Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer

Zhong, Ruikang; Liu, Yuanwei; Mu, Xidong; Chen, Yue; Wang, Xianbin; Hanzo, Lajos

doi:10.1109/jsac.2022.3192053

Cited by 21 publications

(4 citation statements)

References 33 publications

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“…Zhong et al [83] investigated the STAR-RIS MISO scenario and considered a coupled phase-shift model. By defining a joint passive and active beamforming optimization problem, the power consumption for long-term broadcasting was minimized under the coupled phase-shift restriction and the least data rate constraint.…”

Section: Heavy Shadowing Rician Fading Ao Algorithmmentioning

confidence: 99%

A Survey on STAR-RIS: Use Cases, Recent Advances, and Future Research Challenges

Ahmed

Wahid

Laique

et al. 2023

IEEE Internet Things J.

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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.

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Section: Heavy Shadowing Rician Fading Ao Algorithmmentioning

confidence: 99%

A Survey on STAR-RIS: Use Cases, Recent Advances, and Future Research Challenges

Ahmed

Wahid

Laique

et al. 2023

IEEE Internet Things J.

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“…Particularly, RIS is an emerging technology that can provide a dynamic, controllable, and programmable radio environment. Further, different metasurface implementations have been developed for the full space environment, including simultaneous transmitting and reflecting-RIS (STAR-RIS) and intelligent omni-surface (IOS) [8]. Owing to the dependence of the PLS on a richly scattered environment, the dynamic control of channels achieved by the RIS can significantly improve security in 6G-IoT networks.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Transmitting and Reflecting-Reconfigurable Intelligent Surface in 6G: Design Guidelines and Future Perspectives

Khalid¹,

Kaleem²,

Ullah³

et al. 2023

IEEE Network

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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 STAR-RIS and malicious RIS.

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“…Unlike conventional RIS systems, transmission and reflection elements in STAR-RIS systems are coupled together, which further increase the resource allocation complexity [3]. In this light, deep learning (DL) has stood out as a cost-effective solution for the intelligent reflecting surfaces assisted system optimization [10]- [13]. Particularly, fully connected neural networks (FCNNs) were proposed in [10] passive beamforming optimization problem in RIS-assisted single user systems.…”

Section: Introductionmentioning

confidence: 99%

“…The transmitting beamforming vectors and STAR-RIS coefficient were jointly optimized based on the CSI via an trial-and-error process. Furthermore, the authors in [13] proposed two solutions for the joint active and passive beamforming optimization in STAR-RIS-aided NOMA systems, namely the hybrid DDPG algorithm and the joint DDPG and deep Q network algorithm. It was shown that the proposed solutions achieve superior performance compared to the conventional DDPG framework, albeit with increased computational complexity.…”

Section: Introductionmentioning

confidence: 99%

RIS-Assisted MIMO Communication Systems: Model-based versus Autoencoder Approaches

Chien

Nguven

et al. 2022

2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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This paper investigates a joint active and passive beamforming design for distributed simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) assisted multi-user (MU)-mutiple input single output (MISO) systems, where the energy splitting (ES) mode is considered for the STAR-RIS. We aim to design the active beamforming vectors at the base station (BS) and the passive beamforming at the STAR-RIS to maximize the user sum rate under transmitting power constraints. The formulated problem is non-convex and nontrivial to obtain the global optimum due to the coupling between active beamforming vectors and STAR-RIS phase shifts. To efficiently solve the problem, we propose a novel graph neural network (GNN)-based framework. Specifically, we first model the interactions among users and network entities are using a heterogeneous graph representation. A heterogeneous graph neural network (HGNN) implementation is then introduced to directly optimizes beamforming vectors and STAR-RIS coefficients with the system objective. Numerical results show that the proposed approach yields efficient performance compared to the previous benchmarks. Furthermore, the proposed GNN is scalable with various system configurations.

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Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer

Cited by 21 publications

References 33 publications

A Survey on STAR-RIS: Use Cases, Recent Advances, and Future Research Challenges

A Survey on STAR-RIS: Use Cases, Recent Advances, and Future Research Challenges

Simultaneous Transmitting and Reflecting-Reconfigurable Intelligent Surface in 6G: Design Guidelines and Future Perspectives

RIS-Assisted MIMO Communication Systems: Model-based versus Autoencoder Approaches

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