This survey provides a comprehensive analysis of the integration of Reconfigurable Intelligent Surfaces (RIS) with edge computing, underscoring RIS's critical role in advancing wireless communication networks. The examination begins by demystifying edge computing, contrasting it with traditional cloud computing, and categorizing it into several types. It further delves into advanced edge computing models like Multi-Access Edge Computing (MEC), Vehicle Fog Computing (VFC), and Vehicle Edge Computing (VEC) and challenges. Progressing deeper, the survey explores RIS technology, categorizing it into passive, active, and hybrid RIS, and offers an in-depth analysis of Beyond Diagonal RIS (BD-RIS), including reflective, transmissive, and Simultaneous Transmit and Reflect (STAR) modes. Subsequently, the study assesses RIS's applications within edge computing, revealing its diverse use cases and strategies for performance analysis. The discussion comprises how RIS-driven computation can elevate rates, reduce latency, and contribute to an eco-friendly edge computing approach through better Energy Efficiency (EE). The survey also scrutinizes RIS's role in bolstering security within edge computing. To aid comprehension, each subsection is complemented by summary tables that meticulously elaborate on, compare, and evaluate the literature, focusing on aspects like system models, scenarios, RIS details, Channel State Information (CSI), offloading types, employed schemes, methodologies, and proposed solutions. This organized approach ensures a cohesive and thorough exploration of the survey’s diverse topics. By illustrating the synergy between RIS and edge computing, the study provides valuable insights or lessons learned for enhancing wireless networks, paving the way for future breakthroughs in communication technologies. Before conclusion, the survey also identifies ongoing challenges and future research directions in RIS-assisted edge computing, emphasizing the vast potential of this field.  

This survey investigates the integration of artificial intelligence (AI) with reconfigurable intelligent surfaces (RISs), emphasizing its pivotal role in advancing wireless communications. Initially, it provides an overview of RIS variants-passive, active, and hybrid-and the evolution to beyond diagonal RIS (BD-RIS), detailing their architectures, operational modes, benefits, and challenges. The focus then shifts to various AI algorithms applied within the RIS context, including supervised, unsupervised, deep learning (DL), reinforcement learning (RL), deep reinforcement learning (DRL), federated learning (FL), graph learning (GL), transfer learning (TL), meta-learning techniques. We explore how AI enhances RIS functionality in wireless networks, optimizing key aspects such as channel estimation (CE), rate maximization, expanding coverage, energy efficiency (EE), and enhancing security within RIS-based networks. Additionally, the survey conducts a thorough performance analysis of these AI-enhanced RIS systems under different networks. Comprehensive summary tables are provided to elucidate system models, channel state information, AI algorithms, and optimization strategies. The survey concludes by synthesizing the insights gained from AI in RIS applications, identifying current challenges, and suggesting directions for future research, thereby underscoring AI's substantial impact on the evolution of RISs and the 6G and beyond wireless networks.

A document by Manzoor Ahmed . Click on the document to view its contents.

This article proposes a new resource allocation framework that uses the dual theory approach. Specifically, the sum-rate of the multi-cell network having backscatter tags and NOMA user equipments is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems and then derived the closed-form solutions. A comparison with the orthogonal multiple access (OMA) ambient BackCom and pure NOMA transmission has been provided.

Intelligent reconfigurable surfaces (RIS) have emerged as one of the most promising and cost-effective technologies due to their high energy efficiency, extended wireless coverage, enhanced signal strength, and interference mitigation capability. This paper provides a new framework of cognitive radio-based integrated terrestrial non-terrestrial networks (ITNTNs) involving IRS. The objective is to maximize the achievable sum rate of the secondary network by simultaneously optimizing the transmission power, user association, phase shift design of IRS and 2D placement of UAVs while controlling the co-channel interference temperature to the primary network. The problem is formulated as non-convex/non-linear due to interference and decision variables which makes it NP-hard and intractable. To reduce the complexity and make the problem tractable, we first decouple it into subproblems and iteratively obtain an efficient solution. Numerical results demonstrate that the proposed optimization scheme converges within a few iterations and achieves high sum rate than the benchmark suboptimal schemes.

This work proposes a hybrid intelligent Deep Learning (DL)-enabled mechanism to secure IIoT infrastructure from lethal and sophisticated multi-variant botnet attacks. The proposed mechanism has been rigorously evaluated with latest available dataset, standard and extended performance evaluation metrics, and current DL benchmark algorithms. Besides, cross validation of our results are also performed to clearly show overall performance.

This paper considers RIS enhanced D2D communications underlaying unmanned aerial vehicle (UAV) networks with non-orthogonal multiple access (NOMA). The objective is to maximize the sum rate of NOMA D2D communications by optimizing the power budget of D2D transmitter, NOMA power allocation coefficients of D2D receivers and passive beamforming of RIS while guaranteeing the quality of services of UAV user. Due to non-convexity, the optimization problem is intractable and challenging to handle. Therefore, it is solved in two parts using alternating optimization. Simulation results unviel the performance of the proposed RIS enhanced D2D communications scheme. Results demonstrate that the proposed scheme achieves 15\% and 27\% higher sum rates compared to the fixed power D2D and orthogonal D2D schemes.

This paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.

The recent development of metasurfaces, which may enable several use cases by modifying the propagation environment, is anticipated to have a substantial effect on the performance of 6G wireless communications. Metasurface elements can produce essentially passive sub-wavelength scattering to enable a smart radio environment. STAR-RIS, which refers to reconfigurable intelligent surfaces (RIS) that can transmit and reflect concurrently (STAR), is gaining popularity. In contrast to the widely studied RIS, which can only reflect the wireless signal and serve users on the same side as the transmitter, the STAR-RIS can both reflect and refract (transmit), enabling 360-degree wireless coverage, thus serving users on both sides of the transmitter. This paper presents a comprehensive review of the STAR-RIS, with a focus on the most recent schemes for diverse use cases in 6G networks, resource allocation, and performance evaluation. We begin by laying the foundation for RIS (passive, active, STAR-RIS), and then discuss the STAR-RIS protocols, advantages, and applications. In addition, we categorize the approaches within the domain of use scenarios, which includes increasing coverage, enhancing physical layer security (PLS), maximizing sum rate, improving energy efficiency (EE), and reducing interference. Next, we will discuss the various strategies for resource allocation and measures for performance evaluation. We aimed to elaborate, compare, and evaluate the literature in terms of setup, channel characteristics, methodology, and objectives. In conclusion, we examine the open research problems and potential future prospects in this field.

The connected and autonomous vehicles   (CAV)   applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’  traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective,  i.e.,  vehicle to vehicle  (V2V),  vehicle to roadside infrastructure  (V2I),  and vehicle to everything  (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category,  we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

Reconfigurable intelligent surface (RIS), nonorthogonal multiple access (NOMA), and underwater optical wireless communication (UOWC) are paradigms of technologies that drive the development of next generation communication systems. In this paper, we investigate the performance of a NOMA-based RIS-assisted hybrid radio frequency (RF) UOWC system. The ship works as a relay that redirects the received signal to two underwater destinations simultaneously. Due to the interruption of the direct link between the base station and the ship floating on the surface of the water, communication will be carried out via an RIS fixed to an intermediate building. In this paper, we provide new analytical expressions for the outage probability (OP), asymptotic analyses of the OP, and diversity order (D) to gain insights into the system performance. The results showed that the diversity order depends on the UOWC receiver detection technique. In the end, we illustrated that the NOMA-based RIS-assisted system significantly improves the outage performance of hybrid RF-UWOC systems over a benchmark system.

With the technological evolution and new applications, user equipment (UEs) has become a vital part of our lives. However, limited computational capabilities and finite battery life bottleneck the performance of computationally demanding applications. A practical solution to enhance the quality of experience (QoE) is to offload the extensive computation to the mobile edge cloud (MEC). Moreover, the network’s performance can be further improved by deploying an unmanned aerial vehicle (UAV) integrated with intelligent reflective surfaces (IRS): an effective alternative to massive antenna systems to enhance the signal quality and suppress interference. In this work, the MEC network architecture is assisted by UAV-IRS to provide computational services to the UEs. To do so, a cost minimization problem in terms of computing time and hovering energy consumption is formulated. Furthermore, to achieve an efficient solution to a formulated challenging problem, the original optimization problem is decoupled into sub-problems using the block-coordinate decent method. Moreover, numerical results are compared to baseline schemes to determine the effectiveness of the proposed scheme. Simulation results demonstrate that the optimal allocation of local computational resources results in minimizing tasks’ computational time and hovering energy consumption.