Weighted TSVR Based Nonlinear Channel Frequency Response Estimation for MIMO-OFDM System

Li, Shuo; Lei, Weimin; Zhang, Wei; Wang, Xiaoyu; Wang, Lidong

doi:10.1109/access.2020.3045706

Cited by 3 publications

(4 citation statements)

References 32 publications

(48 reference statements)

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“…Recently, the research on estimators based on regression algorithms has been scarce, which is understood due to the growth of NNs and RL solutions. However, some research has accomplished promising results regarding the SVR for OFDM and MIMO-OFDM systems [172,173]. Therefore, it is a research direction to apply these estimators for OFDM variations or other multicarrier systems, extending them to MIMO schemes.…”

Section: Discussion and Research Directionsmentioning

confidence: 99%

“…The linear, polynomial, and nonlinear regression algorithms are early basic applications of ML concepts for channel estimation. Support vector regression (SVR) has recently been raised as a potential regression strategy in AI-aided channel estimation techniques [172,173]. The evolutionary algorithm has also been applied to channel estimation, whereas the genetic algorithm is more widely used than other evolutionary techniques.…”

Section: Classical Learning-aided Channel Estimation Techniquesmentioning

confidence: 99%

“…The main concept is to find a function that best fits the training data behavior to perform predictions. For example, linear [175][176][177], polynomial [178], 2D nonlinear [99,179,180], and support vector [172,173,[181][182][183][184][185][186][187] regressions have been employed in channel estimation for multicarrier systems. Regression algorithms go under the supervised learning paradigm.…”

Section: Regressionmentioning

confidence: 99%

“…The algorithm leads to lower BER under the same SNR compared with the LS and feedback estimators, from a good approximation to a perfect estimation. The wavelet transform was used to obtain weights to improve twin SVR (TSVR) channel estimation in pilot-assisted OFDM systems operating in fast selective fading channels [172,173]. The training samples are weighted according to their distance from the mean values filtered by the wavelet transform.…”

Section: Support Vector Regressionmentioning

confidence: 99%

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

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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: Discussion and Research Directionsmentioning

confidence: 99%

Section: Classical Learning-aided Channel Estimation Techniquesmentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

Section: Support Vector Regressionmentioning

confidence: 99%

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

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Performance Analysis of Multi-Rate Coherent BPSK-OCDMA Network for Multimedia Applications

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2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)

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Review of the evolution of statistical models and deep learning models: Based on the perspective of ARIMA and SVM

Wang

2023

HSET

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The importance of the evolution of statistical models and deep learning models, such as autoregressive integrated moving average model (ARIMA) and support vector machine (SVM), has drawn much attention from scholars. This paper investigates the evolution of statistical models and deep learning models based on the perspective of ARIMA and SVM. Then, this paper finds that there are some limitations of the literature on ARIMA and SVM, including less attention to causal inference, the scarce application of the future option, and the deep exploration of parameter optimization.

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Weighted TSVR Based Nonlinear Channel Frequency Response Estimation for MIMO-OFDM System

Cited by 3 publications

References 32 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

Performance Analysis of Multi-Rate Coherent BPSK-OCDMA Network for Multimedia Applications

Review of the evolution of statistical models and deep learning models: Based on the perspective of ARIMA and SVM

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