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Rotatable Antenna Meets UAV: Towards Dual-Level Channel Reconfiguration Paradigm for ISAC

Shiying Chen, Guangji Chen, Long Shi, Qingqing Wu, Kang Wei

TL;DR

This work tackles ISAC with a UAV platform equipped with rotatable antennas to jointly manage large-scale path loss and small-scale channel coupling. By deriving closed-form optimal RA rotation $\phi^*(t)$ and MRT beamforming $\mathbf w^*(\mathbf q)$ given the UAV position, the authors decouple trajectory design from transceiver design and address both static and mobile UAV scenarios. For static UAVs, they obtain a closed-form hovering location and a piecewise rate–sensing expression, demonstrating non-convexity of the static region and gains via time-sharing. For mobile UAVs, they prove a hover–fly–hover (HFH) trajectory is globally optimal, reducing the optimization to a 2D hover-point search with a derived time-allocation rule, and show mobility enlarges the achievable S&C region in simulations. Overall, the dual-level reconfiguration yields significant improvements over benchmarks, highlighting the practical benefits of RA rotation and UAV mobility in ISAC systems.

Abstract

Integrated sensing and communication (ISAC) is viewed as a key enabler for future wireless networks by sharing the hardware and wireless resources between the functionalities of sensing and communication (S&C). Due to the shared wireless resources for both S&C, it is challenging to achieve a critical trade-off between these two integrated functionalities. To address this issue, this paper proposes a novel dual-level channel reconfiguration framework for ISAC by deploying rotatable antennas at an unmanned aerial vehicle (UAV), where both the large-scale path loss and the correlation of S&C channels can be proactively controlled, thereby allowing a flexible trade-off between S&C performance. To characterize the S&C tradeoff, we aim to maximize the communication rate by jointly optimizing the RA rotation, the transmit beamforming, and the UAV trajectory, subject to the given requirement of sensing performance. For the typical scenario of static UAV deployment, we introduce the concept of subspace correlation coefficient to derive closed-form solutions for the optimal RA rotation, transmit beamforming, and UAV hovering location. For the scenario of a fully mobile UAV, we prove that the optimal trajectory of a UAV follows a hover-fly-hover (HFH) structure, thereby obtaining its global optimal solution. Simulation results show that the proposed design significantly improves the achievable S&C trade-off region compared to benchmark schemes.

Rotatable Antenna Meets UAV: Towards Dual-Level Channel Reconfiguration Paradigm for ISAC

TL;DR

This work tackles ISAC with a UAV platform equipped with rotatable antennas to jointly manage large-scale path loss and small-scale channel coupling. By deriving closed-form optimal RA rotation and MRT beamforming given the UAV position, the authors decouple trajectory design from transceiver design and address both static and mobile UAV scenarios. For static UAVs, they obtain a closed-form hovering location and a piecewise rate–sensing expression, demonstrating non-convexity of the static region and gains via time-sharing. For mobile UAVs, they prove a hover–fly–hover (HFH) trajectory is globally optimal, reducing the optimization to a 2D hover-point search with a derived time-allocation rule, and show mobility enlarges the achievable S&C region in simulations. Overall, the dual-level reconfiguration yields significant improvements over benchmarks, highlighting the practical benefits of RA rotation and UAV mobility in ISAC systems.

Abstract

Integrated sensing and communication (ISAC) is viewed as a key enabler for future wireless networks by sharing the hardware and wireless resources between the functionalities of sensing and communication (S&C). Due to the shared wireless resources for both S&C, it is challenging to achieve a critical trade-off between these two integrated functionalities. To address this issue, this paper proposes a novel dual-level channel reconfiguration framework for ISAC by deploying rotatable antennas at an unmanned aerial vehicle (UAV), where both the large-scale path loss and the correlation of S&C channels can be proactively controlled, thereby allowing a flexible trade-off between S&C performance. To characterize the S&C tradeoff, we aim to maximize the communication rate by jointly optimizing the RA rotation, the transmit beamforming, and the UAV trajectory, subject to the given requirement of sensing performance. For the typical scenario of static UAV deployment, we introduce the concept of subspace correlation coefficient to derive closed-form solutions for the optimal RA rotation, transmit beamforming, and UAV hovering location. For the scenario of a fully mobile UAV, we prove that the optimal trajectory of a UAV follows a hover-fly-hover (HFH) structure, thereby obtaining its global optimal solution. Simulation results show that the proposed design significantly improves the achievable S&C trade-off region compared to benchmark schemes.
Paper Structure (10 sections, 5 theorems, 29 equations, 4 figures)

This paper contains 10 sections, 5 theorems, 29 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Proposed Solution
  4. Static UAV Deployment
  5. Proposed Solution
  6. Numerical Results
  7. Fully Mobile UAV Case
  8. Proposed Solution
  9. Numerical Results
  10. Conclusion

Key Result

Lemma 1

With the optimal beamforming vector, the maximum communication SNR is where $g(\rho) =\frac{\|\mathbf h_c\|^2}{\sigma_c^2\,\|\mathbf h_t\|^2}(\rho\sqrt{\tilde{\Gamma}}+\sqrt{1-\rho^2}\,\sqrt{\|\mathbf h_t\|^2 P_{\max}-\tilde{\Gamma}})^{\!2}$, and the correlation coefficient is $\rho = \frac{|\mathbf h_c^H\mathbf h_t|}{\|\mathbf h_c\|\,\|\mathbf h_t\|}\in[0,1]$.

Figures (4)

  • Figure 1: The schematic of RA-enabled ISAC systems.
  • Figure 2: Achievable rate versus sensing threshold under the static UAV deployment.
  • Figure 3: Illustration of the TS-bound and the static boundary induced by $C_f(x_I)$ and $C_f(x_F)$.
  • Figure 4: Achievable rate versus sensing threshold $\Gamma_{\mathrm{th}}$ under dynamic trajectories.

Theorems & Definitions (7)

  • Lemma 1
  • Theorem 1
  • Theorem 2
  • Proposition 1
  • Remark 1
  • Remark 2
  • Theorem 3