Deep learning-based channel estimation for OFDM-IM systems over Rayleigh fading channels

[ X ]

Tarih

2024

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Wiley

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Deep learning (DL)-based channel estimation for orthogonal frequency division multiplexing with index modulation (OFDM-IM) under Rayleigh fading channel conditions is presented in this paper. A deep neural network (DNN) is utilized to estimate the channel response in simulations. The proposed DNN is trained using the channel coefficient derived through the least squares (LS) method. Then channel estimation is conducted using the trained DNN. Within the DNN, the long short-term memory (LSTM) layer is included as the hidden layer. Different scenarios are handled in simulations and the proposed DNN is compared with traditional channel estimation methods. The simulations demonstrate that the DL-based channel estimation significantly surpasses the LS and minimum mean-square error (MMSE) techniques. This paper presents a deep learning (DL)-based channel estimation method for orthogonal frequency division multiplexing with index modulation (OFDM-IM) under Rayleigh fading channel conditions. The proposed method employs a Long Short-Term Memory (LSTM)-based network for channel estimation. The results demonstrate that the DL-based channel estimation significantly outperforms the least squares (LS) and minimum mean-square error (MMSE) techniques. image

Açıklama

Anahtar Kelimeler

channel estimation, deep learning, index modulation, least squares (LS), minimum mean-square error (MMSE), OFDM-IM

Kaynak

International Journal of Communication Systems

WoS Q Değeri

Q3

Scopus Q Değeri

Q2

Cilt

37

Sayı

18

Künye