5G NOMA Defense Application Environment and Stacked LSTM Network Architectures

Authors

  • Ravi Shankar Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India
  • Manoj Kumar Beuria Kalinga Institute of Industrial Technology University, Odisha, India
  • Sudhansu Sekhar Singh Kalinga Institute of Industrial Technology University, Odisha, India
  • Farkhanda Ana Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, Rajouri, J&K, India – 185234
  • Haider Mehraj Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, Rajouri, J&K, India – 185234
  • V. Gokula Krishnan Department of Computer Science and Engineering, RMK Engineering College, Kavaraipettai – 601206, Thiruvallur, Tamil Nadu, India

DOI:

https://doi.org/10.13052/jmm1550-4646.1914

Keywords:

5G, NOMA, B5G, PAPR, DNN, Nakagami-m fading channel, RNN, S-LSTM Fourier transform

Abstract

In 5G and beyond 5G wireless communication networks, the NOMA scheme is widely considered a major non-orthogonal access technique for improving system capacity and data rates. The main challenges in current NOMA systems are limited channel feedback and the difficulty of integrating it with advanced adaptive coding and modulation algorithms. This study analyses S-LSTM-based DL NOMA receivers in i.i.d. Nakagami-m fading channel circumstances as opposed to previously presented solutions. The LSTM has the advantage of responding dynamically to changing channel conditions. When compared to a typical NOMA system, a typical NOMA system has a 12% lower outage probability, a 39% increase in net throughput, and a maximum SER reduction of 48%. Complex modulated M-ary PSK and M-ary QAM data symbols are employed in D/L NOMA transmission. Classic receivers such as LS and MMSE are outperformed by the S-LSTM-based DL-NOMA receiver. The CP and non-linear clipping noise simulation curves compare the performance of the MMSE and LS receivers with that of the DL NOMA receiver in real-time propagation circumstances. The DL-based detector outperforms the MMSE for SNRs greater than 15 dB because the S-LSTM method is more robust than the clipping noise.

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Author Biographies

Ravi Shankar, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India

Ravi Shankar received the bachelor’s degree in Electronics and Communication Engineering from Jiwaji University Gwalior in 2006, the master’s degree in wireless and communication engineering from GGSIPU, New Delhi in 2012, and the philosophy of doctorate degree in Electronics & Communication Engineering from NIT Patna in 2020, respectively. His research areas include mobile security, deep learning, and social network analysis. He has been serving as a reviewer for many highly respected journals.

Manoj Kumar Beuria, Kalinga Institute of Industrial Technology University, Odisha, India

Manoj Kumar Beuria working as an Assistant professor in KIIT University, Odisha, India. He received his B. Tech degree in electronics and communication engineering from National institute of technology (NIT) Rourkela. Received his MTech degree in electronics and communication from Indian Institute of Technology (IIT) Roorkee. And currently pursuing his PHD from KIIT University. He has more than seven years of teaching experience and more than two years of industry experience. He teaches Digital Signal Processing (DSP), Communication system, Information theory And Coding, Signals And Systems, and mobile communication. Now he is doing research in 5G Communication, Wireless Sensor Network, IOT And Machine Learning.

Sudhansu Sekhar Singh, Kalinga Institute of Industrial Technology University, Odisha, India

Sudhansu Sekhar Singh has received a PhD in Engineering (Wireless Communication) from Jadavpur University, Kolkata, India and a M.E. in Electronic System and Communication Engineering from R.E.C (presently NIT) Rourkela, India. He is working as a Professor in School of Electronics Engineering, KIIT University, Bhubaneswar, India. He has more than 18 years of teaching experience and 8 years of other professional experience. More than 75 (seventy-five) publications in international journals and reputed international conference proceedings are to his credit. Also, he has guided more than twenty-five PG thesis and three doctoral theses. He has also examined several doctoral dissertations. His broad research area includes but not certainly limited to wireless and mobile communication, multicarrier CDMA, MIMO-OFDM, Wireless Sensor Networks.

Farkhanda Ana, Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, Rajouri, J&K, India – 185234

Farkhanda Ana received the bachelor’s degree in S.S.M College of Engineering and Technology, Affiliated to Kashmir University, the master’s degree in Department of Electronics and Communication, National Institute of Technology, Srinagar, and the philosophy of doctorate degree in Department of Electronics and Communication from Department of ECE, National Institute of Technology, Srinagar, respectively. She is currently working as an Assistant Professor at the Department of Electronics and Communication School of Engineering and Technology BGSB University, Rajouri, J&K-185131. Her research areas include mobile security, deep learning, and social network analysis. He has been serving as a reviewer for many highly respected journals.

Haider Mehraj, Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, Rajouri, J&K, India – 185234

Haider Mehraj received his B. Tech in Electronics and Communication Engineering from the Guru Nanak Dev University, Amritsar, India in 2009 and MTech in Communication and Information Technology from National Institute of Technology, Srinagar, India in 2011. He is currently pursuing PhD in Biometrics at the National Institute of Technology, Srinagar, India and 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, RMK Engineering College, Kavaraipettai – 601206, Thiruvallur, Tamil Nadu, India

V. Gokula Krishnan is working as a Professor at Department of Computer Science and Engineering, RMK Engineering College, Kavaraipettai – 601206, Thiruvallur, Tamil Nadu, India. His research areas include mobile security, deep learning, and social network analysis. He has been serving as a reviewer for many highly respected journals.

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Published

2022-08-25

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