2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2021
DOI: 10.1109/spawc51858.2021.9593172
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Channel Estimation with Simultaneous Reflecting and Sensing Reconfigurable Intelligent Metasurfaces

Abstract: Reconfigurable Intelligent Surfaces (RISs) are envisioned to play a key role in future wireless communications, enabling programmable radio propagation environments. They are usually considered as almost passive planar structures that operate as adjustable reflectors, giving rise to a multitude of implementation challenges, including the inherent difficulty in estimating the underlying wireless channels. In this paper, we focus on the recently conceived concept of Hybrid Reconfigurable Intelligent Surfaces (HR… Show more

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Cited by 19 publications
(11 citation statements)
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References 38 publications
(58 reference statements)
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“…Its footprint can be reduced and the coupling to the sampling waveguide can be mitigated by keeping this waveguide near cutoff. Such hybrid RISs were recently considered in [49], [50] for facilitating explicit and implicit channel estimation as well as flexible network management with reduced overhead compared to cascaded channel estimation with passive RISs.…”
Section: B Operation Modesmentioning
confidence: 99%
“…Its footprint can be reduced and the coupling to the sampling waveguide can be mitigated by keeping this waveguide near cutoff. Such hybrid RISs were recently considered in [49], [50] for facilitating explicit and implicit channel estimation as well as flexible network management with reduced overhead compared to cascaded channel estimation with passive RISs.…”
Section: B Operation Modesmentioning
confidence: 99%
“…This is, admittedly, the most general approach, though it requires establishing pertinent communication links, as discussed in Section IV-A. Conversely, combining the DRL-based intelligent controller of the RIS with the actual RIS may greatly reduce the overhead of the control information exchange, by also taking advantage of the recently proposed hybrid RIS hardware architectures with embedded sensing capabilities [105], [110]- [120]. In such configurations of smart wireless environments, far fewer control information exchanges for realizing DRL will be required, albeit with the added cost of the computationally and sensingly autonomous RIS.…”
Section: B Open Research Challengesmentioning
confidence: 99%
“…In the light of the large numbers of free parameters to configure, usually introduced by the phase-tunable RIS metaatoms, it is expected that there are considerable performance improvements to be exploited with the help of trained ANNs, compared to conventional non-convex optimization approaches which are usually iterative, complex, and without convergence guarantees. In particular, deep learning has already been successfully assessed over a number of pertinent scopes for smart radio environments, like rate maximization through analog beamforming [105]- [108], power maximization [109], compressive sensing estimation [110], channel estimation with possibly hybrid reflecting and sensing RISs [105], [110]- [120], secrecy maximization [121], and even real-time imaging [122]. Most works focus on applications on the physical layer, whereas others like [106], [123] are intended for deploying deep-learning-based systems on the Medium Access Control (MAC) layer.…”
Section: Introductionmentioning
confidence: 99%
“…They constitute a low-cost solution for improving the propagation conditions for edge users [4] and millimeter wave (mmWave) communications [5], as well as energyefficient communication [6]. Furthermore, RISs can also provide additional functions beyond signal enhancement, such as integrated sensing and reflecting [7], channel estimation [8], and user localization [9].…”
Section: Introductionmentioning
confidence: 99%