# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 1 Introduction  
Beyond fifth Generation (B5G) network demands ensuring accessibility for all types of users and devices. Drone's play a vital role in cooperating in high-speed communications and facilitating an extensive coverage of wireless networks. Flexible positioning, Line of Sight (LoS) connectivity, possibility of smart decision, and association with fixed communication systems are advantages of drones [1, 2]. In addition, numerous drones can provide specialized coverage for end devices in smart environments and unreachable areas. Expanding communication coverage has become an inevitable trend in developing smart cities, as the future B5G Internet of Things (IoT) networks need ultra-high coverage. Drone's are now one of the most effective ways for improving communication coverage due to the significant cost and time reduction involved in establishing additional base stations. Drone's communication differs from standard ground wireless communication in two significant ways. First, drones typically have a strong LoS connection with ground nodes (i.e., users and smart devices). Thus, drones provide better channel conditions than ground fading channels. Furthermore, drones can even predict the Channel State Information (CSI) of different drones in 3D positions based on ground node location information andcommunication performance. Second, the drone features 3D manoeuvrability, which enables full control. The drone can modify its height and position to serve and improve drone communication performance with users/devices in smart environments [3; 4; 5]. In [6], the authors proposed an architecture to maximize wireless coverage in areas while also increasing the number of drones. The research published in [7] introduced the best 3D drone usage to increase the number of land users without compromising the Quality of Service (QoS). The authors proposed a wireless network for an extensive ground network for multiple drone base station placements [8]. In [9], the authors recommended using Long Term Evolution (LTE)-Unlicensed for deploying drones as a base station to create wireless connections following a natural catastrophe. Due to damage to the ground LTE infrastructure, coverage is limited in such situations [10]. Recently, Machine Learning (ML) has played a vital role in improving communication network performance in B5G systems. It supports communications services in smart environments and enhances the connectivity, signal quality, and QoS [11]. Artificial intelligence and ML play a vital role in improving the connectivity and QoS during the mobility of drones, robots, and things for supporting B5G communication systems [12; 13]. ML-based techniques have been used extensively to optimize performance in drone communication [14]. The authors of [15] covered physical layer communication concerns for cellular-connected UAVs, a UAV deployment where UAVs act as users. However, the height at which the drone operates makes it susceptible to atmospheric disturbances. In [16], the authors proposed an ML algorithm for trajectory planning with an underlying device-to-device communication. An ML-based algorithm was proposed for Unmanned Aerial Vehicle (UAV) trajectory planning and was proposed in Non-Orthogonal Multiple Access (NOMA) systems [17]. Under high LoS propagation circumstances and heavy interference, an ML-based drone-to-ground LTE cellular network was explored in [18]. In [19], the authors applied an artificial neural network to estimate drone signal strength over smart cities. In this work, we apply the K-means algorithm, an unsupervised ML algorithm, in drone-user communication with atmospheric disturbances. The main contribution of this paper is to improve the performance of drone-users/device communication from signal quality analysis at the receiver. The work proposes the best possible error performance with given signal quality. Data rate and error rate values are observed for different environment types, such as suburban, urban, and rural locations and various rain rates.