Channel Estimation Techniques for RIS-Assisted Communication: Millimeter-Wave and Sub-THz Systems

Noh, Song; Lee, Junse; Lee, Gilwon; Seo, Kyungsik; Sung, Youngchul; Yu, Heejung

doi:10.1109/mvt.2022.3158765

“…Swindlehurst et al [10] consider the identifiability of the models as a function of the pilots and RIS training states, and further consider special cases such as single-input single-output (SISO) and MIMO, availability or unavailability of a direct link, and narrowband vs. wideband. Noh et al [11] concentrate on characteristics of RIS channels in terahertz (THz) and mmWave channels. Zheng et al [12] provide a survey of RIS channel estimation that enumerates the problems and outcomes, but does not dwell on methodology or characterization of the methods.…”

Section: A Contributions and Distinctions Of The Present Workmentioning

confidence: 99%

Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

Shamasundar

¹

,

Daryanavardan

²

,

Nosratinia

³

2023

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Reconfigurable intelligent surfaces (RIS) are passive controllable arrays of small reflectors that direct electromagnetic energy towards or away from the target nodes, thereby allowing better management of signals and interference in a wireless network. The RIS has the potential for significantly improving the performance of wireless networks. Unfortunately, RIS also multiplies the number of Channel State Information (CSI) coefficients between the transmitter and receiver, which magnifies the challenges in estimating and communicating the channel state information. Furthermore, the simplicity and cost-effectiveness of the passive RIS also implies that the incoming links are not locally estimated at the RIS, and fresh pilots are not inserted into outgoing RIS links. This introduces new challenges for training and estimation of channel state information. The rapid growth of the literature on CSI acquisition in RIS-aided systems has been accompanied by variations in the underlying assumptions, models, and notation, which can obscure the similarities and differences of various techniques, and their relative merits. This paper presents a comprehensive exposition of principles and approaches in RIS channel estimation. The basic ideas underlying each class of techniques are reduced to their simplest form under a unified model and notation, and various approaches within each class are discussed. Several open problems in this area are identified and highlighted.INDEX TERMS Reconfigurable intelligent surfaces, channel estimation, overhead reduction, spectral efficiency, sparse recovery, machine learning techniques, channel modeling.

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“…Swindlehurst et al [10] consider the identifiability of the models as a function of the pilots and RIS training states, and further consider special cases such as single-input single-output (SISO) and MIMO, availability or unavailability of a direct link, and narrowband vs. wideband. Noh et al [11] concentrates on characteristics of RIS channels in terahertz (THz) and mmWave channels. Zheng et al [12] provides a survey of RIS channel estimation that enumerates the problems and outcomes, but does not dwell on methodology or characterization of the methods.…”

Section: A Contributions and Distinctions Of The Present Workmentioning

confidence: 99%

Channel Training and Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

Shamasundar¹,

Daryanavardan²,

Nosratinia³

2023

Preprint

0

View full text Add to dashboard Cite

Reconfigurable intelligent surfaces (RIS) are passive controllable arrays of small reflectors that direct electromagnetic energy towards or away from the target nodes, thereby allowing better management of signals and interference in a wireless network. The RIS has the potential for significantly improving the performance of wireless networks. Unfortunately, RIS also multiplies the number of Channel State Information (CSI) coefficients between the transmitter and receiver, which magnifies the challenges in estimating and communicating the channel state information. Furthermore, the simplicity and cost-effectiveness of the passive RIS also implies that the incoming links are not locally estimated at the RIS, and fresh pilots are not inserted into outgoing RIS links. This introduces new challenges for training and estimation of channel state information. The rapid growth of the literature on CSI acquisition in RIS-aided systems has been accompanied by variations in the underlying assumptions, models, and notation, which can obscure the similarities and differences of various techniques, and their relative merits. This paper presents a comprehensive exposition of principles and approaches in RIS channel estimation. The basic ideas underlying each class of techniques are reduced to their simplest form under a unified model and notation, and various approaches within each class are discussed. Several open problems in this area are identified and highlighted.

show abstract

“…Channel estimation in mmWave and THz systems employing beamforming and beam management technologies is challenging due to several factors, such as the complexity of the channel (estimation of a large number of channel coefficients accurately), limited coherence time (short coherence time makes accurate channel estimation difficult), susceptibility to impairments (signal propagation at these frequencies is more susceptible to attenuation, scattering, and path loss), sparsity of the multipath components (signals at these frequencies are directional and sparse with few dominant paths, requiring systems to capture and model these paths), hardware constraints (limited hardware resources make channel estimation more challenging since it needs to be done efficiently and with low complexity), and beam misalignment (misalignment might occur due to changes in the user location or mobility, which can degrade the beamforming performance) [212][213][214]. Addressing these challenges requires developing advanced channel estimation techniques that can accurately estimate the channel parameters while also being computationally efficient and scalable.…”

Section: Channel Estimationmentioning

confidence: 99%

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

Brilhante¹,

Manjares²,

Moreira³

et al. 2023

Preprint

2

0

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The performance of modern wireless communication systems is highly dependent on the adoption of multiple antennas and the associated signal processing. In 5G and 6G networks, beamforming and beam management become challenging tasks due to aspects such as user mobility, increased number of antennas, and the adoption of higher frequencies. Artificial intelligence, and more specifically, machine learning, are efficient tools to reduce the complexity involved in generating beams and the overhead associated with beam management without sacrificing system performance. Therefore, AI-aided beamforming and beam management have received a lot of attention recently. This article presents a complete survey on this topic, emphasizing open problems and promising directions. The discussion includes architectural and signal processing aspects of modern beamforming and beam management. The article presents communication problems and respective solutions using centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

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Channel Estimation Techniques for RIS-Assisted Communication: Millimeter-Wave and Sub-THz Systems

Cited by 45 publications

References 13 publications

Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

Channel Training and Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

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