AAT model based channel estimation for mmWave massive MIMO systems

Abstract

To solve the problems of temporal correlation and susceptibility to noise in millimeter wave massive MIMO channels, which result in decreased channel estimation accuracy, a novel channel estimation method based on an improved temporal convolutional network was proposed.The channel matrices obtained from simulation were feed into the system as two-dimensional image data.The temporal correlation was utilized for feature fusion and an attention in attention network was constructed to enhance the system’s ability to extract deep channel features.Then, AAN was integrated into the temporal convolutional network for training.Finally, the system outputted a denoised two-dimensional image, namely, the channel estimation matrix.Simulation results demonstrate that the proposed method not only exhibits good performance and complexity compared to conventional channel estimation methods but also maintains robustness when the test scenario changes.

Keywords

• massive MIMO channel
• temporal convolutional neural network
• channel estimation
• attention in attention network