Back close

Performance Analysis of Polar Codes for 5G Wireless Communication Network

Publication Type : Journal Article

Publisher : 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, Springer International Publishing

Source : 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, Springer International Publishing, Cham (2021)

Url : https://link.springer.com/chapter/10.1007/978-3-030-47560-4_29

ISBN : 9783030475604

Campus : Bengaluru

School : Department of Electronics and Communication Engineering, School of Engineering

Department : Electronics and Communication

Year : 2021

Abstract : Polar Codes have lately been chosen for control channels in physical layer (Layer 1) as short code communication in 5G for error correction. The standard clearly defines the algorithm for polar encoder. However, the standard doesn't explicitly state the algorithm for polar decoders. Polar decoders are of key value for the equipment manufacturers/chip vendors for showing better system performance with control channels. This work focuses on exploring and analyzing some of the polar decoder algorithms, validating performance by simulation, and identifying suggestions for better performance characteristics. Performance with respect to block error rate versus SNR (signal-to-noise ratio), execution time, and complexity is discussed and compared with the legacy decoders. A new methodology is proposed to overcome the long processing cycle times and larger memory size requirement with proper selection of the algorithm in consideration of the standard deviation of the demodulated I&Q soft bit samples. Results from SC (successive cancellation) and SCL (successive cancellation list) algorithms along with the proposed improvement in decoder are explained and discussed.

Cite this Research Publication : J. Pechetti, Hallingar, B., Prasad, P. V. N. D., and Dr. Navin Kumar, “Performance Analysis of Polar Codes for 5G Wireless Communication Network”, 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, 2021.

Admissions Apply Now