An adaptive equalizer based on high order LSTM in GFDM
Niu Andong;Miao Shuo;Liu Jianing;Li Yingshan;College of Electronic Information and Optical Engineering , Nankai University;Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology , Nankai University;
In the generalized frequency division multiplexing system( GFDM), in order to solve the problem of severe signal distor-tion in the sub-6 GHz frequency band channel of the vehicle-mounted mobile communication under the 5G network, an adaptive e-qualizer based on high order long short-term memory( HO-LSTM) neural network structure is proposed. Based on the traditional high-order feedforward neural network( HO-FNN), HO-LSTM adaptive equalizer uses the generalized memory polynomial model( GMP) with lower complexity instead of Volterra model, and introduces LSTM neural network to make it more suitable for the pre-diction of complex nonlinear models. The results show that, compared with the traditional HO-FNN equalizer and LSTM equalizer,the equalization effect of the proposed HO-LSTM equalizer is significantly improved, and the system performance is further im-proved.