Quantum-Position-Locked Loop: New Concept for Collaborative Beam Forming for UAV Swarm

Future developments in aerial communication infrastructures hold great potential for further enhancement through the use of synchronized, harmonious swarms of uncrewed aerial vehicles (UAVs), capable of performing high-precision tasks such as beamforming and distributed Multiple Input Multiple Output (MIMO), where multiple transmitting and receiving antennas are spread across different locations, rather than being concentrated in a single device or base station. Collaborative UAV swarm-assisted distributed beam forming faces significant electronic and mechanical challenges due to the random hovering effects. We propose a novel quantum computing method for distributed beam-reforming in hovering-impaired UAV-assisted systems. Introducing the concept of quantum-position-lock loop (Q-P-LL), we achieve real-time positioning and accurate tracing of UAVs through a novel quantum optimization of combined Grover/Nelder-Mead algorithms. Q-P-LL overcomes beam distortion caused by random hovering, dynamically collects real-time positioning information, and predicts joint positioning control signals for beam-reforming, which forms reliable aerial links by flexible and accessible UAV hardware. Our results demonstrates robust performance despite the existence of measurement errors in the Angle of Arrival (AoA) within 10 degrees. This allows us to maintain the accuracy of the position lock at an average of 2.5 cm among UAV swarm size of 4, 6, and 8, guaranteeing a stable beam with high precision.

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Publication Details

Journal
IEEE Wireless Communications Letters
Published
2024-11-27
DOI
https://doi.org/10.1109/lwc.2024.3508657
Citations
8
Primary Topic
Advanced Surface Polishing Techniques
Type
article
Field-Weighted Citation Impact
1.48
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article

Quantum-Position-Locked Loop: New Concept for Collaborative Beam Forming for UAV Swarm

Sudhanshu Arya, Ying Wang
8 citations
IEEE Wireless Communications Letters
Advanced Surface Polishing Techniques
1.48
article

Quantum-Position-Locked Loop: New Concept for Collaborative Beam Forming for UAV Swarm

Sudhanshu Arya, Ying Wang
article en
8 citations

Abstract

Future developments in aerial communication infrastructures hold great potential for further enhancement through the use of synchronized, harmonious swarms of uncrewed aerial vehicles (UAVs), capable of performing high-precision tasks such as beamforming and distributed Multiple Input Multiple Output (MIMO), where multiple transmitting and receiving antennas are spread across different locations, rather than being concentrated in a single device or base station. Collaborative UAV swarm-assisted distributed beam forming faces significant electronic and mechanical challenges due to the random hovering effects. We propose a novel quantum computing method for distributed beam-reforming in hovering-impaired UAV-assisted systems. Introducing the concept of quantum-position-lock loop (Q-P-LL), we achieve real-time positioning and accurate tracing of UAVs through a novel quantum optimization of combined Grover/Nelder-Mead algorithms. Q-P-LL overcomes beam distortion caused by random hovering, dynamically collects real-time positioning information, and predicts joint positioning control signals for beam-reforming, which forms reliable aerial links by flexible and accessible UAV hardware. Our results demonstrates robust performance despite the existence of measurement errors in the Angle of Arrival (AoA) within 10 degrees. This allows us to maintain the accuracy of the position lock at an average of 2.5 cm among UAV swarm size of 4, 6, and 8, guaranteeing a stable beam with high precision.

IEEE Wireless Communications LettersVol. 14(2)
Stevens Institute of Technology (US), Vellore Institute of Technology University (IN)
Openalex Percentile: Top 19%
Advanced Surface Polishing Techniques
1.48
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Quantum-Position-Locked Loop: New Concept for Collaborative Beam Forming for UAV Swarm — Sudhanshu Arya, Ying Wang · IEEE Wireless Communications Letters (2024) | TGRS Research Map | TGRS