1.
Nafees, Muhammad; Baniasadi, Mohammadamin; Hopgood, James R.; Safari, Majid; Thompson, John S.
Integrated Sensing and Communication for UAV Trajectory Optimization in Mixed FSO-RF Networks in Dynamic Weather Conditions Proceedings Article
In: 2025 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, 2025, ISSN: 1558-2612, (ISSN: 1558-2612).
Abstract | Links | BibTeX | Tags: 6G mobile communication, Autonomous aerial vehicles, Backhaul networks, free-space optical (FSO), Integrated sensing and communication, Meteorology, millimeter wave (mmWave), Millimeter wave communication, Optical attenuators, Optical feedback, Optical integrated sensing and communication (O-ISAC), Optical sensors, Sixth-generation (6G), Throughput, unmanned aerial vehicles (UAVs)
@inproceedings{nafees_integrated_2025,
title = {Integrated Sensing and Communication for UAV Trajectory Optimization in Mixed FSO-RF Networks in Dynamic Weather Conditions},
author = {Muhammad Nafees and Mohammadamin Baniasadi and James R. Hopgood and Majid Safari and John S. Thompson},
url = {https://ieeexplore.ieee.org/document/10978163},
doi = {10.1109/WCNC61545.2025.10978163},
issn = {1558-2612},
year = {2025},
date = {2025-03-01},
urldate = {2025-10-08},
booktitle = {2025 IEEE Wireless Communications and Networking Conference (WCNC)},
pages = {1–6},
abstract = {Integrated sensing and communication (ISAC) is expected to transform data transmission and real-time sensing, enhancing sixth-generation (6G) networks. Free-space optical (FSO) communication is a key 6G backhaul solution, complementing radio frequency (RF) technologies like millimeter wave (mmWave) for improved network reliability. However, adverse weather can significantly reduce FSO link reliability due to atmospheric attenuation. Such adverse weather conditions also increase the level of back-scattered light, potentially enabling the real-time sensing of the atmospheric channel gain at the transmitter side. Therefore, this paper proposes a novel optical ISAC (O-ISAC) framework, where the back-scattered light from the FSO communication signal is used as the sensing feedback signal. This O-ISAC framework is analyzed considering a single-cell network aided by an unmanned aerial vehicle (UAV) to support edge users. The UAV is connected to the gateway via a FSO backhaul link while estimating the FSO channel gain based on the back-scattered light and dynamically optimizing its trajectory. The aim of this adaptive O-ISAC system is to maximize the end-to-end network throughput of the edge users while considering FSO backhaul capacity and the UAV's directional antenna beamwidth and bandwidth allocation. Numerical results demonstrate that UAV can effectively optimize its trajectory by adjusting the antenna beamwidth and downlink bandwidth allocation at different weather conditions. The proposed framework is tested using hourly visibility data from Edinburgh, demonstrating that optical channel sensing is crucial for the system's overall performance.},
note = {ISSN: 1558-2612},
keywords = {6G mobile communication, Autonomous aerial vehicles, Backhaul networks, free-space optical (FSO), Integrated sensing and communication, Meteorology, millimeter wave (mmWave), Millimeter wave communication, Optical attenuators, Optical feedback, Optical integrated sensing and communication (O-ISAC), Optical sensors, Sixth-generation (6G), Throughput, unmanned aerial vehicles (UAVs)},
pubstate = {published},
tppubtype = {inproceedings}
}
Integrated sensing and communication (ISAC) is expected to transform data transmission and real-time sensing, enhancing sixth-generation (6G) networks. Free-space optical (FSO) communication is a key 6G backhaul solution, complementing radio frequency (RF) technologies like millimeter wave (mmWave) for improved network reliability. However, adverse weather can significantly reduce FSO link reliability due to atmospheric attenuation. Such adverse weather conditions also increase the level of back-scattered light, potentially enabling the real-time sensing of the atmospheric channel gain at the transmitter side. Therefore, this paper proposes a novel optical ISAC (O-ISAC) framework, where the back-scattered light from the FSO communication signal is used as the sensing feedback signal. This O-ISAC framework is analyzed considering a single-cell network aided by an unmanned aerial vehicle (UAV) to support edge users. The UAV is connected to the gateway via a FSO backhaul link while estimating the FSO channel gain based on the back-scattered light and dynamically optimizing its trajectory. The aim of this adaptive O-ISAC system is to maximize the end-to-end network throughput of the edge users while considering FSO backhaul capacity and the UAV's directional antenna beamwidth and bandwidth allocation. Numerical results demonstrate that UAV can effectively optimize its trajectory by adjusting the antenna beamwidth and downlink bandwidth allocation at different weather conditions. The proposed framework is tested using hourly visibility data from Edinburgh, demonstrating that optical channel sensing is crucial for the system's overall performance.
2.
Gupta, Awadhesh; Singh, Jitendra; Srivastava, Suraj; Jagannatham, Aditya K.; Hanzo, Lajos
Bayesian Learning Aided Parameter Estimation and Joint Beamformer Design in mmWave MIMO-OFDM ISAC Systems Journal Article
In: IEEE Transactions on Communications, pp. 1–1, 2025, ISSN: 1558-0857.
Abstract | Links | BibTeX | Tags: Array signal processing, Bayes methods, dual-functional radar-communication (DFRC), Estimation, hybrid analog-digital (HAD) beamforming, Integrated sensing and communication (ISAC), millimeter wave (mmWave), Millimeter wave communication, MIMO, OFDM, Parameter estimation, Radar, Radar cross-sections, Radio frequency, sparse Bayesian learning (SBL)
@article{gupta_bayesian_2025,
title = {Bayesian Learning Aided Parameter Estimation and Joint Beamformer Design in mmWave MIMO-OFDM ISAC Systems},
author = {Awadhesh Gupta and Jitendra Singh and Suraj Srivastava and Aditya K. Jagannatham and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/11030617},
doi = {10.1109/TCOMM.2025.3578813},
issn = {1558-0857},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Communications},
pages = {1–1},
abstract = {A three-dimensional (3D) sparse signal recovery problem formulation is conceived for delay, Doppler, and angular (DDA) domain target parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems relying on a hybrid beamforming architecture. Subsequently, a 3D-sparse Bayesian learning (3D-BL) algorithm is proposed to jointly estimate the angular, range, velocity, and radar cross-section (RCS) parameters of the targets. Furthermore, an uplink beamformer is designed for the user equipment (UE) to alleviate the complexity of uplink parameter estimation at the dual-functional radar-communication (DFRC) base station (BS) by eliminating the need for angle of departure (AoD) estimation. Additionally, a Bayesian alternating minimization (BAT-MIN) algorithm is constructed for the designing of a DFRC waveform, enabling the simultaneous generation of beams toward both the radar targets and the UE. Furthermore, the sparse Bayesian learning lower bound (SBL-LB) and the Bayesian Cramér-Rao lower bound (BCRLB) are derived to serve as benchmarks for estimation performance. Finally, simulation results are presented to showcase the enhanced performance of the proposed methodologies in terms of multiple performance metrics when contrasted both to the existing sparse recovery techniques and to conventional non-sparse parameter estimation algorithms. The simulation outcomes unequivocally demonstrate the commendable performance of the proposed 3D-BL estimation methodology, approaching closely to the SBL-LB. Notably, this approach exhibits a substantial gain of at least 5 dB when compared to alternative techniques. Additionally, the introduced BAT-MIN beamformer emerges as a highly competitive solution, closely approximating the capabilities of a fully digital beamformer while maintaining a noteworthy minimum advantage over its contemporaries. These findings underscore the significance and efficacy of the proposed techniques in the context of advanced signal processing and beamforming.},
keywords = {Array signal processing, Bayes methods, dual-functional radar-communication (DFRC), Estimation, hybrid analog-digital (HAD) beamforming, Integrated sensing and communication (ISAC), millimeter wave (mmWave), Millimeter wave communication, MIMO, OFDM, Parameter estimation, Radar, Radar cross-sections, Radio frequency, sparse Bayesian learning (SBL)},
pubstate = {published},
tppubtype = {article}
}
A three-dimensional (3D) sparse signal recovery problem formulation is conceived for delay, Doppler, and angular (DDA) domain target parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems relying on a hybrid beamforming architecture. Subsequently, a 3D-sparse Bayesian learning (3D-BL) algorithm is proposed to jointly estimate the angular, range, velocity, and radar cross-section (RCS) parameters of the targets. Furthermore, an uplink beamformer is designed for the user equipment (UE) to alleviate the complexity of uplink parameter estimation at the dual-functional radar-communication (DFRC) base station (BS) by eliminating the need for angle of departure (AoD) estimation. Additionally, a Bayesian alternating minimization (BAT-MIN) algorithm is constructed for the designing of a DFRC waveform, enabling the simultaneous generation of beams toward both the radar targets and the UE. Furthermore, the sparse Bayesian learning lower bound (SBL-LB) and the Bayesian Cramér-Rao lower bound (BCRLB) are derived to serve as benchmarks for estimation performance. Finally, simulation results are presented to showcase the enhanced performance of the proposed methodologies in terms of multiple performance metrics when contrasted both to the existing sparse recovery techniques and to conventional non-sparse parameter estimation algorithms. The simulation outcomes unequivocally demonstrate the commendable performance of the proposed 3D-BL estimation methodology, approaching closely to the SBL-LB. Notably, this approach exhibits a substantial gain of at least 5 dB when compared to alternative techniques. Additionally, the introduced BAT-MIN beamformer emerges as a highly competitive solution, closely approximating the capabilities of a fully digital beamformer while maintaining a noteworthy minimum advantage over its contemporaries. These findings underscore the significance and efficacy of the proposed techniques in the context of advanced signal processing and beamforming.