Joint Channel Estimation and Symbol Detection in MIMO-OFDM Systems: A Deep Learning Approach using Bi-LSTM

Nair, Aswathy K; Menon, Vivek

doi:10.1109/comsnets53615.2022.9668456

Cited by 19 publications

(23 citation statements)

References 21 publications

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“…An MLP-LSTM network is found in [277], with the joint solution working well under high-mobility scenarios with a velocity of up to 150km/h. Recently, bidirectional LSTM network architectures have been raised to prove their performance on MIMO-OFDM systems [278][279][280]. The evaluation has confirmed the superposition of conventional estimators.…”

Section: Recurrent Neural Networkmentioning

confidence: 96%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

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Section: Recurrent Neural Networkmentioning

confidence: 96%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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“…This allows RNNs to capture the time‐varying nature of the channel and provide more accurate estimates. Previous research on channel estimation has already demonstrated the benefits of using an RNN design for the CSI acquisition problem, as seen in References 26–28. By utilizing RNNs instead of FNNs, we can minimize the pilot overhead while obtaining satisfactory channel estimation results, as shown in Reference 29.…”

Section: Introductionmentioning

confidence: 94%

Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems

Shahabodini,

Mansoori,

Abouei

et al. 2023

Trans Emerging Tel Tech

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So far, various data‐driven approaches have been presented to obtain channel state information (CSI) in millimeter wave multiple‐input‐multiple‐output wireless networks. In almost all previous works, training and testing channels were assumed to have the same distribution, which may not be the case in practice. In this article, we address this challenge by proposing a learning framework that is a combination of a recurrent neural network (RNN) model and a deep neural network (DNN) for estimating CSI in a dynamic wireless communication environment. Furthermore, we use federated learning to train the learning‐based channel estimation model. More specifically, we introduce a two‐stage downlink pilot transmission procedure, where in the initial stage, long frame length downlink pilot signals are used to train the introduced RNN‐DNN model. Following that, users will receive shorter‐frame‐length pilot signals that can be used for CSI estimation. To speed up the training procedure of the proposed network, we first generate a pre‐trained model and then modify it according to the collected data samples. Simulation results demonstrate that, when the channel distribution is unavailable, the proposed approach performs significantly better than the most recent channel estimation algorithms in terms of estimation performance and computational complexity.

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“…17 A new structure incorporates DL to the massive MIMO for CE along direction-of-arrival estimation. 18 A combined pilot design along CE depending on DL is suggested to diminish the system MSE without considering estimators linearity. 19 The existing DL-based methods do not address the issue of CE in higher mobility environs for MIMO-OFDM.…”

Section: Introductionmentioning

confidence: 99%

“…Deep neural network (DNN) is used in the online DL‐based CE approach for doubly selective fading channels to follow the dynamic channel after being trained with simulated data offline 17 . A new structure incorporates DL to the massive MIMO for CE along direction‐of‐arrival estimation 18 . A combined pilot design along CE depending on DL is suggested to diminish the system MSE without considering estimators linearity 19 …”

Section: Introductionmentioning

confidence: 99%

Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM

Mydhili

2024

Int J Communication

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SummaryDual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM (DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM) is proposed in this manuscript. In OFDM, there is a non‐stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA‐COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%; lesser bit error rate 98.33%, 83.12%, and 88.96%; and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO‐OFDM system using deep neural network and MN‐based improved AMO model (DNN‐IAMO‐MIMO‐OFDM), MIMO‐OFDM systems using the deep learning and optimization (RBFNN‐PSO‐MIMO‐OFDM), and MIMO‐OFDM systems using hybrid neural network (HNN‐CSI‐MIMO‐OFDM) respectively.

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Joint Channel Estimation and Symbol Detection in MIMO-OFDM Systems: A Deep Learning Approach using Bi-LSTM

Cited by 19 publications

References 21 publications

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems

Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM

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