System Performance Insights into Design of RIS-assisted Smart Radio Environments for 6G

Toumi, Mehdi; Aijaz, Adnan

doi:10.1109/wcnc49053.2021.9417549

Cited by 7 publications

(3 citation statements)

References 14 publications

(18 reference statements)

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“…However, it is only applied to far field case. In [116], the SimRIS channel simulator was adopted to provide performance insights for design of RIS-assisted smart radio environments. The impact of positioning/placement of RISs, the number of reflecting elements and the tilt/rotation of RISs in both single and multi-RIS environments were investigated.…”

Section: Physical Channel Modelmentioning

confidence: 99%

Reconfigurable Intelligent Surfaces: Channel Characterization and Modeling

et al. 2022

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Reconfigurable intelligent surfaces (RISs) are two dimensional (2D) metasurfaces which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RIS is viewed as a potential key technology for the sixth generation (6G) wireless communication systems mainly due to its advantages in tuning wireless signals, thus smartly controlling propagation environments. In this paper, we aim at addressing channel characterization and modeling issues of RISassisted wireless communication systems. At first, the concept, principle, and potential applications of RIS are given. An overview of RIS based channel measurements and experiments is presented by classifying frequency bands, scenarios, system configurations, RIS constructions, experiment purposes, and channel observations. Then, RIS based channel characteristics are studied, including reflection and transmission, Doppler effect and multipath fading mitigation, channel reciprocity, channel hardening, rank improvement, far field and near field, etc. RIS based channel modeling works are investigated, including largescale path loss models and small-scale multipath fading models. At last, future research directions related to RIS-assisted channels are also discussed.Index Terms-6G wireless communications, reconfigurable intelligent surface (RIS), channel measurements, channel characteristics, channel modeling.

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Section: Physical Channel Modelmentioning

confidence: 99%

Reconfigurable Intelligent Surfaces: Channel Characterization and Modeling

et al. 2022

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“…The heterogeneous deployment of SRE entities, namely Integrated Access and Backhaul (IAB) nodes, smart repeaters, RISs, and passive surfaces, is judiciously planned to minimize the total installation costs while simultaneously optimizing the network spectrum [4]. In [5], the system performance of RIS-assisted SREs was studied by adopting a simulation-driven approach and conducting a holistic evaluation of capacity and reliability aspects. In [6], a resource allocation framework was developed that explicitly accounted for the overhead associated with channel estimation and the configuration of the optimal RIS phase shifts.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Smart Radio Environment (ASRE): New Paradigm for Wireless Communication Networks

Luo,

Yan,

Xuan

et al. 2024

IEEE Access

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ABSRACT Recent efforts have promoted Smart Radio Environment (SRE) to enhance the reception quality in high-frequency bands via reconfigurable intelligent surfaces (RISs) for supporting hyperconnectivity of Beyond 5G/6G Era. The functionality of SRE is based on the more degrees of freedom that come from electronically controlling the environment itself rather than transmitter and receiver. Accordingly, the spectral efficiency and the sum rate throughput can be enhanced by applying customized transformations to the electromagnetic radio waves. However, futuristic applications that are envisioned to be time-critical demand much more than what SRE can handle because that too much time is spent on sensing the environment and applying customized transformations.In this work, we propose a novel concept of adaptive SRE (ASRE), which intends to start from the semantic perception of a wireless environment to explore the learning and evolutionary mechanisms of SRE through the loop of recognition, adaptation, and proaction. In combination with the techniques known in artificial intelligence (AI), such as deep reinforcement learning, knowledge graph, etc., this technology can provide enhanced ultra-reliable lowlatency communication (URLLC) services. Furthermore, the wireless environment is expected to become not just adaptive, but partially intelligent by predicting and utilizing the changes in the communication environment. To corroborate the rationality and superiority of ASRE, we also present simulation results related to the typical dynamic NLOS scenario. Finally, we highlight numerous open challenges and research directions.

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“…By placing RIS units into the environment where wireless systems are operating, the properties of the radio channels can be partially controlled [2]. The control over the propagation environment opens possibilities for improving the performance of wireless systems [3], [4], [5]. One application of the RIS is the coverage enhancement of wireless links where a RIS is used to enhance the signal-to-noise ratio (SNR) at the receiver [6].…”

Section: Introductionmentioning

confidence: 99%

Coverage Enhancements using RIS-Integrated NR

Tapio

Shojaeifard

Jagyasi

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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A reconfigurable intelligent surface (RIS) can be used to control the propagation of electromagnetic waves (EM). By deploying the RIS units in radio environment allows to steer the transmitted EM waves to areas that are otherwise shadowed by buildings or other geographic formations resulting in coverage enhancement. The gain offered by the RIS is due to its ability to focus the impinging signal to the desired direction. Hence, errors in the beamforming degrade the performance of a RISassisted link. The effect of the errors in the beamforming at the RIS are studied in this paper. Simulations show that the errors especially in the zenith angle can have significant effect on the uplink performance. With a RIS based on a 16 × 16 element reflect array errors greater than 1 • start to limit the achievable throughput. After the RIS is configured based on the direction information, the user equipment must use a proper precoder to capitalize the benefit of the path via the RIS. The usability of existing precoders from the 5G NR are also studied in this paper. In the simulated use case, utilizing a RIS with 5G NR precoders resulted in 3.6 dB gain in the uplink direction.

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System Performance Insights into Design of RIS-assisted Smart Radio Environments for 6G

Cited by 7 publications

References 14 publications

Reconfigurable Intelligent Surfaces: Channel Characterization and Modeling

Reconfigurable Intelligent Surfaces: Channel Characterization and Modeling

Adaptive Smart Radio Environment (ASRE): New Paradigm for Wireless Communication Networks

Coverage Enhancements using RIS-Integrated NR

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