中圖分類號： TN911.7
文獻(xiàn)標(biāo)識碼： A
DOI：10.16157/j.issn.0258-7998.212382
中文引用格式： 牛安東，苗碩，劉佳寧，等. GFDM中基于高階長短時記憶神經(jīng)網(wǎng)絡(luò)的自適應(yīng)均衡器[J].電子技術(shù)應(yīng)用，2022，48(8)：95-100.
英文引用格式： Niu Andong，Miao Shuo，Liu Jianing，et al. An adaptive equalizer based on high order LSTM in GFDM[J]. Application of Electronic Technique，2022，48(8)：95-100.

An adaptive equalizer based on high order LSTM in GFDM

Abstract： In the generalized frequency division multiplexing system(GFDM), in order to solve the problem of severe signal distortion in the sub-6 GHz frequency band channel of the vehicle-mounted mobile communication under the 5G network, an adaptive equalizer 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 prediction 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 improved.

Key words : generalized frequency division multiplexing(GFDM)；long short-term memory(LSTM)；high order neural network；generalized memory polynomial(GMP)