Quantum-Assisted Adaptive Beamforming in UASs Network: Enhancing Airborne Communication via Collaborative UASs for NextG IoT

TL;DR

This work tackles beam distortions caused by hovering in distributed UAS networks by introducing quantum-inspired adaptive beamforming frameworks. The QSUB method performs a Grover-like quantum search to select optimal links and reconfigure active UASs, while Q-P-LL augments QSUB with Nelder–Mead optimization to mitigate AoA estimation errors. Compared to classical MRT, the quantum approaches offer improved robustness and scalability, particularly under AoA uncertainty, and do not require explicit channel state information. The proposed frameworks are applicable to both classical and quantum computing architectures, highlighting their potential for next-generation airborne IoT deployments.

Abstract

This paper introduces a novel quantum-based method for dynamic beamforming and re-forming in Unmanned Aircraft Systems (UASs), specifically addressing the critical challenges posed by the unavoidable hovering characteristics of UAVs. Hovering creates significant beam path distortions, impacting the reliability and quality of distributed beamforming in airborne networks. To overcome these challenges, our Quantum Search for UAS Beamforming (QSUB) employs quantum superposition, entanglement, and amplitude amplification. It adaptively reconfigures beams, enhancing beam quality and maintaining robust communication links in the face of rapid UAS state changes due to hovering. Furthermore, we propose an optimized framework, Quantum-Position-Locked Loop (Q-P-LL), that is based on the principle of the Nelder-Mead optimization method for adaptive search to reduce prediction error and improve resilience against angle-of-arrival estimation errors, crucial under dynamic hovering conditions. We also demonstrate the scalability of the system performance and computation complexity by comparing various numbers of active UASs. Importantly, QSUB and Q-P-LL can be applied to both classical and quantum computing architectures. Comparative analyses with conventional Maximum Ratio Transmission (MRT) schemes demonstrate the superior performance and scalability of our quantum approaches, marking significant advancements in the next-generation Internet of Things (IoT) applications requiring reliable airborne communication networks.

Figures (11)

• Figure 1: Illustration of the hovering impact and beam distortion.
• Figure 2: UASs arrangement in a ball geometry.
• Figure 3: Illustration of the hovering impact and beam distortion.
• Figure 4: Encoding classical information into quantum states using amplitude encoding and entanglement. In this mapping of classical to the quantum state, the quantum circuit is constructed assuming $r = \Vert \mathbf{P} \Vert = 1$ with hovering resolution $r_r = 0.25$ m whereas $\theta \in [0, \pi]$ and $\phi \in [0, 2\pi)$ have hovering resolutions, $r_\theta$ and $r_\phi$, of $45$ degrees.
• Figure 5: QSUB routine: Quantum circuit to implement the search algorithm for optimal UASs placement prediction.

Theorems & Definitions (3)

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