# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 1 Introduction  
Unmanned aerial vehicles (UAV) were originally developed and used for military purposes, but as drone technology gradually develops, drones are becoming more popular and are being used in various fields [1]. There are various advantages in utilizing multiple drones as a swarm [2]. Since the UAV network generally covers a wide range of maps in a variety of environments, it is essential to ensure a strong connection between objects within the drone squadron. This connection allows the remote ground control unit to receive control messages and UAVs in the swarm to transmit messages to each other. However, the traditional routing algorithms have limitations because they do not guarantee several UAV characteristics, such as mobility, communication instability, lightweight equipment, and wireless communication [3]. In order to prevent data omission when transmitting and receiving UAV data and to reduce data transmission delays, it is necessary to establish an optimal routing algorithm suitable for the UAV environment [4]. In this case, it is possible to form an ad hoc network in which data can be transmitted via multiple unmanned moving objects [5]. Ad hoc routing methods use a UAV exchange link or node state data in the UAV network to obtain a routing path in order to send information to a specific UAV [6]. Ad hoc is suitable for an environment that utilizes a mobile host due to its mobility feature. It enables networks between mobile hosts in an environment where centralized management is not supported [7]. However, ad hoc routing can lead to unnecessary delays and frequent network partitioning, as each UAV is extremely mobile and the communication link is unstable, resulting in constantly changing routing paths for each pair of UAVs [8]. Location-based routing in ad hoc networks has the advantage of enabling efficient routing by using the geographical location information of nodes [9]. In this process, all transmission nodes deliver the data packet to their nearest neighbor node as the destination node. The periodic exchange of location information between UAVs is required to perform reliable routing. However, this process has two problems: the first is unnecessary energyconsumption by nodes in areas where there is no data transfer, and the second is the inaccuracy of neighbor node location information at the time of data transfer [10]. The location-based routing algorithm proposed in this paper can cope with the large amount of communication, irregular mobility, and wide range of communication that occurs when operating multiple UAVs. When multiple UAVs form a network with each other, numerous communications between UAVs can occur, which can burden the network and degrade the quality of communication [11]. In addition, UAVs have a characteristic in that the type of network and transmission path can be flexibly changed according to a given environment and task due to their high mobility [12]. In this case, additional communication between UAVs takes place. At this time, since each UAV establishes a new communication path in a different communication environment, it becomes more burdensome to build a network. If some UAVs in the cluster are accidentally located outside the network range, cluster network communication is disrupted and errors occur during transmission. In this paper, we propose a routing algorithm called the geolocation ad hoc network (GLAN) that minimizes the communication burden and adapts to the changing communication environment. We construct an adaptive geographic ad hoc routing algorithm for UAV swarms based on location information. This routing protocol communicates through broadcasting, but by forwarding range settings learned using reinforcement learning (RL), routes are designed by connecting only the necessary links for data transmission, as shown in Figure 1.  
The traditional routing method, LAR, is finite because it estimates the expected area using the last known destination location to reduce the search space for the desired route [13].