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Decentralized Semantic Communication and Cooperative Tracking Control for a UAV Swarm over Wireless MIMO Fading Channels

Minjie Tang, Chenyuan Feng, Tony Q. S. Quek

TL;DR

This work proposes a cooperative semantic communication-control framework that selectively transmits the most informative control data in a UAV swarm, and substantially outperforms existing methods in both tracking accuracy and communication efficiency.

Abstract

This paper investigates the semantic communication and cooperative tracking control for an UAV swarm comprising a leader UAV and a group of follower UAVs, all interconnected via unreliable wireless multiple-input-multiple-output (MIMO) channels. Initially, we develop a dynamic model for the UAV swarm that accounts for both the internal interactions among the cooperative follower UAVs and the imperfections inherent in the MIMO channels that interlink the leader and follower UAVs. Building on this model, we incorporate the power costs of the UAVs and formulate the communication and cooperative tracking control challenge as a drift-plus-penalty optimization problem. We then derive a closed-form optimal solution that maintains a decentralized semantic architecture, dynamically adjusting to the tracking error costs and local channel conditions within the swarm. Employing Lyapunov drift analysis, we establish closed-form sufficient conditions for the stabilization of the UAV swarm's tracking performance. Numerical results demonstrate the significant enhancements in our proposed scheme over various state-of-the-art methods.

Decentralized Semantic Communication and Cooperative Tracking Control for a UAV Swarm over Wireless MIMO Fading Channels

TL;DR

This work proposes a cooperative semantic communication-control framework that selectively transmits the most informative control data in a UAV swarm, and substantially outperforms existing methods in both tracking accuracy and communication efficiency.

Abstract

This paper investigates the semantic communication and cooperative tracking control for an UAV swarm comprising a leader UAV and a group of follower UAVs, all interconnected via unreliable wireless multiple-input-multiple-output (MIMO) channels. Initially, we develop a dynamic model for the UAV swarm that accounts for both the internal interactions among the cooperative follower UAVs and the imperfections inherent in the MIMO channels that interlink the leader and follower UAVs. Building on this model, we incorporate the power costs of the UAVs and formulate the communication and cooperative tracking control challenge as a drift-plus-penalty optimization problem. We then derive a closed-form optimal solution that maintains a decentralized semantic architecture, dynamically adjusting to the tracking error costs and local channel conditions within the swarm. Employing Lyapunov drift analysis, we establish closed-form sufficient conditions for the stabilization of the UAV swarm's tracking performance. Numerical results demonstrate the significant enhancements in our proposed scheme over various state-of-the-art methods.

Paper Structure

This paper contains 18 sections, 3 theorems, 27 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Dynamic Model
  4. Wireless Communication Model
  5. Performance Metric
  6. Problem Formulation and Proposed Method
  7. Decentralized Lyapunov Optimization Formulation
  8. Decentralized Semantic Solution
  9. Algorithm Design
  10. Sufficient Condition for Tracking Stability
  11. Numerical Results
  12. Impact of the Number of UAVs
  13. Impact of the Number of Transmission Antennas
  14. Impact of Averaged Transmission Power at the leader UAV
  15. Conclusions
  16. ...and 3 more sections

Key Result

Theorem 1

(Lyapunov Drift) Let the singular value decomposition (SVD) of $\mathbf{A}$ and $\mathbf{G}$ be $\mathbf{A}=\mathbf{U}_1\Pi_1\mathbf{V}_1^T$ and $\mathbf{G}=\mathbf{U}_2\Pi_2\mathbf{V}_2^T$, respectively, where $\mathbf{U}_i\in\mathbb{R}^{9M\times 9M}$ and $\mathbf{V}_i\in\mathbb{R}^{9M\times 9M}$ a

Figures (4)

  • Figure 1: Typical architecture of a UAV swarm over the wireless network.
  • Figure 2: Averaged tracking error cost versus number of follower UAVs where $N_t=12$ and the averaged transmission power is 8 dBW.
  • Figure 3: Averaged tracking error cost versus number of transmission antennas where $M=12$ and the averaged transmission power is 8 dBW.
  • Figure 4: Averaged tracking error cost versus averaged transmission power where $M=N_t=12$.

Theorems & Definitions (3)

  • Theorem 1
  • Theorem 2
  • Theorem 3