Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems

Autor:	Soltani, Mehran, Pourahmadi, Vahid, Sheikhzadeh, Hamid
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Information Theory
Druh dokumentu:	Working Paper
Popis:	In this paper, we present a downlink pilot design scheme for Deep Learning (DL) based channel estimation (ChannelNet) in orthogonal frequency-division multiplexing (OFDM) systems. Specifically, in the proposed scheme, a feature selection method named Concrete Autoencoder (ConcreteAE) is used to find the most informative locations for pilot transmission. This autoencoder consists of a concrete layer as the encoder and a multilayer perceptron (MLP) as the decoder. During the training, the concrete layer selects the most informative pilot locations, and the decoder reconstructs an approximate estimation of the channel. Eventually, the ChannelNet is trained on the output of the ConcreteAE aiming to reconstruct the ideal channel response. The estimation error results show that this approach outperforms the previously presented ChannelNet with a uniformly distributed pilot pattern, and its performance is comparable to the minimum mean square error (MMSE). Comment: 11 pages, 8 Figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.08980 Zobrazit plný text záznamu View this record from Arxiv