### Related Studies  
In a significant study [22], deep reinforcement learning (DRL) was utilized to dynamically configure phase shifts in reconfigurable intelligent surfaces (RISs), leading to enhancements in signal coverage, a reduced interference, and an improved spectral efficiency. Moreover, in one study [23], an exploration was conducted to leverage deep Q-networks (DQNs) for enhancing RIS-supported massive multi-input multi-output (MIMO) setups. This proposition centered on an adaptable control strategy, dynamically adjusting the phase shifts and beamforming weights associated with the RIS. This adaptation resulted in notable enhancements in the system's capacity, coverage, and energy efficiency. In [24], the author tackles resource allocation hurdles in vehicular communications. This is achieved by employing a multi-agent deep deterministic policy gradient (DDPG) method, where vehicle-to-vehicle (V2V) communications serve as agents utilizing non-orthogonal multiple access (NOMA) [25] technology for spectrum sharing. By approaching the problem as a decentralized discrete-time and finite-state Markov Decision Process (DFMDP) and implementing the DDPG method, the suggested approach optimizes the sum-rate of V2 Icommunications. It also guarantees the latency and reliability requirements are met for safety-critical V2V transmissions amidst a dynamic vehicular setting. In recent years, traditional social networking has evolved into more intricate social interinternetworking, extending beyond human users to objects. Ref. [26] has explored the Social Internet of Things (SIoT) and Multiple IoT (MIoT) paradigms, with the SIoT focusing on technological challenges of interacting IoT devices, while the MIoT delves into data-driven and semantics-based aspects of smart object interactions. This paper investigates this concept of the scope in multi-IoT scenarios, proposing formalizations and applications, followed by experiments evaluating its effectiveness compared to existing parameters like the diffusion degree and influence degree. In [27], the author proposes a symbiotic radio (SR) system that supports both Internet of Things (IoT) and cellular networks, allowing multiple users to receive information from the base station while multi-IoT devices backscatter their data via the same signal. Leveraging robust design methods, the system minimizes the transmit power under cellular outage probability and multi-IoT transmission rate constraints, addressing channel uncertainty and demonstrating effectiveness through simulation results. [PERSON]. [28] proposes an energy- and trust-aware opportunistic routing approach for the cognitive radio Social Internet of Things (CR-SIoT), leveraging network coding and game-theoretic allocation of trusted channels to enhance the network performance, as validated by extensive simulation results. To improve the social edge service (SES) in the Social Internet of Things (SIoT), [PERSON]. [29] proposed a hybrid graph deep learning (HAD) approach that employs an adaptive trust weight (ATW) model and a quotient user-centric coeval learning (QUCL) mechanism, achieving an improved communication and computation performance and enhancing SES reliability. Ref. [30] presents structural causal modeling (SCM) as a method for ecologists to discern cause-and-effect relationships from observational data, overcoming biases common in traditional statistical analyses like confounding. Utilizing directed acyclic graphs (DAGs) and graphical rules like the backdoor and frontdoor criteria, SCM systematically estimates causal effects between variables of interest in ecological studies, showing promise for advancing causal inference without the need for randomized experiments. However, there are few works that apply the causal structure in the field of communication. The application of causal reinforcement learning in RIS-assisted SIoT communication systems is a promising research direction.