Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave

Bayraktar, Murat; Palacios, Joan; González–Prelcic, Nuria; Zhang, Charlie Jianzhong

doi:10.1109/spawc51304.2022.9833999

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…The computational complexity of the multidimensional OMP strategy [94] was based on compressed channel estimation, which was greatly reduced by not computing on the large dictionary directly. The OMP algorithm was modified to exploit the joint corner-domain sparse structure of the cascaded channels associated with different users.…”

Section: Compressed Sensing-based Algorithms For Ris Channel Estimationmentioning

confidence: 99%

Review on Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication System

et al. 2023

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With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation.

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Section: Compressed Sensing-based Algorithms For Ris Channel Estimationmentioning

confidence: 99%

Review on Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication System

et al. 2023

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“…The computational complexity of the multidimensional OMP strategy was [93] based on compressed channel estimation, which was greatly reduced by not computing on the large dictionary directly. The OMP algorithm was modified to exploit the joint corner-domain sparse structure of the cascaded channels associated with different users.…”

Section: Compressed Sensing-based Algorithms For Ris Channel Estimationmentioning

confidence: 99%

Review on Channel Estimation of Reconfigurable Intelligent Surface Assisted Wireless Communication System

Yun¹,

Wang²,

Zhou³

et al. 2023

Preprint

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With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of the fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surfaces (RIS), one of the potential technologies for the sixth generation (6G), has received a lot of attention. Since it is easier to deploy, consumes less power, and is inexpensive. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This paper conducts a comprehensive investigation of the existing channel estimation methods of RIS, analysis and comparison of channel model building and CSI acquisition schemes in different frequency bands, in addition to a comprehensive description of generic channel estimation methods, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook in the future development of RIS channel estimation.

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“…To minimize the sum of squares of errors, we make ( 16)?s first order partial derivative in relation to Ĥ equal to 0; i.e., ĤLS = X H X −1 X H Y, (17) At this point, the sum of squares of the obtained estimates is the minimum, which is the solution of the LS channel estimation. The LS algorithm is frequently used in practice, has a low computing complexity, and is reasonably easy to implement.…”

Section: Least Square (Ls) Algorithmmentioning

confidence: 99%

“…Many different approaches have recently been put forth to investigate channel estimation in RIS-assisted communication systems [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. The work [ 11 ] specifically proposed an ON/OFF-based channel-estimating approach to directly estimate cascaded channels.…”

Section: Introductionmentioning

confidence: 99%

“…However, this algorithm introduced a hypothetical condition that is difficult to achieve in practical situations. The work [ 17 ] proposed a multidimensional orthogonal matching pursuit strategy for compressive channel estimation, computing the projections on a set of independent dictionaries instead of a single large dictionary to achieve high-accuracy channel estimation at reduced complexity. However, multiple independent dictionaries bring additional pilot overhead.…”

Section: Introductionmentioning

confidence: 99%

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Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications

Liu

Deng

Peng

2023

Sensors

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The large number of estimated parameters in a reconfigurable intelligent surface (RIS) makes it difficult to achieve accurate channel estimation accuracy in 6G. Therefore, we suggest a novel two-phase channel estimation framework for uplink multiuser communication. In this context, we propose an orthogonal matching pursuit (OMP)-based linear minimum mean square error (LMMSE) channel estimation approach. The OMP algorithm is used in the proposed algorithm to update the support set and pick the columns of the sensing matrix that are most correlated with the residual signal, which successfully reduces pilot overhead by removing redundancy. Here, we use LMMSE’s advantages for handling noise to address the problem of inadequate channel estimation accuracy when the signal-to-noise ratio (SNR) is low. Simulation findings demonstrate that the proposed approach outperforms least-squares (LS), traditional OMP, and other OMP-based algorithms in terms of estimate accuracy.

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Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave

Cited by 8 publications

References 7 publications

Review on Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication System

Review on Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication System

Review on Channel Estimation of Reconfigurable Intelligent Surface Assisted Wireless Communication System

Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications

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