Examination of the Bi-LSTM Based 5G-OFDM Wireless Network Over Rayleigh Fading Channel Conditions


  • Sanjaya Kumar Sarangi Department of Computer Science, Utkal University, Bhubaneswar, India
  • Rasmita Lenka School of Electronics, KIIT Deemed to be University, Campus 12, Odisha, India
  • Ravi Shankar Department of Electronics and Communication Engineering, SR University, Warangal, India
  • Haider Mehraj Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, J & K, India
  • V. Gokula Krishnan Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai – 602105, Tamil Nadu, India






Fifth generation (5G) wireless networks’ system performance is dependent on having perfect knowledge of the channel state information (CSI). Deep learning (DL) has helped improve both the end-to-end reliability of 5G and beyond fifth generation (B5G) networks and the computational complexity of these networks. This work uses the Bi-linear long short-term memory (Bi-LSTM) scheme to examine the overall performance of the 5G orthogonal frequency division multiplexing (OFDM) technology. The least squares (LS) channel estimation scheme is a famous scheme employed to estimate the fading channel coefficients due to their lower complexity without the prior CSI. However, this scheme has an exceedingly high CSI error. Using pilot symbols (PS) and loss functions, this work has proposed the Bi-LSTM 5G OFDM estimators to improve the channel estimation obtained by the LS approach. All simulation analysis uses convex optimization (CO) software (CVX software) and stochastic gradient descent (SGD). When combined with many PS (72) and a cross-entropy loss function, the proposed Bi-LSTM outperforms the long-short-term memory (LSTM) cross-entropy, LS, and minimum mean square error (MMSE) estimators in low, medium, and high signal-to-noise ratio (SNR) regimes. The computational and training times of Bi-LSTM and LSTM DL estimators are also compared. Because of its DNN design, it can evaluate massive datasets, find hidden statistical patterns and characteristics, establish underlying relationships, and transfer what it has learnt to other contexts. Statistical analysis of the bit error rate (BER) reveals that Bi-LSTM outperforms the MMSE in terms of accurate channel prediction.


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Author Biographies

Sanjaya Kumar Sarangi, Department of Computer Science, Utkal University, Bhubaneswar, India

Sanjaya Kumar Sarangi, Senior Member IEEE and ACM. Dept. of Computer Science, Utkal University, distinguished background in Academic, Research and Industry sectors combined with 22 years of experiences for the knowledge towards achieving the institutions objectives though skill, knowledge and to nourish the global educational system. He holds PhD in Computer Science, Master of Technology in Comp.Sc & Engg. He was visiting Doctoral Fellow in the University of California, USA. His Research findings are in Wireless Ad hoc and Sensor Network, IDS, Mobile Communications, Geospatial Science and Remote Sensing, Climate Change and Disaster prediction system. He has number of publications in Journals, Patents and Conferences. His interest also in Information and Communication Technology (ICT) to enhance and optimize the information and worldwide research that can lead to be improved the student learning and teaching methods. Also undertakes the ICT based support for the smooth functioning of e-learning system for the universities.

Rasmita Lenka, School of Electronics, KIIT Deemed to be University, Campus 12, Odisha, India

Rasmita Lenka received the B.TECH degree in Electronics & Tele Comm. Engineering from Biju Pattnaik. University of Technology, in 2004, M.TECH degree in Communication System from Veer SurendraSai University of Technology (VSSUT) ODISHA in 2010. She completed her Ph.D in KIIT Deemed To Be University. She has 16 years of teaching experience and currently working as an Assistant Professor in School of Electronics KIIT Deemed University, Bhubaneswar. Her research area broadly includes signal processing and Image processing and its application. Her interest includes: Wireless sensor network, Mobile computing AI.

Ravi Shankar, Department of Electronics and Communication Engineering, SR University, Warangal, India

Ravi Shankar received his BE degree in Electronics and Communication Engineering from Jiwaji University, Gwalior, India, in 2006. He received his MTech degree in Electronics and Communication Engineering from GGSIPU, New Delhi, India, in 2012. He received a PhD in Wireless Communication from the National Institute of Technology Patna, Patna, India, in 2019. He was an assistant professor at MRCE Faridabad, from 2013 to 2014, where he was engaged in researching wireless communication networks. He is presently an assistant professor at SR University, Warangal, India. His current research interests cover cooperative communication, D2D communication, IoT/M2M networks and networks protocols. He is a student member of IEEE.

Haider Mehraj, Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, J & K, India

Haider Mehraj received his B. Tech in Electronics and Communication Engineering from the Guru Nanak Dev University, Amritsar, India in 2009, MTech in Communication and Information Technology from National Institute of Technology, Srinagar, India in 2011 and PhD in Biometrics at the National Institute of Technology, Srinagar, India in 2022. He is currently working as Assistant Professor in BGSB University, Rajouri, India. He has several national and international publications to his credit. His research interests include Biometrics, Image Processing, Deep Learning, and Pattern Recognition.

V. Gokula Krishnan, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai – 602105, Tamil Nadu, India

V. Gokula Krishnan is currently working as Professor in the Department of Computer Science and Engineering in Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India. He has completed his Under-Graduation (BE) in Anna University, Post-Graduation (M.Tech) in Dr. MGR University and Ph.D in Sathyabama Institute of Science and Technology, Chennai. He has more than 16 years of teaching experience in various colleges in Chennai and Hyderabad. He has published several papers in SCI/Scopus/WoS indexed journals and also he has presented various papers in National/International Conferences. His area of interest includes Computer Networks, Computer Architecture, Data Structures, Software Engineering etc. He serves as the guest editor, editorial member and also as reviewer in many reputed international journals. He is a member of Professional Bodies like ISTE, IAENG, CSI and IEEE etc


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How to Cite

Sarangi, S. K. ., Lenka, R. ., Shankar, R. ., Mehraj, H. ., & Krishnan, V. G. . (2023). Examination of the Bi-LSTM Based 5G-OFDM Wireless Network Over Rayleigh Fading Channel Conditions. Journal of Mobile Multimedia, 19(04), 1067–1106. https://doi.org/10.13052/jmm1550-4646.1948




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