Robust Deep Learning Approaches for Wireless Communication Systems
Pages 120 - 125
Abstract
Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is a key technology for wireless transmission systems. But if the peak-to-average power ratio (PAPR) is too high, OFDM symbols can be distorted at the MIMO OFDM transmitter. It will make it harder for the MIMO OFDM receiver in the channel estimation and signal detection phase. To explore the possibilities of Deep Learning (DL) in particular and machine learning in general in the MIMO OFDM system and to serve as a foundation for future research, we develop a DL-based MIMO OFDM receiver using DL in this work. From there, DL models can help filter out the noise caused by the high PAPR problem and change some parts at the receiver to improve the receiver’s performance in the point-to-point MIMO OFDM system. The simulations show that the suggested DL-based receivers have a lower bit error rate (BER) than conventional receivers.
References
[1]
Myung-Sun Baek, Sangwoon Kwak, Jun-Young Jung, Heung Mook Kim, and Dong-Joon Choi. 2019. Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters. IEEE Transactions on Broadcasting 65, 3 (2019), 636–642. https://doi.org/10.1109/TBC.2019.2891051
[2]
Yong Soo Cho, Jaekwon Kim, Won Y Yang, and Chung G Kang. 2010. MIMO-OFDM wireless communications with MATLAB. John Wiley & Sons.
[3]
Zainab Sh. Hammed, Siddeeq Y. Ameen, and Subhi R. M. Zeebaree. 2021. Massive MIMO-OFDM Performance Enhancement on 5G. In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). 1–6. https://doi.org/10.23919/SoftCOM52868.2021.9559117
[4]
Hengtao He, Chao-Kai Wen, and Shi Jin. 2018. Bayesian Optimal Data Detector for Hybrid mmWave MIMO-OFDM Systems With Low-Resolution ADCs. IEEE Journal of Selected Topics in Signal Processing 12, 3 (2018), 469–483. https://doi.org/10.1109/JSTSP.2018.2818063
[5]
Ke He, Zizhi Wang, Wei Huang, Dan Deng, Junjuan Xia, and Liseng Fan. 2020. Generic Deep Learning-Based Linear Detectors for MIMO Systems Over Correlated Noise Environments. IEEE Access 8 (2020), 29922–29929.
[6]
Ke He, Zizhi Wang, Dong Li, Fusheng Zhu, and Lisheng Fan. 2020. Ultra-reliable MU-MIMO detector based on deep learning for 5G/B5G-enabled IoT. Physical Communication 43 (2020), 101181. https://doi.org/10.1016/j.phycom.2020.101181
[7]
Xiaodong Li and L.J. Cimini. 1998. Effects of clipping and filtering on the performance of OFDM. IEEE Communications Letters 2, 5 (1998), 131–133. https://doi.org/10.1109/4234.673657
[8]
Xiaodong Li and Leonard J Cimini. 1997. Effects of clipping and filtering on the performance of OFDM. In 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion, Vol. 3. IEEE, 1634–1638.
[9]
Abdelhamid Riadi, Mohamed Boulouird, and Moha M’Rabet Hassani. 2020. Least Squares Channel Estimation of an OFDM Massive MIMO System for 5G Wireless Communications. In Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.2. 440–450.
[10]
Anuj Singal and Deepak Kedia. 2020. Performance Analysis of MIMO-OFDM System Using SLM with Additive Mapping and U2 Phase Sequence for PAPR Reduction. Wireless Personal Communications 111 (2020), 1377–1390.
[11]
Bolei Wang, Mengnan Jian, Feifei Gao, Geoffrey Ye Li, and Hai Lin. 2019. Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems. IEEE Transactions on Signal Processing 67, 23 (2019), 5893–5908. https://doi.org/10.1109/TSP.2019.2949502
[12]
Junjuan Xia, Ke He, Wei Xu, Shengli Zhang, Lisheng Fan, and George K. Karagiannidis. 2020. A MIMO Detector With Deep Learning in the Presence of Correlated Interference. IEEE Transactions on Vehicular Technology 69, 4 (2020), 4492–4497. https://doi.org/10.1109/TVT.2020.2972806
[13]
Liangyuan Xu, Feifei Gao, Wei Zhang, and Shaodan Ma. 2021. Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers. In 2021 IEEE Wireless Communications and Networking Conference (WCNC). 1–6. https://doi.org/10.1109/WCNC49053.2021.9417512
[14]
Hao Ye, Geoffrey Ye Li, and Biing-Hwang Juang. 2018. Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems. IEEE Wireless Communications Letters 7, 1 (2018), 114–117. https://doi.org/10.1109/LWC.2017.2757490
[15]
Weile Zhang, Qinye Yin, and Feifei Gao. 2016. Computationally Efficient Blind Estimation of Carrier Frequency Offset for MIMO-OFDM Systems. IEEE Transactions on Wireless Communications 15, 11 (2016), 7644–7656. https://doi.org/10.1109/TWC.2016.2605678
Index Terms
- Robust Deep Learning Approaches for Wireless Communication Systems
Recommendations
IGCC for PAPR Reduction in OFDM Systems Over the Nonlinearity of SSPA and Wireless Fading Channels
High peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems seriously impacts power efficiency in radio frequency section due to the nonlinearity of high-power amplifiers. In this article, an improved gamma ...
Biorthogonal frequency division multiple access
Orthogonal frequency division multiple access (OFDMA) multicarrier modulation is the key technology in wireless communication. However, OFDMA can just get the best performance under strict carrier frequency synchronisation. Furthermore, the rectangular ...
An Efficient Low Complexity Transform for DCT-OFDM Based Wireless Communication System
A precise method for the performance of a new DCT based OFDM system using a low complexity T-transform that combines the WHT and the DCT into a single fast orthonormal unitary transform (T-DCT) on AWGN and Rayleigh fading channels is proposed. The T-...
Comments
Information & Contributors
Information
Published In
December 2023
266 pages
ISBN:9798400709043
DOI:10.1145/3638985
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 March 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Japan Science and Technology Agency
Conference
ICIT 2023
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 6Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)2
Reflects downloads up to 26 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format