Closed-Loop Integrated Sensing, Communication, and Control for Efficient Drone Flight

Abstract

Low-altitude wireless networks (LAWN) require drones to follow specific trajectories controlled by ground base stations (GBSs). However, given complex low-altitude channel conditions and limited spectrum and power resources, sensing errors and wireless link unreliability cannot be ignored, leading to trajectory deviations that threaten flight safety. To address this issue, this paper proposes an integrated sensing-communication-control (ISCC) closed-loop trajectory tracking approach, aiming to reveal the coupling mechanisms among communication, sensing, and control during drone flight. In detail, we incorporate sensing errors in trajectory state estimation, packet losses in control command transmission, and finite blocklength transmission effects into the closed-loop dynamics. First, through theoretical analysis, we identify the dominant role of the time-frequency resources allocated to control in ensuring system stability and derive a lower bound on the resources required to guarantee stable operation. Second, to minimize tracking error, we formulate a time-frequency resource allocation optimization problem for the sensing, communication, and control components, subject to constraints on communication rate and closed-loop stability. Accordingly, a solution algorithm based on successive convex approximation is proposed. Third, simulation results indicate that once stability is ensured, system performance is primarily determined by sensing accuracy, with the trajectory tracking error exhibiting an approximately linear dependence on the position error bound. Finally, it is shown that the proposed ISCC scheme avoids trajectory divergence under FBL transmission compared with ISCC designs ignoring control packet loss, and could achieve decimeter-level average tracking accuracy, reducing the error to only 17.37% of that observed in the baseline global navigation satellite system scheme.

Figures (9)

• Figure 1: Illustration of ISCC-based drone trajectory tracking.
• Figure 2: Illustration of the ISAC-enabled ISCC closed-loop operation.
• Figure 3: (a) Average VEB $\overline{\mathrm{VEB}}$ versus sensing resource fraction $\alpha_{\rm sen}$ for different SNR levels; (b) Average PEB $\overline{\mathrm{PEB}}$ versus sensing resource fraction $\alpha_{\rm sen}$ for different SNR levels.
• Figure 4: Stability and reliability characteristics of the control loop: (a) Spectral radius $\rho(\mathbf{M})$ of the closed-loop covariance evolution matrix versus the control packet loss rate $\varepsilon_{\rm ctrl}$; (b) Control packet loss rate $\varepsilon_{\rm ctrl}$ as a function of the control resource fraction $\alpha_{\rm ctrl}$; (c) Spectral radius $\rho(\mathbf{M})$ of the closed-loop covariance evolution matrix versus the control resource fraction $\alpha_{\mathrm{ctrl}}$.
• Figure 5: Impact of sensing and control resource allocation on the long-term average LQG cost in the ISCC closed-loop system. (a) LQG cost versus control resource fraction $\alpha_{\mathrm{ctrl}}$ and sensing resource fraction $\alpha_{\mathrm{sen}}$. (b) LQG cost versus control resource fraction $\alpha_{\mathrm{ctrl}}$ for different sensing resource fractions $\alpha_{\mathrm{sen}}$. (c) LQG cost versus sensing resource fraction $\alpha_{\mathrm{sen}}$ for different control resource fractions $\alpha_{\mathrm{ctrl}}$.

Theorems & Definitions (2)

• Remark 1
• Remark 2