# MCST Scheme for UAV Systems over LoRa Networks
## 1 Introduction  
The evolution of today's wireless communication environment is heading to the 6G mobile communication system, which is also known as a fully digital and connected world with human-centric mobile communications. The networks in the 6G are expected to be highly reliable data-driven wireless networks with interconnected and intercommunicated devices, and the number of wireless devices is expected to increase dramatically. [PERSON] [1] described that the technologies such as the Internet of Things (IoT), artificial intelligence (AI), robots, and big data would play a major technology in the future communication system. In the realisation of the IoT system, low-power wide-area network (LPWAN) is the emerging wireless network technology designed for the communication ability among low-power, especially battery-powered devices over long geographical areas [2]. Long-range low-power wireless communications, commonly known as LoRa, is one of the standards of LPWAN and is well known for low-cost design with long-range and low power communication. Since the sensors and end devices in the IoT system are mainly battery-based and power-limited communication devices, LoRa works as the solution to optimize the consumed energy of the end devices in the network. For example, [PERSON] et al. designed an access protocol based on the time-division multiple access (TDMA) principles for LoRa-based IoT systems [3]. In addition, as the application of LoRa in LPWAN, plenty of research has been done in sensor networks and unmanned aerial vehicle (UAV), which is also called drone, networks [4].  
UAV is a flying vehicle without human pilots onboard, usually remotely controlled, and can be fully or partially autonomous. The UAV systems exhibit as an emergingtechnology with its benefits of line-of-sight link, dynamic deployment ability, link reliability and so on [5; 6]. As an example of the UAV system, [PERSON] et al. conduct the experiment of air-to-air communication by developing a drone location information sharing system over the LoRa network in [7]. Besides, they apply the machine learning technique in the model establishment for drone communication [8]. Similarly, [PERSON] et al. [9] also employ UAVs to integrate with the machine learning technique for providing aerial services, i.e., communication service and edge computing service, in the 6G mobile communication system. A different scenario of the UAV system, in particular, a hybrid communication architecture for UAV swarms, is proposed by combining the long-range communication protocol and IEEE 802.11s protocol in [10]. In the other work, [PERSON] et al. [11] evaluate the LoRa network performance with the UAV for disaster management. They proposed the decentralized topology control algorithm for the UAVs to ensure connectivity without being affected by node mobility. The authors in [12] also mention that the spectrum efficiency and energy efficiency of the communication can be significantly improved by utilizing UAV as the relay node to assist the cellular transmissions. Similarly, [PERSON] et al. [13] propose an energy-efficient data collection scheme over the LoRa network by employing the UAV as the gateway and show a significant improvement in energy consumption. However, UAV systems require low latency and high capacity transmission for resource sharing and performing mission-critical and time-critical applications. In this paper, we investigate the network capacity improvement when the mixture of concurrent and sequential transmission (MCST) scheme is used for remote identification and tracking of UAV system, which provides low-cost broadcast-based location information sharing between drones over the LoRa network.