control, precoding, and user association, while the statistical CSI-based approach can be utilized to jointly optimize the power control and user association. Simulation results demonstrate that the proposed instantaneous CSI-based algorithm can provide approximately 66.72% sum-rate gain compared to the scheme with random user association, equal power allocation, and fixed local MMSE-based precoding design. Additionally, the statistical CSI-based algorithm offers competitive performance compared with the instantaneous CSI-based algorithm.},
keywords = {Approximation algorithms, Cell-free massive MIMO, Channel estimation, Data communication, Downlink, Optimization, Power control, Precoding, Quality of service, Uplink, Vectors, weighted minimum mean square error, weighted sum-rate maximization},
pubstate = {published},
tppubtype = {article}
}
In this letter, we address the weighted sum-rate maximization problem in a cell-free massive multi-input multi-output (CF-mMIMO) system, subject to constraints on the minimum individual quality of service (QoS), maximum power consumption at each access point (AP), and maximum fronthaul capacity. By harnessing the low computational complexity weighted minimum mean square error (WMMSE) framework, two algorithms are proposed to solve the formulated mixed integer nonlinear programming (MINLP) problems with instantaneous/statistical channel state information (CSI). Our instantaneous CSI-based approach can be applied to jointly optimize the power control, precoding, and user association, while the statistical CSI-based approach can be utilized to jointly optimize the power control and user association. Simulation results demonstrate that the proposed instantaneous CSI-based algorithm can provide approximately 66.72% sum-rate gain compared to the scheme with random user association, equal power allocation, and fixed local MMSE-based precoding design. Additionally, the statistical CSI-based algorithm offers competitive performance compared with the instantaneous CSI-based algorithm.
5.
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.
6.
Li, Juncheng; Huang, Yifan; Huang, Shenjie; Tavakkolnia, Iman; Haas, Harald; Safari, Majid
Integrated Communication and Positioning for IRS-Assisted LiFi Networks: 2024 IEEE Wireless Communications and Networking Conference (WCNC) Journal Article
In: 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings, pp. 01–06, 2024, ISSN: 979-8-3503-0359-9, (Place: Piscataway, NJ).
Abstract | Links | BibTeX | Tags: Downlink, Light fidelity, machine learning algorithms, mobile communication, Optical fiber networks, surface emitting lasers, Wireless communication
@article{li_integrated_2024,
title = {Integrated Communication and Positioning for IRS-Assisted LiFi Networks: 2024 IEEE Wireless Communications and Networking Conference (WCNC)},
author = {Juncheng Li and Yifan Huang and Shenjie Huang and Iman Tavakkolnia and Harald Haas and Majid Safari},
url = {https://ieeexplore.ieee.org/document/10570819/},
doi = {10.1109/wcnc57260.2024.10570819},
issn = {979-8-3503-0359-9},
year = {2024},
date = {2024-07-01},
urldate = {2024-08-20},
booktitle = {2024 IEEE Wireless Communications and Networking Conference (WCNC)},
journal = {2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings},
pages = {01–06},
publisher = {IEEE},
address = {Dubai, United Arab Emirates},
series = {IEEE Wireless Communications and Networking Conference, WCNC},
abstract = {Light-fidelity (LiFi) is a networked optical wireless communication (OWC) solution to achieve high-speed mobile communications. To address the misalignment challenges encountered in laser-based LiFi, this study introduces an innovative full-coverage indoor LiFi system with integrated communication and positioning capabilities, leveraging intelligent reflected surfaces (IRSs). By design, the proposed system ensures successful wireless downlink connectivity, irrespective of the user's random location and orientation status. An algorithm is developed to ascertain the optimal deployment of both access points (APs) and IRS layers. Moreover, this study introduces a machine learning (ML)-based OWC positioning approach designed to enhance the accuracy of the user positioning, thereby effectively boosting the performance of the IRS-assisted communication system. Numerical results demonstrate the superiority of the proposed positioning approach over traditional methods in terms of average data rate.},
note = {Place: Piscataway, NJ},
keywords = {Downlink, Light fidelity, machine learning algorithms, mobile communication, Optical fiber networks, surface emitting lasers, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
Light-fidelity (LiFi) is a networked optical wireless communication (OWC) solution to achieve high-speed mobile communications. To address the misalignment challenges encountered in laser-based LiFi, this study introduces an innovative full-coverage indoor LiFi system with integrated communication and positioning capabilities, leveraging intelligent reflected surfaces (IRSs). By design, the proposed system ensures successful wireless downlink connectivity, irrespective of the user's random location and orientation status. An algorithm is developed to ascertain the optimal deployment of both access points (APs) and IRS layers. Moreover, this study introduces a machine learning (ML)-based OWC positioning approach designed to enhance the accuracy of the user positioning, thereby effectively boosting the performance of the IRS-assisted communication system. Numerical results demonstrate the superiority of the proposed positioning approach over traditional methods in terms of average data rate.