The joint communications and sensing (JCAS) paradigm is envisioned as a core capability of sixth-generation (6G) wireless networks, enabling the integration of data communication and environmental sensing within a unified system. By reusing spectrum, waveforms, and hardware resources, JCAS improves spectral efficiency, reduces system complexity, and hardware cost, while enabling new use cases. Nevertheless, the realization of JCAS is hindered by inherent trade-offs between communication and sensing objectives, limited controllability of wireless propagation, and stringent hardware and design constraints. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have recently emerged as a promising technology to address these challenges by enabling full-space programmable manipulation of electromagnetic waves. This survey provides a systematic and in-depth review of STAR-RIS-enabled JCAS systems. Specifically, we first introduce the fundamental principles of JCAS and STAR-RIS. We then classify and review the state-of-the-art research on STAR-RIS-assisted JCAS from multiple perspectives, encompassing system architectures, waveform and beamforming design, resource allocation, optimization frameworks, and learning-based control. Finally, we identify key open challenges that remain unsolved and outline promising future research directions toward intelligent, flexible, and perceptive 6G wireless networks.

The integration of artificial intelligence (AI), reconfigurable intelligent surfaces (RIS), and integrated sensing and communication (ISAC) is emerging as a key enabler for intelligent, adaptive, and efficient wireless networks in the 6G era. This survey provides a comprehensive and well-structured overview of how AI is revolutionizing RIS-assisted ISAC by enabling dynamic, data-driven control over the radio environment. Through intelligent configuration of RIS, AI facilitates real-time adaptation of signal propagation paths, enhances sensing resolution, and ensures robust communication performance across diverse and dynamic network conditions. The discussion is organized across two major domains: terrestrial networks (TNs) and non-terrestrial networks (NTNs). In TNs, we examine four critical directions: (i) improving EE, (ii) strengthening security, (iii) achieving high sensing accuracy and low communication latency via joint optimization, and (iv) maximizing throughput. In NTN domain, especially within UAV-based platforms, we explore AI-RIS frameworks that support coordinated sensing and communication, reinforce link security in mobile and unpredictable environments, and enhance aerial network capacity through real-time RIS tuning. By bridging both terrestrial and aerial applications, this survey highlights how AI-driven RIS control enables environment-aware, low-latency, and secure ISAC operations. Finally, we identify and analyze key challenges such as managing trade-offs between sensing and communication, generalizing AI models across varied network scenarios, and ensuring scalable, low-complexity real-time deployment. The work concludes with forward-looking insights into future research directions essential for realizing robust and intelligent RIS-assisted ISAC architectures in 6G and beyond.

This work investigates a robust resource allocation framework for a downlink multi-user communication system integrating movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) under the rate-splitting multiple access (RSMA) transmission protocol. Unlike conventional fixed-position antenna architectures, the considered MAs-enabled system introduces spatially adaptive channel variations in which antenna positions directly influence the effective channel responses. Consequently, under imperfect channel state information (CSI), the impact of CSI uncertainty propagates not only through active and passive beamforming design, but also through the antenna position optimization process, leading to a highly coupled robust optimization problem. To address this challenge, we formulate a system sum-rate maximization problem by jointly optimizing the transmit precoding vectors, RIS reflection matrix, common-rate allocation, and MAs positions, subject to quality-of-service (QoS), power-budget, common-rate decoding, and mutual coupling constraints. The resulting non-convex problem is efficiently handled through an iterative robust optimization framework, where the original problem is successively decomposed into active beamforming, RIS reflection matrix, and MAs position optimization subproblems, and tractable convex surrogate functions are constructed to enable iterative optimization. Moreover, system robustness is ensured by incorporating a bounded CSI uncertainty model that explicitly captures channel estimation errors and guarantees reliable communication performance under worst-case channel conditions. Finally, extensive simulation results demonstrate that the proposed framework achieves significant performance gains and enhanced robustness compared with benchmark schemes, while also exhibiting fast and stable convergence behavior under practical imperfect CSI conditions.

Existing surveys on Reconfigurable Intelligent Surfaces (RISs) either focus on hardware fundamentals or provide preliminary discussions of Artificial Intelligence (AI) integration without systematic analysis. This survey bridges that gap by delivering a structured taxonomy of AI techniques applied to RIS-assisted wireless networks. We first examine RIS variants from passive and active designs to Beyond Diagonal RIS (BD-RIS), analyzing their architectures and operational constraints. Subsequently, we review AI methodologies including supervised learning, deep learning, reinforcement learning, deep reinforcement learning, federated learning, graph learning, transfer learning, and meta-learning, emphasizing their suitability for specific RIS challenges. The core contribution is a five-domain analysis covering channel estimation, sum rate maximization, energy efficiency, security enhancement, and performance evaluation, supported by comprehensive summary tables detailing system models, channel state information, algorithms, and optimization strategies. Following that, we synthesize lessons learned, identify open challenges, and outline future research directions, underscoring AI’s transformative role in advancing RIS toward sixth generation networks.

The increasing complexity of urban environments has amplified the demand for secure, reliable, and energy-efficient wireless communications. Traditional terrestrial base stations frequently face challenges in dense urban areas, mainly due to recurring non-line-of-sight (NLOS) scenarios, intense interference, and high susceptibility to eavesdropping. Unmanned aerial vehicles (UAVs) have stood out as a valuable supplementary option, thanks to their ability to support flexible deployment and reliable line-of-sight (LOS) connections. Meanwhile, millimeter-wave (mmWave) bands provide abundant spectrum to satisfy the growing capacity demands of urban networks, but remain highly susceptible to path loss, blockages, and beam misalignment, limiting their effectiveness in such environments. As a cost-efficient and energy-conscious option, reconfigurable intelligent surfaces (RIS) work by smartly reconfiguring wireless reflections to recover blocked links, expand coverage range, and boost secrecy performance. This paper investigates a RIS-aided UAV mmWave communication framework engineered to jointly address energy efficiency and physical layer security under potential eavesdropping threats. A deep reinforcement learning (DRL) strategy is formulated, where the UAV agent interacts with the dynamic urban environment based on imperfect channel state information (CSI) and local position feedback. The agent jointly optimizes UAV active beamforming, RIS phase shifts, and UAV trajectory in real time. To mitigate estimation bias, a softmax operator is incorporated into the learning process. Simulation findings demonstrate that the method put forward in this study outperforms baseline solutions relying on DRL. Specifically, it alleviates both overestimation and underestimation issues while significantly improving secrecy capacity and secure energy efficiency in RIS-assisted UAV mmWave systems.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) has emerged as a key architectural candidate for sixth-generation (6G) wireless networks, in which many distributed access points (APs) cooperate to serve users without cell boundaries. When combined with integrated sensing and communication (ISAC), this infrastructure evolves from a pure connectivity layer into a spatially distributed sensing-communication fabric capable of high-rate data delivery and fine-grained environmental perception. This survey provides a structured overview of CF-mMIMO-ISAC systems. We first revisit the fundamentals of CF-mMIMO and ISAC and clarify their synergies and inherent tensions. We then synthesize recent progress along several core design axes: joint maximization of communication sum-rate and sensing signal-to-noise ratio (SNR); physical-layer security and privacy-aware sensing; energy-efficient operation with stringent latency and age-of-information requirements; performance evaluation and scalability under realistic hardware and fronthaul constraints; and integration with enabling technologies such as reconfigurable intelligent surfaces (RISs), movable antennas (MAs), orthogonal time-frequency space (OTFS) modulation, and uncrewed aerial vehicle (UAV) platforms. Across these themes, we compare optimization-based and learning-based methods, emphasizing how they reshape the rate-sensing tradeoff, how sensitive they are to channel state information (CSI) assumptions, and how system-level coordination influences scalability. Finally, we distill cross-cutting lessons and outline open problems in distributed joint sensing-communication (JSC) design. The survey is intended as both a technical reference and a roadmap for designing CF-mMIMO ISAC frameworks in 6G and beyond.

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral efficiency, energy efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate allocation of users, the power-splitting ratios, and scattering matrix of the BD-RIS, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model and ensure that the resulting system satisfies all energy harvesting constraints. In the considered system, we design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. To tackle the inherent non-convexity of the problem, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized in an iterative manner. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming problem through successive-convex approximation (SCA), whereas the inherently convex power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the conjugate-gradient method. Finally, numerical simulations are conducted to evaluate the performance of the proposed scheme, revealing significant performance improvements over existing benchmarks and demonstrating rapid convergence within a reasonable number of iterations.

Integrated sensing and communication (ISAC) has emerged as a key enabling technology for sixth-generation (6 G) networks, supporting joint environment perception and data transmission with high spectral efficiency and reduced hardware cost. However, the design and deployment of ISAC systems remain challenging due to dynamic wireless environments, sensing–communication trade-offs, and the increasing complexity of large-scale networks. Reinforcement learning (RL) and deep reinforcement learning (DRL) have recently attracted significant attention as data-driven approaches for addressing these challenges through model-free, adaptive, and real-time optimization. This survey first reviews the fundamentals of ISAC technology and then summarizes major RL and DRL algorithms relevant to ISAC design. It further provides a comprehensive overview of RL/DRL methods for ISAC in 6 G networks. Existing approaches are categorized into value-based, policy-based, and hybrid methods, and are further classified according to representative ISAC scenarios, including reconfigurable intelligent surface (RIS)-assisted systems, unmanned aerial vehicle (UAV) and satellite systems, vehicular networks, and other emerging 6 G applications. The survey highlights reported gains in spectral efficiency, sensing accuracy, adaptability, and robustness, while also identifying key limitations of current approaches. Finally, the survey outlines current challenges, future research directions, and key lessons learned.

Unmanned aerial vehicle (UAV)-based integrated sensing and communication (ISAC) systems are poised to revolutionize next-generation wireless networks by enabling simultaneous sensing and communication (S&C). This survey comprehensively reviews UAV-ISAC systems, highlighting foundational concepts, key advancements, and future research directions. We explore recent advancements in UAV-based ISAC systems from various perspectives and objectives, including advanced channel estimation (CE), beam tracking, and system throughput optimization under joint sensing and communication S&C constraints. Additionally, we examine weighted sum rate (WSR) and sensing trade-offs, delay and age of information (AoI) minimization, energy efficiency (EE), and security enhancement. These applications highlight the potential of UAV-based ISAC systems to improve spectrum utilization, enhance communication reliability, reduce latency, and optimize energy consumption across diverse domains, including smart cities, disaster relief, and defense operations. The survey also features summary tables for comparative analysis of existing methodologies, emphasizing performance, limitations, and effectiveness in addressing various challenges. By synthesizing recent advancements and identifying open research challenges, this survey aims to be a valuable resource for developing efficient, adaptive, and secure UAV-based ISAC systems.

This comprehensive survey examines how Reconfigurable Intelligent Surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It further explores RIS integration in complex network environments, such as Heterogeneous Wireless Networks (HetNets) and Non-Orthogonal Multiple Access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like Terahertz (THz) networks, Vehicular Communication (VC), and Unmanned Aerial Vehicle (UAV) communications, highlighting the synergy between RIS and Artificial Intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues.

Intelligent reconfigurable surfaces (IRS) have emerged as a promising technology to enhance wireless communications by dynamically controlling the propagation environment. Despite their potential, practical challenges such as effective integration with existing systems and efficient optimization remain critical. This paper investigates the sum capacity enhancement of NOMA-enabled uncrewed aerial vehicle (UAV) communications in vehicular networks assisted IRS. In urban environments where direct links from UAV to vehicles are often obstructed by buildings or other obstacles, the IRS plays a critical role in improving signal quality by reflecting signals toward vehicles. We consider a downlink NOMA transmission scenario, where the UAV serves multiple ground vehicles, and signals are delivered through both direct and IRS-assisted links. A joint optimization problem is formulated to maximize the sum capacity by simultaneously optimizing UAV power allocation and IRS passive beamforming while ensuring a minimum signal-to-interference plus noise ratio requirement for each vehicle. To address the non-convex nature and reduce the complexity of the optimization, we first transform the original problem using the first-order Taylor expansion method. Then, we employ a two-step solution based on the fixed-point iteration method for passive beamforming at the IRS and standard convex optimization for UAV power allocation. The proposed solution is compared with a benchmark scheme with direct UAV-to-vehicle communication without IRS assistance. Numerical results demonstrate that our proposed framework converges quickly and significantly outperforms the benchmarks in terms of system capacity.

The integration of RIS into UAV networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services-spanning logistics to smart surveillance-their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, reduce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced EE-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, QoS guarantees, secrecy rate improvement, latency reduction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like MEC, NOMA, V2X communication, and WPT. These technologies are crucial to the LAE ecosystem. Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.

With the advent of advancements in future sixth-generation (6G) communication systems, Internet of Things (IoT) devices, characterized by their limited computational and communication capacities, have become integral in our lives. These devices are deployed extensively to gather vast amounts of data in real-time applications. However, their restricted battery life and computational resources present significant challenges in meeting the requirements of advanced communication systems. Mobile Edge Computing (MEC) has emerged as a promising solution to these challenges within the IoT realm in recent years. Despite its potential, securing MEC infrastructure in the context of IoT remains an open task. This study explores the operational dynamics of a secured IoT-enabled MEC infrastructure, focusing on providing real-time, on-demand, secure computational resources to low-powered IoT devices. It outlines a joint optimization problem to maximize computational throughput, minimize device energy consumption, reduce computational latency, and mitigate security overhead. An optimization algorithm is introduced to address these challenges by jointly allocating resources, thereby optimizing throughput, conserving energy, and meeting latency benchmarks through dynamic system adaptation. The effectiveness of the proposed model and algorithm is demonstrated through comparisons with relevant benchmark schemes, highlighting its efficiency in various scenarios. This work showcases the potential of advancements in encryption to deliver scalable security solutions with reduced resource consumption as the number of devices increases.

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into Passive RIS (PRIS) and Active RIS (ARIS) paradigms. PRIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.

The integration of artificial intelligence (AI) in 6G demonstrates a transformative leap in redefining network efficiency, intelligence, and adaptability. However, AI largely leverages discriminative models relying on labelled and quality data, where data accessibility remains serious concern. Generative AI (GenAI) has gained traction due to its immense potential in resolving the issue of data scarcity, complexity, and incompleteness. GenAI models excel in understanding underlying data distributions, enabling them to generate synthetic data that mirrors real-world patterns. GenAI supports adaptive learning and scenario modeling, making it indispensable for addressing the unpredictability and complexity inherent in 6G networks. Since the complexity of wireless communication systems is increasing and the demand for such systems is growing, GenAI presents new ideas for enhancing network performance, increasing system efficiency, and developing intelligent decision-making capabilities. This survey paper investigates the promising role of GenAI in the evolution of 6G networks. An in-depth discussion of notable GenAI models is presented, outlining their application in enhancing key network components. Specifically, the application of GenAI in advanced technologies including reconfigurable intelligent surfaces (RIS), unmanned aerial vehicles (UAVs), digital twins (DTs), and integrated sensing and communications (ISACs) is thoroughly investigated with respect to optimize the adaptability, flexibility, and robustness of the wireless networks. Moreover, use cases of GenAI-enabled wireless networks are presented to highlight the realization of GenAI in 6G. The paper also presents the lessons learned, existing challenges, and future research directions. This paper systematically explores GenAI and its pivotal role in the development of 6G, providing a foundation for researchers to further investigate and advance GenAI-enabled 6G.

The integration of Unmanned Aerial Vehicle (UAV) technology with Reconfigurable Intelligent Surface (RIS) systems has become an important research direction for improving wireless communication efficiency and energy harvesting capabilities. This paper investigates the energy harvesting efficiency of RIS-assisted UAV communication systems in the presence of eavesdroppers. It proposes an optimization framework based on Twin Delayed Deep Deterministic Policy Gradient (TD3), aiming to optimize the energy harvesting efficiency of UAVs under secure communication rate constraints. The optimization objective is to intelligently allocate the time ratio between energy harvesting and information transmission in each time slot, as well as the allocation ratio of RIS reflection resources, thereby maximizing the overall system performance. Simulation results demonstrate that this method significantly enhances UAV energy harvesting efficiency while improving convergence speed and stability.

This paper studies the problem of hybrid holographic beamforming for sum-rate maximization in a communication system assisted by a reconfigurable holographic surface. Existing methodologies predominantly rely on gradient-based or approximation techniques necessitating iterative optimization for each update of the holographic response, which imposes substantial computational overhead. To address these limitations, we establish a mathematical relationship between the mean squared error (MSE) criterion and the holographic response of the RHS to enable alternating optimization based on the minimum MSE (MMSE). Our analysis demonstrates that this relationship exhibits a quadratic dependency on each element of the holographic beamformer. Exploiting this property, we derive closed-form optimal expressions for updating the holographic beamforming weights. Our complexity analysis indicates that the proposed approach exhibits only linear complexity in terms of the RHS size, thus, ensuring scalability for large-scale deployments. The presented simulation results validate the effectiveness of our MMSE-based holographic approach, providing useful insights.

Uncrewed aerial vehicle (UAV)-assisted multi-access edge computing (MEC) is increasingly being adopted to meet the rising demand for low-latency and efficient data processing, particularly in environments with limited ground infrastructure. However, optimizing task offloading and resource allocation in such networks remains a significant challenge as the number of UAVs and connected devices grows. To address this, we propose a Trajectory-Based Task Offloading in UAV-Assisted MEC (TB-TUAV) scheme, which leverages UAV mobility to enhance resource utilization and reduce latency. Unlike existing methods, TB-TUAV integrates a deep reinforcement learning (DRL) framework based on a Markov Decision Process (MDP) to dynamically optimize task offloading and resource allocation in multi-UAV networks. Our approach effectively balances exploration and exploitation, improving learning stability and ensuring fast convergence. By incorporating trajectory optimization through random path exploration, the proposed scheme efficiently distributes computational tasks while mitigating processing delays. Simulation results demonstrate that TB-TUAV significantly improves resource efficiency and reduces latency compared to state-of-the-art baseline methods. This research presents a scalable and adaptive solution for real-time MEC applications in dynamic multi-UAV environments, ensuring improved performance even under resource constraints.

This manuscript proposes an efficient resource management strategy for a rate-splitting multiple access (RSMA) based simultaneous wireless information and power transfer (SWIPT) system by leveraging reconfigurable intelligent surface (RIS) in consumer-centric sixth-generation (6G) networks for industry 5.0, under residual hardware impairments (RHIs) both at the transmitter and receiver nodes. Specifically, we aim to maximize the sum-rate of a RIS-assisted RSMA-based SWIPT system by incorporating a practical non-linear energy-harvesting model, while adhering to the quality-of-service (QoS), power-budget, power-splitting ratios, energy-conservation, and energy-harvesting constraints of the system. Moreover, the presented optimization technique addresses the highly non-convex problem in four distinct steps. Firstly, the power-allocation for both common and private messages of RSMA users is determined by converting a significantly non-convex power-allocation problem into a convex one by exploiting the successive-convex approximation (SCA) technique. Secondly, power-splitting ratios for RSMA users are computed by using the interior-point method facilitated by the Mosek-enabled toolbox in CVX. Thirdly, it computes transmit passive beamforming of a transmitter equipped with a transmissive-RIS (T-RIS), by exploiting SCA and semidefinite relaxation (SDR) techniques. Finally, passive beamforming vectors for the transmission and reflection regions of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) node are determined by converting a non-convex problem into a standard SDP problem using SCA, SDR, and Gaussian randomization techniques. Additionally, numerical simulation results affirm the effectiveness of the proposed optimization strategy, indicating superior performance against benchmark techniques and fast convergence within a reasonable number of iterations.

The reconfigurable intelligent surface (RIS) technology shows great potential in sixth-generation (6G) terrestrial and non-terrestrial networks (NTNs) since it can effectively change wireless settings to improve connectivity. Extensive research has been conducted on traditional RIS systems with diagonal phase response matrices. The straightforward RIS architecture, while cost-effective, has restricted capabilities in manipulating the wireless channels. The beyond diagonal reconfigurable intelligent surface (BD-RIS) greatly improves control over the wireless environment by utilizing interconnected phase response elements. This work proposes the integration of unmanned aerial vehicle (UAV) communications and BD-RIS in 6G NTNs, which has the potential to further enhance wireless coverage and spectral efficiency. We begin with the preliminaries of UAV communications and then discuss the fundamentals of BD-RIS technology. Subsequently, we discuss the potential of BD-RIS and UAV communications integration. We then propose a case study based on UAV-mounted transmissive BD-RIS communication. Finally, we highlight future research directions and conclude this work.