Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization

Schroeder, Rafaela; He, Jiguang; Juntti, Markku

doi:10.48550/arxiv.2106.10909

Cited by 5 publications

(7 citation statements)

References 20 publications

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“…In addition, SE changes with respect to the RIS location. In [52] and [55], it was observed that better SE performance was obtained when RIS was closer to BS.…”

Section: A Traditional Estimation Techniquesmentioning

confidence: 98%

“…The authors in [55] considered uplink RIS with a certain number of active sensors, where users send pilot signals to BS via RIS. This work aimed to recover the BS-RIS channel pa-rameters and extract the RIS-BS channel characteristics from the obtained signal.…”

Section: A Traditional Estimation Techniquesmentioning

confidence: 99%

“…In the worst case (i.e., the user is far away from both BS and RIS), dynamic RIS architecture outperformed the fixed one in terms of SE irrespective of the geographical deployment of RIS. The authors of [55] also determined that hybrid RIS achieves the best performance when RIS is closer to the user. Similar results were obtained even with a reduced number of active sensors, and therefore it was concluded that their location does not affect the performance.…”

Section: A Deployment Of Rismentioning

confidence: 99%

See 2 more Smart Citations

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

Balgabay¹,

Arzykulov²,

Galymzhan³

2022

Preprint

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<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>

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“…In addition, SE changes with respect to the RIS location. In [52] and [55], it was observed that better SE performance was obtained when RIS was closer to BS.…”

Section: A Traditional Estimation Techniquesmentioning

confidence: 98%

Section: A Traditional Estimation Techniquesmentioning

confidence: 99%

Section: A Deployment Of Rismentioning

confidence: 99%

See 1 more Smart Citation

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

Balgabay¹,

Arzykulov²,

Galymzhan³

2022

Preprint

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“…These are essentially normal reflective antenna arrays without the base band or RF units. These structures are often considered to be fully passive besides some semi-passive structures where certain set of elements receive and help with the channel estimation [21]. The antenna array type approach allows individual phase control at each antenna.…”

Section: Ris Modelingmentioning

confidence: 99%

Channel modeling for reflective phased array type RISs in mmWave networks

Kokkoniemi

Juntti

2021

Proceedings of the 5th ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems

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This paper looks into channel modeling of the phased array type reconfigurable intelligent surfaces (RISs) in the millimeter Wave band (mmWave, 30-300 GHz). The RISs have been under intense investigation lately for their ability to provide control on the mmWave sparse channels. This has been shown to improve coverage and signal levels in environments where line-of-sight (LOS) paths cannot be guaranteed. In this paper, gain properties of the RISs in LOS channels are analysed with respect to frequency and size. It is shown that the RISs provide the best gain compared to LOS links at lower frequencies due to larger RIS size for the same number of antenna elements. On the other hand, high gain RISs tend to push the near field far away from the array in the low frequencies. These observations mean that different parts of the mmWave band have advantages and disadvantages when utilizing RIS assisted channels. CCS CONCEPTS• Networks → Physical links.

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“…The former allows the CE to be formulated as a sparserecovery problem and solved using compressed sensing (CS) tools [10]- [12], which are known to require a few measurements to have an accurate estimate, see [13]- [17]. In [13], by exploiting the low-rank nature of the mmWave channels, we have proposed the TRICE framework, which formulates the CE in RIS-aided mmWave MIMO systems as a two-stage multidimensional sparse-recovery problem.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Channel Estimation and Reflection Design for RIS-Aided Millimeter-Wave MIMO Communication Systems

Gherekhloo¹,

Ardah²,

Almeida³

et al. 2021

Preprint

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In this work, we consider both channel estimation and reflection design problems in point-to-point reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) MIMO communication systems. First, we show that by exploiting the low-rank nature of mmWave MIMO channels, the received training signals can be written as a low-rank multiway tensor admitting a canonical polyadic (CP) decomposition. Utilizing such a structure, a tensor-based RIS channel estimation method (termed TenRICE) is proposed, wherein the tensor factor matrices are estimated using an alternating least squares method. Using TenRICE, the transmitter-to-RIS and the RIS-toreceiver channels are efficiently and separately estimated, up to a trivial scaling factor. After that, we formulate the beamforming and RIS reflection design as a spectral efficiency maximization problem. Due to its non-convexity, we propose a heuristic noniterative two-step method, where the RIS reflection vector is obtained in a closed form using a Frobenius-norm maximization (FroMax) strategy. Our numerical results show that TenRICE has a superior performance, compared to benchmark methods, approaching the Cramér-Rao lower bound with a low training overhead. Moreover, we show that FroMax achieves a comparable performance to benchmark methods with a lower complexity.

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Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization

Cited by 5 publications

References 20 publications

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

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

Channel modeling for reflective phased array type RISs in mmWave networks

Tensor-Based Channel Estimation and Reflection Design for RIS-Aided Millimeter-Wave MIMO Communication Systems

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