Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach

Article Computer Science, Artificial Intelligence

Deep learning for estimating the channel in orthogonal frequency division multiplexing systems

Sowjanya Ponnaluru et al.

Summary: This paper investigates the method of using a deep learning model to evaluate channels and recover data in wireless communication systems, and validates the ability of this model by comparing the bit error rate under different digital modulation techniques.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

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Article Engineering, Electrical & Electronic

Deep learning-based pilot-assisted channel state estimator for OFDM systems

Mohamed Hassan Essai Ali

Summary: This study introduces an online deep learning-based channel state estimator for OFDM wireless communication systems using LSTM neural networks. The proposed estimator outperforms traditional LS and MMSE estimators when limited pilots are used, and does not require prior knowledge of channel statistics, showing promising performance in channel state estimation.

IET COMMUNICATIONS (2021)

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Article Telecommunications

Artificial neural network based estimation of sparse multipath channels in OFDM systems

Habib Senol et al.

Summary: In order to enhance the performance of transceiver in frequency selective fading channel environment, an Orthogonal Frequency Division Multiplexing (OFDM) system is utilized to combat inter-symbol-interference. This study investigates a channel estimation scheme for an OFDM system under sparse multipath channel conditions using artificial neural networks (ANN). Utilizing ANN's learning capability, the proposed technique is able to provide improved system throughput by modeling and obtaining a channel estimate efficiently. A performance-efficient ANN based sparse channel estimator is proposed with lower computational cost compared to Matching Pursuit (MP) and Orthogonal MP (OMP) algorithms commonly used in compressed sensing literature. Despite some slight performance loss in certain simulation scenarios due to lower computational complexity advantage, the majority of computer simulations demonstrate that the low complexity ANN based channel estimator outperforms MP and OMP algorithms in terms of mean squared error and symbol error rate.

TELECOMMUNICATION SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence