RoemNet: Robust Meta Learning Based Channel Estimation in OFDM Systems
Hengxi Mao, Hancheng Lu, Yujiao Lu, Daren Zhu · 2019
Recently, in order to achieve performance improvement in scenarios where the channel is either unknown, or too complex for an analytical description, Neural Network (NN) based channel estimation is introduced in Orthogonal Frequency Division Multiplexing (OFDM) systems. However, this kind of learning method is not reliable enough when the conditions of online deployment of the common NNs are not consistent with the channel models used in the training stage. Furthermore, common NNs need plenty of data as well as time to be trained, which is not suitable for the OFDM communication network with time varying channels. To tackle these challenges, we propose a novel meta learning based channel estimation approach called RoemNet. The most distinctive characteristic of RoemNet is that it involves a meta-learner that can learn from the environment of different channels. With the update of meta-learner, RoemNet is robust enough to solve new channel learning tasks using only a small number of pilots. Furthermore, RoemNet can alleviate the effect of Doppler spread and significantly improve the Bit Error Ratio (BER) performance under different channel environments. Experiment results demonstrate that the proposed RoemNet outperforms existing channel estimation methods including existing learning methods under various scenarios.