Localization via Multiple Reconfigurable Intelligent Surfaces Equipped With Single Receive RF Chains

Alexandropoulos, George C.; Vinieratou, Ioanna; Wymeersch, Henk

doi:10.1109/lwc.2022.3156427

Cited by 52 publications

(37 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3) Receiving RISs: The DMA principle was also conceptualized in the parallel work [74] for enabling RISs with baseband reception capability in order to perform explicit or implicit channel estimation with minimal number of receive RF chains. In fact, [76] presented a signal direction estimation approach with a single RF chain, while [74] designed a matrixcompletion-based channel estimation technique with relatively short training requirements, requiring much fewer receive RF chains than RIS elements.…”

Section: B Operation Modesmentioning

confidence: 99%

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Jian¹,

Alexandropoulos²,

Başar³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks has been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure.Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

show abstract

Section: B Operation Modesmentioning

confidence: 99%

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Jian¹,

Alexandropoulos²,

Başar³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The AoDs between the BS and each RIS are also assumed known due to the known placement of the RISs and the BS position. In the context of the latter partial CSI case, we do not deal with the problem of accurately estimating the angular channel parameters, instead we assume computationally and sensingly autonomous RISs (e.g., [72], [111], [116], [119]).…”

Section: F Orchestration With Partial Csi Observabilitymentioning

confidence: 99%

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

Alexandropoulos¹,

Stylianopoulos²,

Huang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardwareefficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces with limited, or even the absence of, computing hardware. In this paper, we consider multi-user and multi-RIS-empowered wireless systems, and present a thorough survey of the online machine learning approaches for the orchestration of their various tunable components. Focusing on the sum-rate maximization as a representative design objective, we present a comprehensive problem formulation based on Deep Reinforcement Learning (DRL). We detail the correspondences among the parameters of the wireless system and the DRL terminology, and devise generic algorithmic steps for the artificial neural network training and deployment, while discussing their implementation details. Further practical considerations for multi-RIS-empowered wireless communications in the sixth Generation (6G) era are presented along with some key open research challenges. Differently from the DRL-based status quo, we leverage the independence between the configuration of the system design parameters and the future states of the wireless environment, and present efficient multi-armed bandits approaches, whose resulting sum-rate performances are numerically shown to outperform random configurations, while being sufficiently close to the conventional Deep Q-Network (DQN) algorithm, but with lower implementation complexity.

show abstract

“…The authors in [95] investigated the effect of a placement of three RISs on the localization and coverage, where each RIS consists of 64 elements, with dimensions of 8 × 8, and is equipped with one (1) RF chain. The results revealed that, for better localization, it is recommended to place these RISs on the same wall and at the same height.…”

Section: A Deployment Of Rismentioning

confidence: 99%

Semi-Passive Reconfigurable Intelligent Surfaces Enabled Communications: A Comprehensive Survey

Balgabay¹,

Arzykulov²,

Galymzhan³

2022

Preprint

View full text Add to dashboard Cite

<p>Along with the advancement of edge computing and next-generation cellular networks, the demand for wireless communication systems is increasing significantly. The fifth-generation (5G) wireless systems are beginning to spread worldwide and gradually finding their application in many domains. However, 5G alone is not enough to solve the problems associated with more complex systems, where technologies such as machine learning (ML) and artificial intelligence are applied, the operational requirements of which can be covered by the use of sixth-generation (6G) networks. One of the most important aspects of implementing 6G is the reconfigurable intelligent surface (RIS), which uses reflective elements capable of manipulating electromagnetic waves. However, the use of a large number of passive reflecting elements in standard RIS complicates the task of channel estimation (CE). To achieve a significant increase in performance, a semi-passive RIS architecture is proposed using a few active sensors that can receive and reflect signals. In this paper, we consider semi-passive RIS and explore different respective CE approaches, classifying them into traditional and ML-based ones, with different metrics for optimization. Finally, in addition to the CE challenges, the issues with RIS deployment and user localization are also studied.</p>

show abstract

Localization via Multiple Reconfigurable Intelligent Surfaces Equipped With Single Receive RF Chains

Cited by 52 publications

References 17 publications

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

Semi-Passive Reconfigurable Intelligent Surfaces Enabled Communications: A Comprehensive Survey

Contact Info

Product

Resources

About