Improving ANN based Channel Identification and Compensation using GRNN Method under Fast Fading Environment

Omura, Takaki; Hoeur, Nythanel; Maruta, Kazuki; Ahn, C. J.

doi:10.1109/atc.2019.8924557

Cited by 3 publications

(15 citation statements)

References 9 publications

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“…In smoothing with LWMA, the best BER performance is observed when M is 2 and 3. compared to MA, the range of performance degradation due to M is wide. From (15), since LWMA performs smoothing with a larger amount of information than MA when M becomes too large, even harmful effects are captured and smoothed, resulting in significant degradation of BER. Fig.…”

Section: Computer Simulation a Simulation Resultsmentioning

confidence: 99%

“…Fig. 13 presents the BER performances of the conventional PCE, MLFNN [14], GRNN-based method [15], and the proposed method that employs SG filtering. The BER performance of the PCE is seriously deteriorated because decision errors are frequently happened in the last part of the packet; it shows an error floor.…”

Section: Computer Simulation a Simulation Resultsmentioning

confidence: 99%

“…Because of the low reliability of DFCE in the last part of the packet, additional approach is needed to stabilize the whole CSI transition using partially obtained CSI at the beginning part of the packet. To resolve this problem, we previously proposed the NN-aided approach [14], [15]. This method applies a NN for channel tracking using partially obtained CSI by PCE and DFCE.…”

Section: B Neural Network (Nn)mentioning

confidence: 99%

“…In order to eliminate the iterative training process without impairing the generalization capability, we previously employed GRNN for the NN part [15]. GRNN was originally developed by Donald Specht in 1991 [26].…”

Section: ) Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

“…Meanwhile, there was a need for improvement in terms of estimation accuracy and computational complexity. We then conceived its extension by using a generalized regression neural network (GRNN) [15]. A one-pass learning process of GRNN can eliminate the iterative training process without impairing the generalization capability.…”

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confidence: 99%

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Generalized Regression Neural Network Based Fast Fading Channel Tracking Using Frequency-Domain CSI Smoothing

Kojima

Omura

et al. 2021

IEEE Access

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In the high mobility environment, the channel state information (CSI) in the last part of the packet is different from the beginning part's actual channel. This phenomenon degrades channel estimation accuracy, and hence it is necessary to be compensated to realize reliable communications. Decision feedback channel estimation (DFCE) has been widely considered as the channel tracking approach. It still causes estimation errors due to the decision-making process in the presence of time and frequency selective fading environments. To address these issues, this paper newly proposes a generalized regression neural network (GRNN) based channel tracking scheme incorporated with frequency-domain CSI smoothing. The latter part is the key to improve the dependability of the training data sets. Computer simulation results confirm that the proposed scheme can achieve superior BER performance and the lower root mean square error (RMSE) value of estimated CSI better than the conventional ones.

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Section: Computer Simulation a Simulation Resultsmentioning

confidence: 99%

Section: Computer Simulation a Simulation Resultsmentioning

confidence: 99%

Section: B Neural Network (Nn)mentioning

confidence: 99%

Section: ) Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

mentioning

confidence: 99%

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Generalized Regression Neural Network Based Fast Fading Channel Tracking Using Frequency-Domain CSI Smoothing

Kojima

Omura

et al. 2021

IEEE Access

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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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Regression CNN Based Fast Fading Channel Tracking Using Decision Feedback Channel Estimation

Wang

Kojima

et al. 2023

Journal of Signal Processing

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In a high-speed moving mobile environment, the channel state information (CSI) in the last part of the packet is different from the actual channel in the beginning part. Therefore, the channel estimation accuracy is degraded, especially when a small number of pilot symbols are used to ensure transmission efficiency. For the above reasons, it is necessary to compensate for CSIs to achieve reliable communication. Decision feedback channel estimation (DFCE) has been widely considered to be one of the channel tracking methods. However, the presence of time and frequency selective fading environments still causes estimation errors due to the decision-making process. We focused on the time-frequency domain response of the CSIs, which can be represented as a two-dimensional image. This paper newly proposes a regression convolutional neural network (CNN) based channel tracking scheme using the time-frequency domain response of the CSIs by DFCE for training and prediction to solve these problems. Computer simulation results demonstrate that the proposed scheme can achieve higher BER performance than the conventional schemes.

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Improving ANN based Channel Identification and Compensation using GRNN Method under Fast Fading Environment

Cited by 3 publications

References 9 publications

Generalized Regression Neural Network Based Fast Fading Channel Tracking Using Frequency-Domain CSI Smoothing

Generalized Regression Neural Network Based Fast Fading Channel Tracking Using Frequency-Domain CSI Smoothing

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

Regression CNN Based Fast Fading Channel Tracking Using Decision Feedback Channel Estimation

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