TY - GEN
T1 - TC-MIMONet: A Learning-based Transceiver for MIMO Systems with Temporal Correlations
AU - Chen, Chunhui
AU - Wang, Zihao
AU - Mao, Yuyi
AU - Wu, Hao
AU - Bai, Bo
AU - Zhang, Gong
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 11871297 and the Tsinghua University Initiative Scientific Research Program.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - Data-driven approaches have recently emerged as promising remedies for communication system designs, which leverage deep learning techniques for automated development and optimization. In this paper, we revisit the designs of multi-input multi-output (MIMO) wireless systems and investigate the end-to-end learning for MIMO systems with temporal correlations. Our objective is to develop a MIMO transceiver to improve the communication performance by making fully use of the available temporal information. Although the end-to-end learning framework has been applied to various communication systems, existing designs largely rely on memoryless autoencoders (AEs) and overlook the time dependency. To overcome this issue, we propose a novel learning-based MIMO transceiver, namely, the TC-MIMONet, which extends the conventional memoryless AE-based transceivers by customizing two neural network components with memory. In particular, a long short-term memory (LSTM)-based CSI predictor is adopted at the transmitter, while a two-timescale LSTM-based decoder is developed for the receiver. Simulation results show that TC-MIMONet achieves significant block error rate reduction compared to two baseline schemes without utilizing the available temporal information.
AB - Data-driven approaches have recently emerged as promising remedies for communication system designs, which leverage deep learning techniques for automated development and optimization. In this paper, we revisit the designs of multi-input multi-output (MIMO) wireless systems and investigate the end-to-end learning for MIMO systems with temporal correlations. Our objective is to develop a MIMO transceiver to improve the communication performance by making fully use of the available temporal information. Although the end-to-end learning framework has been applied to various communication systems, existing designs largely rely on memoryless autoencoders (AEs) and overlook the time dependency. To overcome this issue, we propose a novel learning-based MIMO transceiver, namely, the TC-MIMONet, which extends the conventional memoryless AE-based transceivers by customizing two neural network components with memory. In particular, a long short-term memory (LSTM)-based CSI predictor is adopted at the transmitter, while a two-timescale LSTM-based decoder is developed for the receiver. Simulation results show that TC-MIMONet achieves significant block error rate reduction compared to two baseline schemes without utilizing the available temporal information.
KW - Deep learning
KW - autoencoder (AE)
KW - long short-term memory (LSTM)
KW - multi-input multi-output (MIMO)
KW - temporal correlations
UR - http://www.scopus.com/inward/record.url?scp=85112446466&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Spring51267.2021.9448981
DO - 10.1109/VTC2021-Spring51267.2021.9448981
M3 - Conference article published in proceeding or book
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Helsinki, Finland
ER -