1.
Smith, Peter J.; Inwood, Amy S.; Matthaiou, Michail; Senanayake, Rajitha
Dimensional Scaling Laws for Continuous Fluid Antenna Systems Journal Article
In: IEEE Wireless Communications Letters, vol. 14, no. 7, pp. 2004–2008, 2025, ISSN: 2162-2345.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
2.
Mobini, Zahra; Mohammadi, Mohammadali; He, Jiajun; Ngo, Hien Quoc; Matthaiou, Michail
Cell-Free Massive MIMO-Assisted ISAC with Beam Scanning Proceedings Article
In: 2025 IEEE 26th International Workshop on Signal Processing and Artificial Intelligence for Wireless Communications (SPAWC), pp. 1–5, 2025, ISSN: 1948-3252, (ISSN: 1948-3252).
Abstract | Links | BibTeX | Tags: Conferences, Copper, Integrated sensing and communication, Optimization, Programming, Protocols, Signal processing, Spectral efficiency, Three-dimensional displays, Transmitters
@inproceedings{mobini_cell-free_2025,
title = {Cell-Free Massive MIMO-Assisted ISAC with Beam Scanning},
author = {Zahra Mobini and Mohammadali Mohammadi and Jiajun He and Hien Quoc Ngo and Michail Matthaiou},
url = {https://ieeexplore.ieee.org/document/11143371},
doi = {10.1109/SPAWC66079.2025.11143371},
issn = {1948-3252},
year = {2025},
date = {2025-07-01},
urldate = {2025-10-08},
booktitle = {2025 IEEE 26th International Workshop on Signal Processing and Artificial Intelligence for Wireless Communications (SPAWC)},
pages = {1–5},
abstract = {This paper proposes a comprehensive framework for a cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) system, where the access points (APs) are partitioned into communication APs (CAPs) and the sensing APs (SAPs) to simultaneously support downlink (DL) communications and multi-static sensing. A dedicated sensing transmitter (ST) and the SAPs cooperatively sense a target within a designated zone, while the CAPs serve multiple communication users (CUs). To enable practical 3-dimensional (3D) target localization, we develop a novel beam scanning protocol and derive closed-form expressions for the DL spectral efficiency (SE), mainlobe-to-average sensing ratio (MASR), and the Cramer-Rao lower bound (CRLB) of target estimation. Moreover, we formulate a power optimization problem to improve the sensing performance under SE constraints for CUs, solving it efficiently using fractional programming (FP) techniques. Numerical results demonstrate that our approach achieves sensing performance gains of up to 20 dB and significantly reduces the CRLB.},
note = {ISSN: 1948-3252},
keywords = {Conferences, Copper, Integrated sensing and communication, Optimization, Programming, Protocols, Signal processing, Spectral efficiency, Three-dimensional displays, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a comprehensive framework for a cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) system, where the access points (APs) are partitioned into communication APs (CAPs) and the sensing APs (SAPs) to simultaneously support downlink (DL) communications and multi-static sensing. A dedicated sensing transmitter (ST) and the SAPs cooperatively sense a target within a designated zone, while the CAPs serve multiple communication users (CUs). To enable practical 3-dimensional (3D) target localization, we develop a novel beam scanning protocol and derive closed-form expressions for the DL spectral efficiency (SE), mainlobe-to-average sensing ratio (MASR), and the Cramer-Rao lower bound (CRLB) of target estimation. Moreover, we formulate a power optimization problem to improve the sensing performance under SE constraints for CUs, solving it efficiently using fractional programming (FP) techniques. Numerical results demonstrate that our approach achieves sensing performance gains of up to 20 dB and significantly reduces the CRLB.
3.
Wang, Zhipeng; Ng, Soon Xin; El-Hajjar, Mohammed
A 3D Spatial Information Compression Based Deep Reinforcement Learning Technique for UAV Path Planning in Cluttered Environments Journal Article
In: IEEE Open Journal of Vehicular Technology, vol. 6, pp. 647–661, 2025, ISSN: 2644-1330.
Abstract | Links | BibTeX | Tags: 3D path planning, 3D spatial information compression, Autonomous aerial vehicles, Classification algorithms, Convergence, deep reinforcement learning, Navigation, Path planning, Principal component analysis, Search problems, Solid modeling, Three-dimensional displays, Training, training efficiency, unmanned aerial vehicles
@article{wang_3d_2025,
title = {A 3D Spatial Information Compression Based Deep Reinforcement Learning Technique for UAV Path Planning in Cluttered Environments},
author = {Zhipeng Wang and Soon Xin Ng and Mohammed El-Hajjar},
url = {https://ieeexplore.ieee.org/document/10878448},
doi = {10.1109/OJVT.2025.3540174},
issn = {2644-1330},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Open Journal of Vehicular Technology},
volume = {6},
pages = {647–661},
abstract = {Unmanned aerial vehicles (UAVs) can be considered in many applications, such as wireless communication, logistics transportation, agriculture and disaster prevention. The flexible maneuverability of UAVs also means that the UAV often operates in complex 3D environments, which requires efficient and reliable path planning system support. However, as a limited resource platform, the UAV systems cannot support highly complex path planning algorithms in lots of scenarios. In this paper, we propose a 3D spatial information compression (3DSIC) based deep reinforcement learning (DRL) algorithm for UAV path planning in cluttered 3D environments. Specifically, the proposed algorithm compresses the 3D spatial information to 2D through 3DSIC, and then combines the compressed 2D environment information with the current UAV layer spatial information to train UAV agents for path planning using neural networks. Additionally, the proposed 3DSIC is a plug and use module that can be combined with various DRL frameworks such as Deep Q-Network (DQN) and deep deterministic policy gradient (DDPG). Our simulation results show that the training efficiency of 3DSIC-DQN is 4.028 times higher than that directly implementing DQN in a 100 textbackslashtimes 100 textbackslashtimes 50 3D cluttered environment. Furthermore, the training efficiency of 3DSIC-DDPG is 3.9 times higher than the traditional DDPG in the same environment. Moreover, 3DSIC combined with fast recurrent stochastic value gradient (FRSVG), which can be considered as the most state-of-the-art DRL algorithm for UAV path planning, exhibits 2.35 times faster training speed compared with the original FRSVG algorithm.},
keywords = {3D path planning, 3D spatial information compression, Autonomous aerial vehicles, Classification algorithms, Convergence, deep reinforcement learning, Navigation, Path planning, Principal component analysis, Search problems, Solid modeling, Three-dimensional displays, Training, training efficiency, unmanned aerial vehicles},
pubstate = {published},
tppubtype = {article}
}
Unmanned aerial vehicles (UAVs) can be considered in many applications, such as wireless communication, logistics transportation, agriculture and disaster prevention. The flexible maneuverability of UAVs also means that the UAV often operates in complex 3D environments, which requires efficient and reliable path planning system support. However, as a limited resource platform, the UAV systems cannot support highly complex path planning algorithms in lots of scenarios. In this paper, we propose a 3D spatial information compression (3DSIC) based deep reinforcement learning (DRL) algorithm for UAV path planning in cluttered 3D environments. Specifically, the proposed algorithm compresses the 3D spatial information to 2D through 3DSIC, and then combines the compressed 2D environment information with the current UAV layer spatial information to train UAV agents for path planning using neural networks. Additionally, the proposed 3DSIC is a plug and use module that can be combined with various DRL frameworks such as Deep Q-Network (DQN) and deep deterministic policy gradient (DDPG). Our simulation results show that the training efficiency of 3DSIC-DQN is 4.028 times higher than that directly implementing DQN in a 100 textbackslashtimes 100 textbackslashtimes 50 3D cluttered environment. Furthermore, the training efficiency of 3DSIC-DDPG is 3.9 times higher than the traditional DDPG in the same environment. Moreover, 3DSIC combined with fast recurrent stochastic value gradient (FRSVG), which can be considered as the most state-of-the-art DRL algorithm for UAV path planning, exhibits 2.35 times faster training speed compared with the original FRSVG algorithm.
4.
Wang, Zihao; El-Hajjar, Mohammed; Yang, Lie-Liang
Orbital Angular Momentum for Wireless Communications: Key Performance Indicators and Performance Comparison Journal Article
In: IEEE Access, vol. 13, pp. 80889–80913, 2025, ISSN: 2169-3536.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
5.
Maity, Priyanka; Harish, Deepika; Srivastava, Suraj; Jagannatham, Aditya K.; Hanzo, Lajos
Variational Bayesian Learning for 3-D Localization of Extended Targets in mmWave MIMO OFDM ISAC Systems Journal Article
In: IEEE Open Journal of the Communications Society, vol. 6, pp. 4421–4436, 2025, ISSN: 2644-125X.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.