# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 1 Introduction
### Related Work  
UAVs and RIS have emerged as essential solutions for addressing the challenges of establishing LoS and reliable connectivity to GUs. Most existing studies focus on either deploying UAVs (static and mobile) or deploying RIS on buildings to maximize the communication performance of GUs. However, these approaches significantly limit their application in dynamic, real-time GU communication within densely populated urban environments. The integration of RIS in UAV-aided communication systems has recently gained significant attention due to improved system performance [17]. For example, a UAV-based scheme for enhancing mobility management and reducing handover (HO) rate in FWCNs is proposed in [18], utilizing reinforcement learning (RL) to optimize UAV trajectories and enhance LoS links, ensuring seamless connectivity for GUs during transitions between cells. Similarly, a multi-UAV-mounted BS framework for serving multiple IoT devices is proposed in [19], which optimizes 3D layouts and resource allocation to minimize uplink transmission power. However, UAV flexibility is overlooked, which reduces coverage and the ability to adjust positions in response to real-time communication demands. Other studies, such as [20], focus on lowering the HO rate and interference using deep Q-network (DQN) algorithms for UAV communications. However, they do not fully address challenges such as limited coverage at high altitudes or LoS blockages. Additionally, ref. [21] introduces a RIS-assisted HO scheme utilizing deep reinforcement learning (DRL) to mitigate mm-wave channel blockages, significantly reducing HO rate and improving spectral efficiency (SE) through joint adjustment of beamformers and RIS phase shifts. In contrast, RIS-assisted UAV systems have become pivotal in enhancing communication performance, mainly when direct communication links are unavailable. Several studies have explored UAV-RIS systems with various optimization techniques to improve network performance in FWCNs. For instance, ref. [22] investigated a UAV-RIS downlink transmission system and proposed a successive convex approximation (SCA)-based algorithm to optimize UAV trajectory and RIS beamforming. In [23], RIS in UAV-assisted communication systems was explored to achieve substantial performance improvements in UAV-aided cellular networks. Another study, [24], used DRL to optimize UAV trajectory, RIS phase shifts, and power allocation, aiming to maximize the sum rate for mobile UEs. Similarly, ref. [25] developed a RIS-assisted UAV communication system with alternating optimization algorithms to jointly optimize UAV mobility, resource allocation, and RIS scheduling, ensuring improved system rate and heterogeneous QoS for each mobile user. In [26], an RIS-assisted UAV framework employing an energy-efficient UAV deployment (EEUD) algorithm is proposed to maximize energy efficiency by jointly optimizing RIS phase shifts, UAV trajectory, and BS transmit power. The study in [27] investigated UAV-assisted RIS deployment to enhance connectivity and SNR when direct communication links are unavailable. Furthermore, ref. [28] utilized a DRL scheme to improve communication efficiency between the ground base station (GBS) and mobile vehicles. A similar approach was adopted in [29], which employed the deep deterministic policy gradient (DDPG) algorithm to optimize BS power, RIS reflection coefficients, and UAV positioning to maximize the communication rate. In [30], a total energy harvesting strategy for a UAV-RIS system is proposed to enhance energy efficiency (EE) and meet QoS requirements. Other studies, such as [31], have used the UAV-RIS technology to improve downlink secrecy rates by optimizing transmit power allocation, RIS beamforming, and UAV trajectory, demonstrating the potential for high performance in FWCNs. The comparison of the proposed work with existing models, in terms of system configuration, optimization techniques, mobility consideration, and limitations, is presented in Table 1.