## 1 Introduction  
Increasing reported works identify that drones, also known as unmanned aerial vehicles, could and should be sufficiently utilized to work as aerial base stations in the upcoming 6G aerial vehicular networks, especially for providing emergency communication and flexible coverage [1, 2, 3]. Drone wireless communications are expected to provide flexible wireless access, particularly in situations where the conventional telecommunication infrastructure is not available or damaged [4, 5, 6]. The mature drone-aided wireless communications work on the conventional radio frequency, where the available spectrum resource is almost exhausted [7, 8, 9]. Objectively, light-emitting diode (LED) based lighting devices are ubiquitously integrated into these commercially available drone platforms for the general purposes of illumination and indication [10, 11, 12, 13]. Impressively, for further enhancing and diversifying the wireless air interface capability of the above 6G aerial vehicular networks, the LED-based visible light communication (VLC) technologies are increasingly introduced and explored in the rapidly developing drone communications [14, 15, 16, 17, 18]. Typically, the authors in [19] propose one framework to optimize the trajectories of VLC-enabled UAVs using a particle swarm optimization (PSO) algorithm, and the superior performance of jointly maximizing the sum-rate and rate fairness of the users and minimizing the power consumption has been shown as well. Moreover, the study in [20] presented a new time scale structure to study the effect of constant acceleration on the coordinated multipoint UAV-enabled VLC networks. In this proposed scheme, a novel machine learning and multi-agent method was utilized to solve this complex problem. For a cross-interface application scenario, the authors in [21] proposed a drone-assisted VLC system that uses LEDs as one downlink transmitter to provide underwater devices with communication services. More recently, the researchers have initiated and reported a series of design and optimization solutions in drone VLC systems, including but not limited to the power consumption minimization [22], energy and user mobility awareness [23], maritime target tracking [24], sum-rate maximization [25], autonomous obstacle avoidance [26], 3-D placement strategy [27], joint deployment and beam-steering optimization [28], heterogeneous networking [29], optimal height of covert communications [30] and drone non-orthogonal multiple access downlinks [31]. Up to now, numerous performance improvements have been reported from the above typical drone VLC studies. But almost all these studies assume that the concerned LED light sources integrated to drone platform follow generalized Lambertian spatial beams [32], which inevitably cannot cover the LED beam diversity including rotational symmetric and rotational asymmetric non-Lambertian beam patterns and also cannot further leverage this potential design degree of freedom [32, 33, 34]. It should be emphasized that the non-Lambertian LED beam configurations and the related system performance have been studied in many VLC technology branches [35], and mainly include underwater optical communications, non-orthogonal multiple access, channel modeling, and multiple cells planning. The LED beam effects and the potential performance benefits have been separately evaluated and demonstrated in the above 6G-oriented VLC branch directions. Based on the above analysis, it is essential to fill the research gap of the current drone VLC works from the LED beam configuration dimension, so as to tackle the challenging 6G drone communications requirements via differentiated, even flexible light beam configurations. Motivated by the facts about drone VLC and LED light beam diversity, the 6G aerial vehicular networks-oriented drone VLC with non-Lambertian LED beam configurations is investigated for the first time. Furthermore, the respective achievable rate under distinct non-Lambertian LED beam configurations is evaluated.