MTCNet: Multi-Task Complex Network for Concurrent Channel Estimation and Equalization

Convolutional neural networks (CNNs) have been widely adopted in various fields and the wireless communication domain is no exception; researchers leveraged CNNs for communication tasks such as channel estimation and equalization. They showed the potential of CNNs for these applications but were insufficient to be considered for real world implementation due to the limited applicability and high execution latency. In this paper, we propose a novel complex number-based neural network architecture, a multi-task loss function, and a training scheme to learn channel estimation and equalization tasks simultaneously in an end-to-end manner. The resulting single-stage CNN architecture, Multi-Task Complex Network (MTCNet), achieves better performance than any other conventional methods. In addition, to the best of our knowledge, MTCNet is the first CNN-based channel estimation and equalization that can operate with various subcarrier settings with inferencing time below 1 ms.