Deep Learning Aided Channel Estimation Approach for 5G Communication Systems
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The defining feature of the Fifth Generation (5G) mobile communication systems is going to be Multiple Input Multiple Output (MIMO) transmission scheme, which utilizes the multipath diversities to achieve beamforming and increase spectral efficiency. However, these MIMO algorithms rely on accurate channel parameters. To improve the accuracy of the channel coefficients, the study presents a Deep Learning (DL) based approach that uses the 5G Demodulation Reference Signals (DMRS) as training sequence and Deep Neural Networks (DNN) as training and prediction network in a MIMO scenario. The DNN is trained with training data obtained by applying Least Squares (LS) method to the received pilot signals and by comparing it to Clustered Delay Line (CDL) channel model. The DNN is then used to predict real-time channel coefficients. The results show that the model improves channel estimation performance by reducing the effects of noise, thus improving the Normalized Mean Square Error (NMSE) versus Signal-to-Noise Ratio (SNR) metric of the MIMO system.
Açıklama
4th IEEE Global Power, Energy and Communication Conference (IEEE GPECOM) -- JUN 14-17, 2022 -- Cappadocia, TURKEY
Anahtar Kelimeler
5G, Channel Estimation, DMRS, Deep Learning, Convolutional Neural Network
Kaynak
2022 Ieee 4th Global Power, Energy and Communication Conference (Ieee Gpecom2022)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
