## 1 Introduction  
Unmanned aerial vehicle (UAV) wireless communication has attracted a lot of attention in both academia and industry thanks to a lot of advantages (e.g., alleviating the limitation of the locality, reaching hazardous area, controllability, etc.) [1; 2; 3]; that is, it can provide a dynamic wireless access wherever one need. However, a critical practical concern for the UAV wireless communication is to realize a long range communication, which however is limited in practice due to the limited battery life, as well as low energy efficiency of the UAV. In addition, when multiple UAVs exist in practice, there might be crosstalk or interference that would further degrade the communication performance. To address these issues, we leverage the LoRa (Long Range) [4] modulation with a proper transmission technique (such as beamforming or power allocation) for the UAV wireless communication, which is the first attempt in the literature to the best of our knowledge. LoRa is one of the most popular and widely adopted low-power wide-area network (LPWAN) technologies developed for Internet-of-Things (IoT), because it enables long-distance communication with high battery/energy efficiency [5; 6]. Additionally, the transmission technique, such as beamforming or power allocation, is even synergetic when combined with LoRa, because if properly designed or optimized, the received signal power can be further enhanced (and, thus, enables a much longer communication). In the literature [7; 8; 9; 10; 11; 12], several beamforming or power allocation schemes were developedfor the non-UAV (i.e., traditional terrestrial) wireless communication based on the convex optimization-based approaches. Although these schemes can be extended to the UAV wireless communication (this will be presented in Section 2 as performance benchmarks for the proposed scheme), they generally require very high computational complexities, which is prohibitive in practice or even infeasible at the UAVs with limited computing capabilities, because the corresponding convex optimization problems (such as second-order cone programming (SOCP) and linear programming (LP)) requiring a non-negligible complexity need to be re-solved many times while the UAVs move. To address this critical complexity issue, an innovative and effective solution is urgently required. This motivated our work. In this paper, we consider a UAV wireless communication system where a base station (BS) equipped multi antennas communicates with multiple UAVs, each equipped with a single antenna, using the LoRa modulation. In this system, we propose novel and efficient schemes for beamforming design for downlink transmission from the BS to the UAVs and power allocation for uplink transmission from the UAVs to the BS, respectively, based on a deep learning (DL). To address the aforementioned complexity issue effectively and intelligently, we construct a deep neural network (DNN), such that the optimal value of the downlink beamforming or the uplink power allocation can be predicted very efficiently based on the channel state information (CSI). Numerical results demonstrate the effectiveness and efficiency of the proposed scheme. The remainder of this paper is organized as follows; The related work is described in Section 1.1. The system model and problem formulation are described in Section 1.2. The convex optimization-based conventional approaches and the proposed deep learning approach are presented in Section 2. The simulation results are presented in Section 3, and the paper is concluded in Section 4. _Notations_: Boldface upper and lower case letters are used to denote matrices and vectors, respectively. \(\exp(\cdot)\) denotes an exponential function. The transpose of vectors and matrices is denoted by \((\cdot)^{T}\), and we use \((\cdot)^{H}\) to denote Hermitian transpose. Additionally, \(\|\cdot\|\) and \(\preccurlyeq\) are used for denoting the Euclidean norm of vectors and component-wise inequality, respectively.