@article{smith_dimensional_2025,

title = {Dimensional Scaling Laws for Continuous Fluid Antenna Systems},

author = {Peter J. Smith and Amy S. Inwood and Michail Matthaiou and Rajitha Senanayake},

url = {https://ieeexplore.ieee.org/document/10965723},

doi = {10.1109/LWC.2025.3560861},

issn = {2162-2345},

year  = {2025},

date = {2025-07-01},

urldate = {2025-10-08},

journal = {IEEE Wireless Communications Letters},

volume = {14},

number = {7},

pages = {2004–2008},

abstract = {Consider the signal-to-noise ratio (SNR) of a continuous fluid antenna system (CFAS) operating over a Rayleigh fading channel. In this letter, we extend traditional system assumptions and consider spatially coherent isotropic correlation, continuous positioning of the antenna rather than discrete, and the use of multi-dimensional space (1D, 2D and 3D). By focusing on the upper tail of the received SNR distribution (the high SNR probability (HSP)), we are able to derive asymptotically exact closed-form formulas for the HSP. Finally, these results lead to scaling laws which describe the increase in the HSP as we employ more dimensions and the optimal CFAS dimensions.},

keywords = {3D antenna geometries, Antennas, Correlation, Fluid antenna systems, Fluids, high SNR probability, random fields, Rayleigh channels, Rayleigh fading, Shape, Signal to noise ratio, Tail, Three-dimensional displays, Training, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{liu_near-field_2025,

title = {Near-field Communications: What Will Be Different?},

author = {Yuanwei Liu and Jiaqi Xu and Zhaolin Wang and Xidong Mu and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10944643},

doi = {10.1109/MWC.001.2300588},

issn = {1558-0687},

year  = {2025},

date = {2025-04-01},

urldate = {2025-10-08},

journal = {IEEE Wireless Communications},

volume = {32},

number = {2},

pages = {262–270},

abstract = {The design dilemma of “What will be different between near-field communications (NFC) and far-field communications (FFC)?” is addressed from four perspectives. First, from the channel modelling perspective, the differences between near-field and far-field channel models are discussed. A novel Green's function-based channel model is proposed for continuous-aperture antennas, which is contrasted to conventional channel models tailored for spatially-discrete antennas. Second, from the performance analysis per-spective, analytical results for characterizing the degrees of freedom and the power scaling laws in the near-field region are provided for both spatially-discrete and continuous-aperture antennas. Third, from the beamforming perspective, far-field beamforming is analogous to a “flashlight” that enables beamsteering, while near-field beamforming can be likened to a “spotlight” that facilitates beamfocusing. As a further advance, a couple of new beamforming structures are proposed for exploiting the new characteristics of NFC. Fourth, from the application perspective, new designs are discussed in the context of promising next-generation technologies in NFC, where our preliminary numerical results demonstrate that distance-aware target sensing and enhanced physical layer security can be realized in NFC. Finally, several future research directions of NFC are discussed.},

keywords = {Antennas, Array signal processing, Channel models, Green's function methods, Next generation networking, Performance analysis, Physical layer security, Sensors},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}