用语义通信提升无人机采集物联网图像效率
Semantic-Aware UAV Command and Control for Efficient IoT Data Collection
- 通过深度联合信源信道编码压缩图像，实现部分传输下的重建
- 采用双深度Q网络优化无人机飞行轨迹，在固定时间内提升图像质量
- 适合需要低带宽、实时响应的无人机物联网数据采集场景
无人机（UAV）已成为从物联网（IoT）设备中收集数据的关键技术。然而，资源受限与实时决策需求使高效数据采集面临挑战。本文提出一种融合语义通信与无人机指挥控制（C&C）的新框架，实现对物联网设备图像数据的高效采集。每个设备使用深度联合信源信道编码（DeepJSCC）生成紧凑的语义潜在表示，支持在部分传输条件下完成图像重建。基站（BS）通过发送加速度指令控制无人机轨迹，目标是在固定时间范围内，通过足够长时间靠近各设备，最大化重构图像的平均质量。为应对复杂权衡及控制信号延迟问题，将该问题建模为马尔可夫决策过程，并提出基于双深度Q学习（DDQN）的自适应飞行策略。仿真结果表明，本方法在设备覆盖率和语义重建质量方面均优于贪心算法与旅行商算法等基线方法。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler technology for data collection from Internet of Things (IoT) devices. However, effective data collection is challenged by resource constraints and the need for real-time decision-making. In this work, we propose a novel framework that integrates semantic communication with UAV command-and-control (C&C) to enable efficient image data collection from IoT devices. Each device uses Deep Joint Source-Channel Coding (DeepJSCC) to generate a compact semantic latent representation of its image to enable image reconstruction even under partial transmission. A base station (BS) controls the UAV's trajectory by transmitting acceleration commands. The objective is to maximize the average quality of reconstructed images by maintaining proximity to each device for a sufficient duration within a fixed time horizon. To address the challenging trade-off and account for delayed C&C signals, we model the problem as a Markov Decision Process and propose a Double Deep Q-Learning (DDQN)-based adaptive flight policy. Simulation results show that our approach outperforms baseline methods such as greedy and traveling salesman algorithms, in both device coverage and semantic reconstruction quality.
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