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

TL;DR

This paper proposes an integrated sensing, communication, and control framework for energy-efficient Satellite-UAV networks, optimizing UAV trajectory, power, and sensing to improve remote IoT data collection.

Contribution

It introduces a novel joint optimization approach combining deep reinforcement learning, closed-form power allocation, and sensing strategies for energy-efficient satellite-UAV communication.

Findings

Data collection size impacts energy efficiency more than transmission power.

Optimal sensing intervals depend on communication power limits.

The proposed method improves remote IoT data collection efficiency.

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.