Sixth-generation (6G) networks pose substantial security risks because confidential information is transmitted over wireless channels with a broadcast nature, and various attack vectors emerge. Physical layer security (PLS) exploits the dynamic characteristics of wireless environments to provide secure communications, while reconfigurable intelligent surfaces (RISs) can facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight security solution can be designed for low-end Internet of Things (IoT) devices, depending on the design scenario and communication objective. This article discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and jamming attacks. The theoretical background and literature review of RIS-aided PLS are discussed, and design solutions related to resource allocation, beamforming, artificial noise, and cooperative communication are presented. We provide simulation results to show the effectiveness of RIS in terms of PLS. In addition, we examine the research issues and possible solutions for RIS modeling, channel modeling and estimation, optimization, and machine learning. Finally, we discuss recent advances, including simultaneous transmitting and reflecting-RIS and malicious RIS.

This paper investigates the physical layer security and data transmission for the underlay device-to-device (D2D) networks, and considers a combination of the reconfigurable intelligent surface (RIS) and full-duplex (FD) jamming receiver for the robustness and security enhancements of the system. In the demonstrated spectrum sharing setup, the total power of the D2D networks is conceived to the transmitter and receiver to transmit a private message and emit the artificial noise (AN) signals. To prevent information leakage, a beamforming design is presented for a multi-antenna FD D2D receiver in order to suppress and inject the AN signals in the direction of legitimate users and eavesdropper, respectively. The statistical characterization of end-to-end RIS-assisted wireless channels is presented, and the achievable ergodic secrecy rate of the system is derived in novel approximate expressions. The numerical and simulation results confirm the accuracy and effectiveness of the proposed analytical framework. The results demonstrate an optimal selection of the D2D power allocations for different number of reflecting elements in terms of achievable ergodic secrecy rates of the system.

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultrareliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input-multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.

Unmanned air vehicle (UAV) as an aerial base station (ABS) has attracted the attention of cellular service providers to enable emergency communications. However, the unplanned multiple ABS deployment poses severe interference challenges that degrade the user’s performance. To maximize the system sum capacity, we propose the use of K -means and Q -learning assisted 3D ABS Placement and Power allocation algorithm (KQPP). Specifically, we combine the benefits of K -means and Q -learning algorithms to achieve this goal. As a result, we successfully improve the sum capacity by satisfying all the users’ minimum data rate requirements. The proposed approach achieves 6bps/Hz and 16bps/Hz higher sum-capacity gain compared to equal power allocation and particle swarm optimization (PSO)-based power allocation schemes, respectively.

Reconfigurable intelligent surfaces (RISs) have been considered as a promising technology for the sixth-generation (6G) wireless networks that can control wireless channels in a desirable way and significantly enhance the network performance. Simultaneous transmitting and reflecting-RISs (STAR-RISs) can overcome limitation of reflecting-only RISs by leveraging the higher design flexibility and full-space coverage. Despite the benefits, the modeling and analysis of STAR-RISs are complicated because of various control parameters for both transmission and reflection links. In this article, a general framework to facilitate the design and implementation of STAR-RISs in 6G scenarios and network topologies is presented. We provide a systematic introduction to the STAR-RIS operating protocols for different communication modes and discuss recent efforts to identify the research progress and combination solutions. Finally, we provide the design concepts, research challenges, potential solutions, and future directions related to the channel modeling, channel estimation, hardware implementations, modeling and limitations, and optimization.

In this letter, we aim at improving the system sum-rate performance of an assisted aerial base station (ABS) network by considering use of reconfigurable intelligent surface (RIS). We jointly optimize the RIS phase shifts and ABS altitudes using the proposed conjugate gradient and particle swarm optimization (CG-PSO) scheme. Moreover, we divide the problem into sub-problems and solve them using an alternative optimization method. First, an optimal RIS phase shift is computed using the conjugate gradient (CG) under the fixed ABS altitude and varying transmit power. Finally, we find the optimal ABS altitude using particle swarm optimization (PSO), which results in improved sum rate under the optimized RIS phase shift. The results show that the CG-PSO scheme significantly improves the system sum rate compared to the baseline schemes.