[vc_empty_space][vc_empty_space]
Performance Evaluation of Autoencoder for Coding and Modulation in Wireless Communications
Xu J.a, Chen W.a, Ai B.a, He R.a, Li Y.a, Wang J.a, Juhana T.c, Kurniawan A.c
a School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
b Key Lab. of All Optical Network and Advanced Telecommunication Network of Ministry of Education, Beijing Jiaotong University, Beijing, China
c School of Electrical Engineering and Informatics, Institute of Technology Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.The end-to-end autoencoder is a novel and attracting concept to innovate communication system architecture. In its training stage, the end-to-end autoencoder needs differentiable channel models to execute back-propagation algorithm. This shortage impedes the further development of the end-to-end autoencoder. Estimating the channel model by reinforcement learning is a good way to alleviate this problem. However, these training methods increase the difficulty in the training stage. In this paper, we set up a general channel model and employ the measured channel data as input data to alleviate this problem. We compare the performance of the end-to-end autoencoder and Hamming code modulated by Binary Phase Shift Keying under additive white Gaussian noise channel and Rayleigh channel and explore the performance of the end-to-end encoder under the real scenarios. Furthermore, we investigate the performance of the end-to-end autoencoder under mismatched channels. The results demonstrate that the performance of the end-to-end autoencoder is similar with Hamming code and has a significant performance improvement under mismatched channels.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Additive white Gaussian noise channel,General channels,Measured channel,Modulation coding,performance evaluation,Rayleigh channel,Training methods,Wireless communications[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]deep learning,modulation coding,performance evaluation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This work was supported in part by the Beijing natural Haidian joint fund under Grant L172020, National key research and development program under Grant 2016YFE0200900, the National Science Foundation of China under Grant 61725101, U1834210 and 61961130391, the Royal Society Newton Advanced Fellowship under Grant NA191006, Major projects of Beijing Municipal Science and Technology Commission under Grant Z181100003218010, State Key Lab of Rail Traffic Control and Safety under Grant RCS2018ZZ007 and RCS2019ZZ007.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/WCSP.2019.8928071[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]