Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO
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Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done via centralized learning (CL), where the whole training dataset is collected from the users at the base station (BS). This approach introduces huge communication overhead for data collection. In this paper, to address this challenge, we propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We develop FL-based channel estimation schemes for both conventional and RIS (intelligent reflecting surface) assisted massive MIMO (multiple-input multiple-output) systems, where a single CNN is trained for two different datasets for both scenarios. We evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower overhead than CL, while maintaining satisfactory performance close to CL. Furthermore, the proposed architecture exhibits lower estimation error than the state-of-the-art ML-based schemes.
Açıklama
Anahtar Kelimeler
Channel Estimation; Training; Data Models; Computational Modeling; Radio Frequency; Massive Mimo; Wireless Communication; Channel Estimation; Federated Learning; Machine Learning; Centralized Learning; Massive Mimo, Reconfigurable Intelligent Surfaces; Beamforming Design; Antenna Selection; Deep; Systems
Kaynak
Ieee Transactions on Wireless Communications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
21
Sayı
6
