Work place: School of Electronics and Communication Engineering at REVA University, Bengaluru, India
E-mail: riyaz@reva.edu.in
Website:
Research Interests: Artificial Intelligence, Machine Learning, IoT
Biography
Mohammed Riyaz Ahmed received a bachelor’s degree in Electronics and Communication Engineering and master’s degree in Computer Network Engineering from VTU, Belgaum, India, in 2007 and 2010 respectively, and a Ph.D. degree in Electronics and Communication Engineering from Jain University, India, in 2016. He is currently working as an associate professor in the School of Electronics and Communication Engineering at REVA University. His research interests include RFID, Smart devices, IoT, Artificial Intelligence, Cognitive Sciences, Machine Learning, Technology Intervention for the Elderly, Memristors, and Neuromorphic Engineering. He is a recognized mentor from Texas Instruments and recipient of grants from the IEEE standards committee. He is a Senior Member of IEEE. He can be contacted at email: riyaz@reva.edu.in
By Suprith P. G. Mohammed Riyaz Ahmed Mithileysh Sathiyanarayanan
DOI: https://doi.org/10.5815/ijcnis.2024.05.10, Pub. Date: 8 Oct. 2024
Multiple access technologies have grown hand in hand from the first generation to the 5th Generation (5G) with both performance and quality improvement. Non-Orthogonal Multiple Access (NOMA) is the recent multiple access technology adopted in the 5G communication technology. Capacity requirements of wireless networks have grown to a large extent with the penetration of ultra-high-definition video transmission, Internet of Things (IoT), and virtual reality applications taking ground in the recent future. This paper develops the Physical Layer Network Coding (PNC) for collision resolution in a NOMA environment with two users. Traditionally NOMA uses Successive Interference Cancellation (SIC) for collision resolution. While additionally a decoding algorithm is added along with SIC to improve the performance of the collision resolution. MATLAB-based simulation is developed on the NOMA environment with two users using Viterbi coding, Low-Density Parity Check (LDPC), and Turbo coding. Performance parameters of Bit Error Rate (BER) and throughput are compared for these three algorithms. It is observed that the Turbo coding performed better among these three algorithms both in the BER and throughput. The BER obtained from the SIC- Turbo is found to be performing well with an increase of about 14% from the ordinary SIC implementation. The performance of the collision resolution has increased by 13% to 14% when joint decoding techniques are used and thus increasing the throughput of the NOMA paradigm.
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