In this letter, we propose a learning based channel estimation scheme for orthogonal frequency division multiplexing (OFDM) systems in the presence of phase noise doubly-selective fading channels. Two-dimensional (2D) convolutional neural networks (CNNs) are employed effective training and tracking variation both as well time domain. The proposed network learns estimates coefficients entire tim...

Non-orthogonal multiple access principle applied in power domain (power-domain NOMA) enhances spectrum efficiency and connectivity of mobile communication systems through sharing between Orthogonal Multiple Access (OMA) other users. It is the almost main current trend for research standards 5G beyond. Spectrum generates multi-users interference (Multiple Interference, MAI) which destroys orthog...

Nowadays, most wireless communication standards utilize Orthogonal Frequency Division Multiplexing (OFDM) as their modulation technique. For coherent detection, the performance of such systems depends strongly on the accuracy of the channel estimation. One possible estimation technique is Pilotsymbol-Aided Channel Estimation (PACE), which allows the reconstruction of the channel by means of int...

This paper presents an on-line coupled filter extension to the method of Tsatsanis et al. [1] for identification of randomly time-varying linear channels. The channel is modelled as a tapped-delay line filter, with tap coefficients described by an unknown auto-regressive (AR) process. We propose an augmented-state Kalman filter for tracking the channel taps and estimating the mean channel respo...