Multi Kernel Polar Code using Nut cracker optimization based GAN for Successive Cancellation Decoder to attain low latency and high efficiency

Multi-Kernel Polar Code is a type of polar code that utilizes multiple kernels for information encoding and decoding. The selection and optimization of multiple kernels can be challenging. Previous research has demonstrated that existing deep learning models may achieve high decoding accuracy and sp...

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Veröffentlicht in:	Evolving systems Jg. 16; H. 3; S. 93
Hauptverfasser:	Pushpa, B. Yamini, Panda, Sunita
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Artificial Intelligence Binary phase shift keying Codes Coding Coding theory Complex Systems Complexity Decoding Efficiency Energy consumption Engineering Error correction & detection Generative adversarial networks Generative artificial intelligence Literature reviews Machine learning Methods Optimization Original Paper Random noise Wireless communications GEN AI Multi Kernel Polar Code Decoder Transmission matrix Encoder Successive cancellation
ISSN:	1868-6478, 1868-6486
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Abstract	Multi-Kernel Polar Code is a type of polar code that utilizes multiple kernels for information encoding and decoding. The selection and optimization of multiple kernels can be challenging. Previous research has demonstrated that existing deep learning models may achieve high decoding accuracy and speed for polar code when the block length can be very tiny. Its speed, however, dramatically drops with longer codes because of the huge network structure. A successful Generative Artificial Intelligence (GEN AI) is developed in this work for decoding polar codes. The input sequence has been encoded using multiple kernel polar codes, giving a polar encoded output. After encoding the message using the multi-kernel polar encoder, the resulting bits are mapped to binary phase-shift keying (BPSK) symbols prior to transmission. The Gaussian noise term with zero mean and variance in additive white Gaussian noise (AWGN) is used to receive the signal. The improved Generative Adversarial Network (GAN) improves the decoder performance under different channel conditions after the signals have been transmitted via the channel. Computation is employed to determine the approximate reliability of the bit channel. The proposed approach achieves 95.30% of accuracy, 4.70% error, 91.70% precision and 96.80% specificity. Thus, the designed optimized GAN model is the best option for successive cancellation in the decoder. Graphical abstract
AbstractList	Multi-Kernel Polar Code is a type of polar code that utilizes multiple kernels for information encoding and decoding. The selection and optimization of multiple kernels can be challenging. Previous research has demonstrated that existing deep learning models may achieve high decoding accuracy and speed for polar code when the block length can be very tiny. Its speed, however, dramatically drops with longer codes because of the huge network structure. A successful Generative Artificial Intelligence (GEN AI) is developed in this work for decoding polar codes. The input sequence has been encoded using multiple kernel polar codes, giving a polar encoded output. After encoding the message using the multi-kernel polar encoder, the resulting bits are mapped to binary phase-shift keying (BPSK) symbols prior to transmission. The Gaussian noise term with zero mean and variance in additive white Gaussian noise (AWGN) is used to receive the signal. The improved Generative Adversarial Network (GAN) improves the decoder performance under different channel conditions after the signals have been transmitted via the channel. Computation is employed to determine the approximate reliability of the bit channel. The proposed approach achieves 95.30% of accuracy, 4.70% error, 91.70% precision and 96.80% specificity. Thus, the designed optimized GAN model is the best option for successive cancellation in the decoder. Graphical abstract Multi-Kernel Polar Code is a type of polar code that utilizes multiple kernels for information encoding and decoding. The selection and optimization of multiple kernels can be challenging. Previous research has demonstrated that existing deep learning models may achieve high decoding accuracy and speed for polar code when the block length can be very tiny. Its speed, however, dramatically drops with longer codes because of the huge network structure. A successful Generative Artificial Intelligence (GEN AI) is developed in this work for decoding polar codes. The input sequence has been encoded using multiple kernel polar codes, giving a polar encoded output. After encoding the message using the multi-kernel polar encoder, the resulting bits are mapped to binary phase-shift keying (BPSK) symbols prior to transmission. The Gaussian noise term with zero mean and variance in additive white Gaussian noise (AWGN) is used to receive the signal. The improved Generative Adversarial Network (GAN) improves the decoder performance under different channel conditions after the signals have been transmitted via the channel. Computation is employed to determine the approximate reliability of the bit channel. The proposed approach achieves 95.30% of accuracy, 4.70% error, 91.70% precision and 96.80% specificity. Thus, the designed optimized GAN model is the best option for successive cancellation in the decoder.
ArticleNumber	93
Author	Pushpa, B. Yamini Panda, Sunita
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SubjectTerms	Accuracy Algorithms Artificial Intelligence Binary phase shift keying Codes Coding Coding theory Complex Systems Complexity Decoding Efficiency Energy consumption Engineering Error correction & detection Generative adversarial networks Generative artificial intelligence Literature reviews Machine learning Methods Optimization Original Paper Random noise Wireless communications
Title	Multi Kernel Polar Code using Nut cracker optimization based GAN for Successive Cancellation Decoder to attain low latency and high efficiency
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