Sensing, Communication, and Control Co-design for Energy Efficient Satellite-UAV Networks

TL;DR

The paper addresses timely data collection from remote IoT devices by proposing a Satellite-UAV NTN framework where a UAV is remotely controlled via UAV-to-Satellite links. It introduces a joint sensing, communication, and control co-design that maximizes UAV energy efficiency by optimizing trajectory (via a Deep Q-Network), uplink power (in closed form), and sensing scheduling (via 1D search) under control stability and reliability constraints. Key contributions include the integration of LQR-based UAV control with GNSS and GD sensing, a tractable energy-efficiency objective $\eta_{EE}$, and an efficient sub-optimal algorithm whose components: 4.1 DQN trajectory, 4.2 power allocation, and 4.3 sensing scheduling, are validated through simulations. Results indicate data size has a greater impact on EE than transmission power and reveal the trade-offs among sensing interval, maximum power, and control performance, offering practical guidance for remote IoT data collection.

Abstract

Traditional terrestrial communication infrastructures often fail to collect the timely information from Internet of Thing (IoT) devices in remote areas. To address this challenge, we investigate a Satellite-unmanned aerial vehicles (UAV) integrated Non-terrestrial network (NTN), where the UAV is controlled by remote control center via UAV-to-Satellite connections. To maximize the energy efficiency (EE) of the UAV, we optimize the UAV trajectory, power allocation, and state sensing strategies, while guaranteing the control stability and communication reliability. This challenging problem is addressed using an efficient algorithm, incorporating a Deep Q-Network (DQN)-based trajectory determination, a closed form of power allocation, and one-dimensional searching for sensing. Numerical simulations are conducted to validate the effectiveness of our approach. The results showcase the data size of collection has a greater impact than transmission power, and reveal the relationship among sensing interval, communication maximum power and control performance. This study provides promising solutions and valuable insights for efficient data collection in remote IoT.

Figures (11)

• Figure 1: The scenario of remote UAV control for data collection.
• Figure 2: The structure of remote UAV control.
• Figure 3: The experiment scenarion and result of UAV localization.
• Figure 4: The scenario of remote UAV control for data collection.
• Figure 5: The timing diagram of the remote UAV control system.