@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}

}

@article{wang_orbital_2025,

title = {Orbital Angular Momentum for Wireless Communications: Key Performance Indicators and Performance Comparison},

author = {Zihao Wang and Mohammed El-Hajjar and Lie-Liang Yang},

url = {https://ieeexplore.ieee.org/document/10990278},

doi = {10.1109/ACCESS.2025.3567732},

issn = {2169-3536},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Access},

volume = {13},

pages = {80889–80913},

abstract = {Orbital angular momentum (OAM) is an intrinsic property of electromagnetic (EM) waves that has opened new possibilities for enhancing the capacity of wireless communications. Consequently, it has garnered significant attention in recent years. For wireless communications, antenna arrays are the most effective and widely studied approaches for OAM-wave generation. Various types of antenna arrays have been explored in research and development; however, a comprehensive comparison of these arrays remains lacking. This paper addresses this gap by first reviewing the various types of phased arrays that have been considered for OAM generation in the literature. Subsequently, it addresses the key performance indicators (KPIs) of the antenna arrays for OAM-wave generation. These KPIs include directivity, the largest producible OAM-mode (LPM), OAM-mode multiplexing capability, divergence angle, and mode purity. Based on the KPIs, a comparative analysis is conducted across several types of antenna arrays, including uniform square arrays (USA), uniform circular arrays (UCA), three-dimensional (3D) helical circular arrays (HCA), 3D helical circular sub-arrays (HCSA), and concentric UCAs (CUCA), under various settings. The study highlights the advantages and limitations of each antenna array type and examines how different parameters influence their performance.},

keywords = {6G communications, 6G mobile communication, antenna array, Antenna arrays, directivity, divergence angle, Key performance indicator, key performance indicators, Linear antenna arrays, OAM-mode, OFDM, orbital angular momentum (OAM), Orbital calculations, phased array, Phased arrays, purity, Three-dimensional displays, Wireless communication, Wireless communications, Wireless sensor networks},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}