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Energy-Efficient Velocity Profile Optimization for Movable Antenna-Enabled Sensing Systems

Jiannan Wang, Yuyi Mao, Xianghao Yu, Ying-Jun Angela Zhang

Abstract

Movable antennas (MAs) enable the reconfiguration of array geometry within a bounded region to exploit sub-wavelength spatial degrees of freedom in wireless communication and sensing systems. However, most prior research has predominantly focused on the communication and sensing performance, overlooking the mechanical power consumption inherent in antenna movement. To bridge this gap, this paper investigates a velocity profile optimization framework for MA-assisted direction-of-arrival (DoA) estimation, explicitly balancing sensing accuracy with mechanical energy consumption of MAs. We first establish a Newtonian-based mechanical energy model, and formulate a functional optimization problem for sensing energy efficiency (EE) maximization. By applying the calculus of variations, this formulation is transformed into an infinite-dimensional problem defined by the Euler-Lagrange equation. To solve it, we propose a spectral discretization framework based on the Galerkin method, which expands the velocity profile over a sinusoidal basis. In the regime where energy consumption is dominated by linear damping, we prove that the optimal velocity profile follows a closed-form sinusoidal shape. For more general scenarios involving strong nonlinear aerodynamic drag, we leverage the Markov-Lukács theorem to transform the kinematic constraints into strictly convex sum-of-squares (SOS) conditions. Consequently, the infinite-dimensional problem is reformulated as a tractable finite-dimensional nonlinear algebraic system, which is solved by a two-layer algorithm combining Dinkelbach's method with successive convex approximation (SCA). Numerical results demonstrate that our optimized velocity profile significantly outperforms baselines in terms of EE across various system configurations. Insights into the optimized velocity profiles and practical design guidelines are also provided.

Energy-Efficient Velocity Profile Optimization for Movable Antenna-Enabled Sensing Systems

Abstract

Movable antennas (MAs) enable the reconfiguration of array geometry within a bounded region to exploit sub-wavelength spatial degrees of freedom in wireless communication and sensing systems. However, most prior research has predominantly focused on the communication and sensing performance, overlooking the mechanical power consumption inherent in antenna movement. To bridge this gap, this paper investigates a velocity profile optimization framework for MA-assisted direction-of-arrival (DoA) estimation, explicitly balancing sensing accuracy with mechanical energy consumption of MAs. We first establish a Newtonian-based mechanical energy model, and formulate a functional optimization problem for sensing energy efficiency (EE) maximization. By applying the calculus of variations, this formulation is transformed into an infinite-dimensional problem defined by the Euler-Lagrange equation. To solve it, we propose a spectral discretization framework based on the Galerkin method, which expands the velocity profile over a sinusoidal basis. In the regime where energy consumption is dominated by linear damping, we prove that the optimal velocity profile follows a closed-form sinusoidal shape. For more general scenarios involving strong nonlinear aerodynamic drag, we leverage the Markov-Lukács theorem to transform the kinematic constraints into strictly convex sum-of-squares (SOS) conditions. Consequently, the infinite-dimensional problem is reformulated as a tractable finite-dimensional nonlinear algebraic system, which is solved by a two-layer algorithm combining Dinkelbach's method with successive convex approximation (SCA). Numerical results demonstrate that our optimized velocity profile significantly outperforms baselines in terms of EE across various system configurations. Insights into the optimized velocity profiles and practical design guidelines are also provided.

Paper Structure

This paper contains 23 sections, 4 theorems, 64 equations, 8 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Signal Model
  4. CRB of 1-D DoA Estimation
  5. Problem Formulation
  6. Mechanical Energy Consumption Model
  7. Optimization Problem Statement
  8. Functional Analysis and Discretization
  9. Variational Derivative and Euler-Lagrange Equation
  10. Galerkin-Based Spectral Discretization
  11. Constrained Optimization Framework
  12. Reformulation of the Constraint $v(t)\ge0$
  13. Reformulation of the Constraint $v(t)\le V_{\max}$
  14. Optimization Problem Formulation
  15. Simulations
  16. ...and 8 more sections

Key Result

Proposition 1

The variance functional $\mathcal{V}(v)$ defined in eq: var can be reformulated as an explicit kernel representation in terms of $v(t)$, which is given by where the kernel function $K(u,s)$ is defined as Proof: Please see Appendix sec:app1.

Figures (8)

  • Figure 1: Diagram of a bi-static wireless sensing system with a single MA.
  • Figure 2: Comparison of the feasible regions in terms of $\mathbf{c}$ for the velocity constraint $|v(t)| \le V_{\max}$ with $N=2$, $T=1$, and $V_{\max}=1$.
  • Figure 3: Sensing EE versus the truncation order $N$ for the proposed method.
  • Figure 4: Comparison of the optimized coefficients $\mathbf{c}$ under different $\alpha_2$.
  • Figure 5: Comparison of optimized velocity profiles under different methods.
  • ...and 3 more figures

Theorems & Definitions (5)

  • Proposition 1
  • Lemma 1: Mercer Series of the Variance Kernel van2004detection
  • Lemma 2: Theorem of Markov-Lukács szeg1939orthogonal
  • Lemma 3: Gram Matrix Representation of SOS Polynomials 10383828roh2006discrete
  • Remark 1