Deep Compressed Sensing-Based Cascaded Channel Estimation for RIS-Aided Communication Systems

Xie, Wenwu; Xiao, Jianguo; Zhu, Peng; Yu, Chao; Yang, Liang

doi:10.1109/lwc.2022.3147590

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…For common end-to-end mode DNN signal detection models, the received complex-valued signals are generally used directly as inputs to the network; however, singularly treating the overall received signals as inputs to the network is not rigorous. Inspired by the literature [12], this paper converts the complex-valued signal input into the training set into an equivalent real-valued model, and C 1×M N upgrades the original input linear layer to C 2×M N , That is to say, split the received complex-valued vector y into real part and imaginary part, integrate it into one dimension, and use it as the input of the deep neural network input unit.…”

Section: A Data Preprocessingmentioning

confidence: 99%

“…It effectively mitigates Doppler frequency offset resulting from high-speed movement and can transform linear time-varying channels, featuring multipath propagation fading and Doppler frequency offset fading, into linear time-invariant channels within the Delay Doppler (DD) domain [11]. OTFS outperforms Orthogonal Frequency Division Multiplexing (OFDM) in high Doppler environments and can be seamlessly integrated into OFDM by incorporating additional pre-processing and post-processing blocks [12], enhancing overall system compatibility. Additionally, Non-Orthogonal Multiple Access (NOMA) stands out as another crucial technology for meeting the requirements of 6G wireless networks.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Signal Detection in Non-orthogonal Multiple Access Orthogonal Time-Frequency Space

Yu,

Wu,

Zhang

et al. 2024

Preprint

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The Non-orthogonal Multiple Access Orthogonal Time-Frequency Space (NOMA-OTFS) modulation technique can address the multiuser communication challenges in high-mobility scenarios. To tackle the high model complexity in the downlink detection system for NOMA-OTFS modulation, this paper introduces a signal detection algorithm based on deep neural network technology. The use of deep learning techniques enhances the receiver model, reduces model complexity, and improves system efficiency. The algorithm’s stability, reliability, and scenario applicability are ensured through offline training of symbol-level datasets. Simulation results indicate that the proposed algorithm outperforms existing schemes in terms of Bit Error Rate (BER), demonstrating superior detection performance and robustness.

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Section: A Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Signal Detection in Non-orthogonal Multiple Access Orthogonal Time-Frequency Space

Yu,

Wu,

Zhang

et al. 2024

Preprint

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“…There are many researches on the obtaining of CSI in the IRS-assisted wireless communications system (e.g. see [18,19]). Under the system model, the channel from the kth WD and the AP is acquired by…”

Section: Communication Modelmentioning

confidence: 99%

“…There are many researches on the obtaining of CSI in the IRS‐assisted wireless communications system (e.g. see [18, 19]). Under the system model, the channel from the k th WD and the AP is acquired by

\begin{eqnarray} {h}_k &=& {{\bm{g}}}_2\ {{{\bm \Theta }}}_{2,k}{\bm{R}}{{{\bm \Theta }}}_{1,k}{{\bm{h}}}_{1,\ k} + {{\bm{g}}}_2{{\bm \Theta }}_2^k{{\bm{h}}}_{2,\ k} + {{\bm{g}}}_1{{\bm \Theta }}_1^k{{\bm{h}}}_{1,\ k}\nonumber \\ & =& {{\bm{g}}}_2\ {{{\bm \Theta }}}_{2,k}{\bm{R}}{{{\bm \Theta }}}_{1,k}{{\bm{h}}}_{1,\ k} + {{\bm{\tilde{h}}}}_{2,\ k}{{\bm{\theta }}}_{2,k} + {{\bm{\tilde{h}}}}_{1,\ k}{{\bm{\theta }}}_{1,k}\end{eqnarray}

where

{{{\bm \Theta }}}_{u,k} = diag\{ {{{\bm{\theta }}}_{u,k}} \}

depicts the IRS u diagonal reflection matrix with

u \in \{ {1,2} \}

, and

{\overset{h}{true}}_{u, k} = g_{u} d

…”

Section: System Modelmentioning

confidence: 99%

Computation rate optimization for double‐intelligent reflecting surface aided mobile edge computing system

Xie

et al. 2023

IET Communications

Self Cite

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In order to improve the performance of the mobile edge computing (MEC) system, the intelligent reflecting surface (IRS) has recently been included. This paper investigates the computation performance of an IRS‐aided MEC system, where an access point services multi‐MEC devices by utilizing two distributed IRSs to operate a partial offloading strategy. Through collaboratively constructing the cooperative passive beamforming at the two IRSs, the central processing unit (CPU) frequency, the offloading time allocation, and the transmit power of users, an optimization problem is constructed to maximize the sum computation rate of the proposed system. To tackle this non‐convex issue, the authors first illustrate that the design of passive beamforming is independent to optimize other variables and propose to solve it alternatively. Then the joint optimization problem of other parameters is proved to be a convex problem and is resolvable by adopting the Lagrange dual approach. Compared with the benchmark schemes, the simulation outcomes demonstrated that the performance of the proposed system is vastly improved by deploying two distributed IRSs.

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“…In [85], Xie et al investigated an estimation method based on deep compressive sensing (DCS) for RIS-aided massive MIMO channels, with the aim of reducing the pilot overhead. Specifically, they developed a ResU-Net by combining U-Net with the DCS framework while introducing residual learning.…”

Section: Application Of Deep Learning In Channel Estimation For Ris-a...mentioning

confidence: 99%

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

Lv,

Luo

2023

Electronics

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With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed.

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Deep Compressed Sensing-Based Cascaded Channel Estimation for RIS-Aided Communication Systems

Cited by 13 publications

References 21 publications

Deep Learning for Signal Detection in Non-orthogonal Multiple Access Orthogonal Time-Frequency Space

Deep Learning for Signal Detection in Non-orthogonal Multiple Access Orthogonal Time-Frequency Space

Computation rate optimization for double‐intelligent reflecting surface aided mobile edge computing system

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

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