# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 1 Introduction
### Prior Works  
UAVs are becoming increasingly prominent in various fields, including environmental safety and social governance. For instance, [PERSON] et al. [3] explored the utilization of swarm UAVs for target search in unknown environments and proposed a collaborative method. Consequently, wireless communication in UAV systems has gained significant attention as a fundamental aspect of UAV applications [4]. Unlike conventional cellular systems, wireless communication in cellular-connected UAV swarms presents unique challenges due to the characteristics of machine communication and the specific network topology [5; 6]. A fundamental concern in cellular-connected UAVs is ensuring effective information transmission between UAVs and ground base stations. In [7], the authors designed UAV paths based on a radio map to maintain connectivity between the base stations and UAVs. In [8], the authors proposed a three-dimensional system model that incorporates UAVs and employs an analytical framework to assess system coverage. In [9], the joint optimization of UAV operation time, communication scheduling, trajectory, and transmission power was investigated to maximize data upload throughput while adhering to quality of service (QoS) requirements and energy constraints. Furthermore, the authors in [10] examined the application of large-scale multiple-input multiple-output (MIMO) and UAV communication, obtaining a lower bound for UAV capacity, and deriving optimal antenna spacing and UAV distribution strategies that maximize this capacity. In [11], the authors analyzed the coverage probability of UAV-assisted small base stations for clustered users while considering millimeter wave and directional beamforming based on MIMO. Furthermore, the authors in [12] analyzed the coverage probability of UAV-assisted cellular networks with incomplete beam alignment using the random geometry theory. In environments with blocking, the mounting of RIS on UAVs presents a promising solution [13; 14]. Compared to ground-based RIS, UAV-mounted RIS offers line-of-sight (LoS) paths to UAVs, leading to improved performance of UAV swarms. For example, in [15], a hovering UAV equipped with RIS is designed, with optimization conducted for RIS association, hovering altitude, and RIS phase shifting. In [16], the authors focus on the weight design of the RIS to facilitate communication between the base station (BS) and suburban users, aiming to reduce the number of RIS elements. Additionally, the authors in [17] investigate the optimal UAV altitude and element quantity for the UAV-RIS system. [PERSON] et al. [18] analyze RIS deployment under different multiple access schemes and jointly optimize the coefficient of the RIS and power allocation at access points. Eliminating interference is also a crucial challenge in cellular UAVs. Efforts have been made to alleviate interference between UAV communications and ground users communications [19], for example, the interference between UAVs and ground users are reduced through resource allocation and interference cancellation designs [20]. [PERSON] et al. propose using multiple antennas on the UAV to improve the transmission rate and avoid interference to ground users [21]. The widely employed beamforming design, relying on channel state information (CSI), effectively reduces inter-cell interference and maximizes the sum rate [22]. When accurate channel state information (CSI) is not shared between cells, optimizing beam scheduling proves to be an efficient method for reducing inter-cell interference. [PERSON] et al. [23] minimize the number of inter-cell beam collisions for two cells by optimizing the scheduling order of beams. Additionally, they utilize graph theory to optimize beam scheduling and avoid interference in multiple cells [24]. Although the communication interference among UAV clusters is similar to that among ground cells, the UAV's high mobility characteristics pose difficulties in utilizing complete CSI for interference cancellation.