SISO-OFDM SISO-OFDM channel estimation apparatus using deep neural network based on adaptive ensemble supervised learning and method thereof

摘要

The present invention relates to an SISO-OFDM channel estimation device using a deep neural network based on adaptive ensemble supervised learning, and a method thereof. According to the present invention, provided is an SISO-OFDM channel estimation method which comprises: a step of, in responding to training symbols modulated and sent from a transmission end, separately inputting the training symbols respectively received through a plurality of path channels to a plurality of independent deep neural networks to make the plurality of deep neural networks respectively learn; a step of calculating weighted values to be respectively applied to subcarrier of predetermined symbols by a path channel based on reception power of each subcarrier of the training symbols received by a plurality of path channels; a step of, in responding to first symbols modulated and sent from the transmission end, separately inputting the first symbols respectively received through the plurality of path channels to the plurality of pre-learned deep neural networks to acquire an output value, and deducing a detection symbol with respect to the first symbols from the output value; and a step of coupling detection symbols respectively deduced from the plurality of neural networks by subcarrier to acquire a coupling signal, applying a weighted value corresponding to the subcarrier to the coupling signal, conducting ensemble learning, and detecting each modulation symbol by the subcarrier of the first symbols. According to the present invention, a deep neural network is applied to a receiving end to effectively estimate and compensate a channel, and adaptive ensemble supervised learning is used for overcoming an overfitting problem of the deep neural network, thereby increasing reliability of a system.