Adaptive Deep Neural Network for Non-Stationary Wireless Channels

Deep neural networks (DNNs) have been widely used in recent years for wireless communication applications, including channel estimation. However, DNN performs well for environments (data) that it has been trained for. However, it is still challenging to work in a non-stationary system where the statistical characteristics of the environment change with time. This paper introduces an online adaptation approach that allows the neural network to dynamically change to cope with the non-stationary channel estimation. The idea is to use the existing detected packets to continuously update the weights of the DNN. Simulation results show that our approach can significantly improve the performance of the DNN in non-stationary environments whereas concurrent state-of-the-art algorithms fail.