There are generally two types of reinforcement learning, one is value-based and the other is policy-based. Hence, DRL is more appropriate for this study, as training labels are very hard to obtain in real-time wireless communication systems. On the other hand, unlike DL which needs a huge number of training labels, DRL-based methods allow wireless communication systems to learn by interacting with the environment. To date, artificial intelligence (AI), such as deep learning (DL) and deep reinforcement learning (DRL)-based methods have been successfully applied to a variety of wireless communication problems ( Cui et al., 2019 Ding, 2020). Conventional optimization methods, such as convex optimization, are difficult to solve non-convex joint optimization problems with highly coupled variables. However, the time-varying multi-user scenario is closer to the real wireless communication systems. Most RIS-related works consider only fixed channel environments. Our prior works ( Jiao et al., 2020) jointly optimized beamforming and phase shift with pre-optimized UAV position and derived the closed-form of the optimal beamforming for a 2-user RIS-UAV-NOMA downlink system. This study introduces UAV to a RIS-NOMA system, which enhances the flexibility of RIS significantly. To the best of our knowledge, most RIS-related works consider fixed RIS deployment scenarios ( Ding et al., 2020 Fang et al., 2020 Zuo et al., 2020). (2022) proposed a scheme that maximizes the average security computation capacity of a NOMA-based UAV-MEC network when a flying eavesdropper exists. On the other hand, as another promising 6G technique ( Chowdhury et al., 2020), unmanned aerial vehicles (UAV) have been widely applied in NOMA systems, such as UAV-MEC-NOMA, UAV-RIS-NOMA, etc. (2020) have illustrated the better performance of combining RIS with NOMA than it has with the conventional orthogonal multiple access (OMA). Inspired by the superiorities of non-orthogonal multiple access (NOMA) such as high spectrum efficiency ( Ding et al., 2017), this study combines NOMA with the IRS. A typical scenario to apply RIS is when the direct links from the base station (BS) to users are blocked by buildings or mountains, which means RIS can create extra propagation paths to guarantee the quality of service (QoS). ![]() A variety of proven techniques, such as massive multiple-input multiple-output (massive-MIMO) and cooperative communications, only focus on how the transceiver can adapt to the channel environment, while RIS have the capability to control the wireless communication propagation environment ( Chen et al., 2019). RIS can be viewed as a low-cost antenna array consisting of a large number of programmable reflecting elements ( Wu and Zhang, 2019). Reconfigurable intelligent surfaces (RIS) have been recognized as one of the promising technologies for sixth-generation (6G) wireless communications ( Zhang et al., 2019) since they have shown excellent features with better spectrum-, energy-, and cost-efficiency ( Zhao, 2019).
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