@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{maity_variational_2025,

title = {Variational Bayesian Learning for 3-D Localization of Extended Targets in mmWave MIMO OFDM ISAC Systems},

author = {Priyanka Maity and Deepika Harish and Suraj Srivastava and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10990143},

doi = {10.1109/OJCOMS.2025.3567429},

issn = {2644-125X},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Open Journal of the Communications Society},

volume = {6},

pages = {4421–4436},

abstract = {Variational Bayesian learning (VBL)-aided extended target localization is conceived for orthogonal frequency division multiplexing (OFDM) based-mmWave MIMO systems using the OFDM integrated sensing and communication (ISAC) waveform. The proposed framework also considers the intercarrier interference (ICI) effects encountered in mobile scenarios and the clutter present in the environment. The proposed algorithm is based on a hybrid mmWave MIMO architecture, where the number of radio frequency (RF) chains is significantly lower than the number of antennas. A range, Doppler and angular (RDA)-domain representation of the target in three-dimensional (3D) space is conceived for accurate target parameter estimation. The proposed algorithm exploits the four-dimensional (4D) sparsity arising in the RDA domain of the scattering scene and employs the powerful VBL framework for the estimation of target parameters, such as elevation angle, azimuth angle, range and velocity. To handle a practical scenario where the actual target parameters typically deviate from their finite-resolution grid, a super-resolution-based improved off-grid VBL is developed for recursively updating the parameter grid, thereby progressively improving the estimates. We also determine the Cramér-Rao bound (CRB) and Bayesian CRB for the estimation of the target parameters in order to bound the estimation performance. Our simulation results validate the superior performance of the proposed approach in comparison to the existing algorithms.},

keywords = {azimuth angle, Bayes methods, Bayesian learning, Clutter, Direction-of-arrival estimation, Doppler effect, elevation angle, Estimation, extended targets, Integrated sensing and communication, Location awareness, Millimeter wave communication, MIMO, mmWave, OFDM, Radar, sparsity, Three-dimensional displays},

pubstate = {published},

tppubtype = {article}

}

@article{li_indoor_2025,

title = {Indoor Localization and Tracking in Reconfigurable Intelligent Surface Aided mmWave Systems},

author = {Kunlun Li and Mohammed El-Hajjar and Chao Xu and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/11050944},

doi = {10.1109/OJVT.2025.3582885},

issn = {2644-1330},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Open Journal of Vehicular Technology},

volume = {6},

pages = {1815–1831},

abstract = {Millimeter wave (mmWave) carriers have a high available bandwidth, which can be beneficial for high-resolution localization in both the angular and temporal domains. However, the limited coverage due to severe path loss and line-of-sight (LoS) blockage are considered to be major challenges in mmWave. A promising solution is to employ reconfigurable intelligent surfaces (RIS) to circumvent the lack of line-of-sight paths, which can assist in localization. Furthermore, radio localization and tracking are capable of accurate real-time monitoring of the UE's locations and trajectories. In this paper, we propose a three-stage indoor tracking scheme. In the first stage, channel sounding is harnessed in support of the transmitter beamforming and receiver combining design. Based on the estimation in the first stage, a simplified received signal model is obtained, while using a discrete Fourier transform (DFT) matrix for the configuration of the RIS phase shifter for each time block. Based on the simplified received signal model, tracking initialization is carried out. Finally, in the third stage, Kalman filtering is employed for tracking. Our results demonstrate that the proposed scheme is capable of improving both the accuracy and robustness of tracking compared to single-shot successive localization. Additionally, we derive the position error bounds (PEB) of single-shot localization.},

keywords = {Array signal processing, Channel estimation, Covariance matrices, Estimation, Kalman filters, localization/positioning, Location awareness, Millimeter wave communication, mmWave, OFDM, Radar tracking, Reconfigurable Intelligent Surfaces, sparse Bayesian learning, tracking},

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}

}