PR-SRNN: Constellation Image Analysis Assisted Channel Estimation with 1 DMRS

Abstract

The time-varying and non-stationary channel characteristics caused by high Doppler effects is a significant challenge for modern wireless communication systems. Aiming at the high-speed scenarios, a constellation image analysis aided channel estimation using a hybrid neural network (NN) structure, named PR-SRNN, is proposed to combat Doppler effects. Only 1 demodulation reference signal (DMRS) pilot, rather than multiple pilots as in previous reports, is needed by the proposed PR-SRNN in high-speed scenarios. The impacts from Doppler effects are analyzed and compensated by an intelligent pattern recognition neural network (PRNN) based on the contaminated constellation images. The output layer of PRNN is then merged with a super resolution NN (SRNN) which preliminarily reconstructs the channel estimation in frequency domain. Adaptive features learning against Doppler effects and synergic compensation against multipath fading are learnt jointly. It is the first time to our knowledge that a pattern recognition NN on constellation images is introduced to the physical layer, functioning as expert-like analysis system. The simulation results based on both 3GPP statistical channel models and ray tracing show that PR-SRNN exhibits robustness against diverse degrees of Doppler effects beyond the pretrained scope. Amongst different framework candidates of super resolution (SR) NNs, residual convolution SRNN with channel attention has been selected regarding its performance superiority in terms of loss and convergence speed. Furthermore, the cross validation between PR-SRNN and our previously proposed SubSRNN which takes in extra semantic information as a 2^nd input proves that PR-SRNN is effective for Dopplers effects without extra side information to be reported.