Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities

Kisseleff, Steven; Martins, Wallace A.; Al-Hraishawi, Hayder; Chatzinotas, Symeon; Ottersten, Björn

doi:10.1109/ojcoms.2020.3036839

Cited by 84 publications

(61 citation statements)

References 56 publications

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“…6, the outage probability of our system configuration over Rayleigh fading channels is depicted for two cases, i.e., N = 2, 3, 4, 5 and N = 35, 45, 55. The analytical result given from Remark 1 is compared with the MoG-based approximated result given in (8). The CLT-based result shows poor performance compared with the MoG-based analytical result, especially when N is small, as shown in Fig.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…Recently, reconfigurable intelligent surfaces (RIS)-based wireless transmission is rapidly considered and explored in various wireless communications scenarios, e.g., deviceto-device communications [1], millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system [2], [3], covert communication [4], physical layer security [5], [6], index modulation [7], smart cities [8], etc. What makes the RIS technology popular lies in its unique capability in controlling the propagation environment so as to increase the quality of service (QoS) [5], [7], [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

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Channel Modeling and Analysis of Reconfigurable Intelligent Surfaces Assisted Vehicular Networks

Kong

et al. 2021

2021 IEEE International Conference on Communications Workshops (ICC Workshops)

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The new concept named reconfigurable intelligent surfaces (RIS) is becoming an appealing enabler due to its uniqueness with having low hardware complexity and low power consumption advantages simultaneously. In this paper, an RISaided vehicular Adhoc network (VANET) is considered, where the beacon vehicle is enabled with a passive RIS, the communication links between the beacon vehicle and client vehicle caused due to the multipath fading effects, are modeled with Fox's H-function distribution. This paper first models the inter-vehicle links for the given system setup and then investigates the outage probability and effective rate as performance metrics. More specifically, the unsupervised expectation-maximization (EM) algorithm is consequently used to characterize the distribution of the received signal-to-noise ratio (SNR) at the client vehicle, which is modeled as the mixture of Gaussian (MoG) distribution. The accuracy of our approach is further validated with the Kolmogorov-Smirnov (KS) goodness of fit test. The MoG-based approach successfully tackles the RIS-enabled inter-vehicle communication with an easy, accurate, and tractable solution compared to the widely used central limit theorem (CLT) method. It leads to the closedform outage probability and effective rate expressions.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Channel Modeling and Analysis of Reconfigurable Intelligent Surfaces Assisted Vehicular Networks

Kong

et al. 2021

2021 IEEE International Conference on Communications Workshops (ICC Workshops)

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“…Therefore, by learning this relationship between the reconfiguration profile and the MSF response, we can manipulate the radiation pattern to optimize the communication channel. Moreover, free space RIS was the subject of several recent works such as free space optical systems [ 66 ], smart radio environments [ 67 , 68 , 69 ], vehicular communication systems [ 70 , 71 ], and a line of sight wireless communications [ 5 ]. Regardless of the actual characteristics of the source (e.g., horn antenna), far-field impinging waves upon the MSF can be considered to be locally planar [ 69 ].…”

Section: Methodsmentioning

confidence: 99%

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

Taghvaee

Jain

Timoneda

et al. 2021

Sensors

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As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment.

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“…Figure 8 provides an IRS illustration. Since IRS is comparatively simple to install and uses low energy, IRS-assisted communications may be used to bring about a significant increase in the coverage of 6G networks in various possible situations [102][103][104]. For instance, IRS can be used to coat the external wall of buildings in the outdoor urban environment.…”

Section: Intelligent Reflecting Surface (Irs)mentioning

confidence: 99%

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Alraih

Shayea

Behjati

et al. 2022

Sensors

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Ever since the introduction of fifth generation (5G) mobile communications, the mobile telecommunications industry has been debating whether 5G is an “evolution” or “revolution” from the previous legacy mobile networks, but now that 5G has been commercially available for the past few years, the research direction has recently shifted towards the upcoming generation of mobile communication system, known as the sixth generation (6G), which is expected to drastically provide significant and evolutionary, if not revolutionary, improvements in mobile networks. The promise of extremely high data rates (in terabits), artificial intelligence (AI), ultra-low latency, near-zero/low energy, and immense connected devices is expected to enhance the connectivity, sustainability, and trustworthiness and provide some new services, such as truly immersive “extended reality” (XR), high-fidelity mobile hologram, and a new generation of entertainment. Sixth generation and its vision are still under research and open for developers and researchers to establish and develop their directions to realize future 6G technology, which is expected to be ready as early as 2028. This paper reviews 6G mobile technology, including its vision, requirements, enabling technologies, and challenges. Meanwhile, a total of 11 communication technologies, including terahertz (THz) communication, visible light communication (VLC), multiple access, coding, cell-free massive multiple-input multiple-output (CF-mMIMO) zero-energy interface, intelligent reflecting surface (IRS), and infusion of AI/machine learning (ML) in wireless transmission techniques, are presented. Moreover, this paper compares 5G and 6G in terms of services, key technologies, and enabling communications techniques. Finally, it discusses the crucial future directions and technology developments in 6G.

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Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities

Cited by 84 publications

References 56 publications

Channel Modeling and Analysis of Reconfigurable Intelligent Surfaces Assisted Vehicular Networks

Channel Modeling and Analysis of Reconfigurable Intelligent Surfaces Assisted Vehicular Networks

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

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