Neural network based channel identification and compensation

Abstract

This letter proposes a neural network based channel identification and compensation methods for an OFDM system. Under the fast fading environment, pilot-aided channel estimation suffers from channel state fluctuation particularly in the last part of the packet. The proposed approach can estimate the whole transition of channel states and efficiently compensate the channel variation using the generalization capability of a neural network. The computer simulation results clarify its effectiveness via improved BER performance even under the stringent Doppler shift.

Generalized Regression Neural Network Based Fast Fading Channel Tracking Using Frequency-Domain CSI Smoothing

“…Fig. 13 presents the BER performances of the conventional PCE, MLFNN [14], GRNN-based method [15], and the proposed method that employs SG filtering. The BER performance of the PCE is seriously deteriorated because decision errors are frequently happened in the last part of the packet; it shows an error floor.…”

“…Because of the low reliability of DFCE in the last part of the packet, additional approach is needed to stabilize the whole CSI transition using partially obtained CSI at the beginning part of the packet. To resolve this problem, we previously proposed the NN-aided approach [14], [15]. This method applies a NN for channel tracking using partially obtained CSI by PCE and DFCE.…”

“…Previous work [14] employed a fully connected 2-layer feed-forward neural network. MLFNN has a layered structure constructed by neurons, and every neuron passes signals to neurons of the next layer.…”

“…In view of the above background, we previously proposed a multilayer feedforward neural network (MLFNN) based channel tracking scheme which is effective even in fast-moving environments [14]. It employs MLFNN to estimate the whole channel state transition and compensates the channel variation using the generalization capability derived from the relationship between inputs and outputs.…”

Uplink Null-Space Expansion for Multiuser Massive MIMO in Time-Varying Channels Under Unknown Interference

“…Content may change prior to final publication. [36], [37]. It will also be helpful for our proposal to find the optimal null-space from trained network via past channel transition.…”