Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
Yükleniyor...
Dosyalar
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.
Açıklama
Anahtar Kelimeler
Channel estimation, MIMO communication, Complexity theory, Training, Machine learning, Surface waves, Array signal processing, Deep learning, channel estimation, large intelligent surfaces, massive MIMO, Antenna Selection, Design
Kaynak
Ieee Wireless Communications Letters
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
9
Sayı
9
