FEDERATED CHANNEL LEARNING FOR INTELLIGENT REFLECTING SURFACES WITH FEWER PILOT SIGNALS
Yükleniyor...
Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Channel estimation is a critical task in intelligent reflecting surface (IRS)-assisted wireless systems due to the uncertainties imposed by environment dynamics and rapid changes in the IRS configuration. To deal with these uncertainties, deep learning (DL) approaches have been proposed. Previous works consider centralized learning (CL) approach for model training, which entails the collection of the whole training dataset from the users at the base station (BS), hence introducing huge transmission overhead for data collection. To address this challenge, this paper proposes a federated learning (FL) framework to jointly estimate both direct and cascaded channels in IRSassisted wireless systems. We design a single convolutional neural network trained on the local datasets of the users without sending them to the BS. We show that the proposed FL-based channel estimation approach requires approximately 60% fewer pilot signals and it exhibits 12 times lower transmission overhead than CL, while maintaining satisfactory performance close to CL. In addition, it provides lower estimation error than the state-of-the-art DL-based schemes.
Açıklama
IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) -- JUN 20-23, 2022 -- Trondheim, NORWAY
Anahtar Kelimeler
Channel Estimation; Federated Learning; Intelligent Reflecting Surfaces; Machine Learning; Massive Mimo, Wave Massive Mimo
Kaynak
2022 Ieee 12th Sensor Array and Multichannel Signal Processing Workshop (Sam)
WoS Q Değeri
N/A
