Deep Learning Techniques for OFDM Systems

Abstract

Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier technique in communication system owing to its robustness against multipath fading and less complexity. Deep learning (DL) approach is more accurate and efficient than traditional approaches, which seeks more attention not only in the fields of natural language processing, video processing, speech and audio processing but also in the field of communication systems. The application of DL technique in OFDM systems supports better system performance, peak-to-average power ratio (PAPR) reduction, and improvement in spectral efficiency. This paper presents a detailed review of recent developments in DL techniques for OFDM systems to improve its performance in terms of bit error rate, signal-to-noise ratio, and PAPR. Various DL frameworks available for architectural design and processing are also explained in this article. The paper is concluded with a discussion on research gaps and challenges for future investigation and development.

KEYWORDS:

• Artificial neural network
• Bit error rate
• Channel state information
• Deep learning
• Framework
• Orthogonal frequency division multiplexing
• Peak-to-average power ratio
• 5G wireless technology

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

M. Meenalakshmi

M Meenalakshmi, Associate Member, IETE, pursued her Bachelor of Engineering degree in electronics and communication engineering from Mepco Schlenk Engineering College, Sivakasi, India in 2008 and Master of Engineering degree in embedded system technologies from Sri Sai Ram Engineering College, Chennai, India in 2013. She is currently a PhD research scholar in the Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India. Her research interests are artificial intelligence, wireless communication and deep learning. Email: [email protected].

Saurabh Chaturvedi

Saurabh Chaturvedi, Senior Member, IEEE, obtained his BTech degree in electronics and communication engineering (ECE) from Jaypee Institute of Information Technology (JIIT), Noida, India in 2005, MTech degree in VLSI design from Centre for Development of Advanced Computing (CDAC), Noida in 2008, and PhD degree in electrical and electronic engineering from the University of Johannesburg, South Africa in 2018. During doctoral programme, he was a visiting researcher with the National institute for Research and Development in Microtechnologies, IMT Bucharest, Romania. He is currently working as an assistant professor in the Department of ECE, JIIT, Noida. He has several research publications in peer-reviewed international journals and conferences. His current research is focused on 5G communication, artificial intelligence and machine learning.

Vivek K. Dwivedi

Vivek K Dwivedi, Member, IEEE, received the Bachelor of Engineering degree from RGPV, Bhopal, India in 2003, Master of Engineering degree from the Birla Institute of Technology, Mesra, Ranchi, India in 2006 and PhD degree in ECE from the Jaypee University of Information Technology, Solan, India in 2012. He was a senior visiting researcher in wireless communications with the University of Pretoria, South Africa from 2014 to 2015. He is currently an associate professor with the Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida India. He has authored several research articles in refereed international journals and conferences. His primary research interest includes machine learning, artificial intelligence, 5G and optical wireless communication. He is serving as an associate editor of IEEE Access Journal. Email: [email protected]