Sparse Recurrent Neural Network Architecture for Turbo Decoding in NextGen Communication Systems

In the rapidly advancing domain of 5G communication systems, channel decoding, particularly turbo decoding, has emerged as a significantly complex challenge. Turbo decoding is an essential element within communication frameworks, necessitating both efficiency and rapid processing to cater to the demanding data rates and stringent low latency requirements of 5G networks. This paper focuses on the unique contributions of employing a Sparse Recurrent Neural Network (SRNN) architecture, leveraging sparsity to reduce computational load while maintaining high performance significantly. Unlike existing approaches, our method introduces a novel piece-wise linear approximation of the activation function, enhancing efficiency and scalability for NextGen communication systems. Our approach leverages the principles of sparsity and employs a piece-wise linear approximation of the activation function to markedly reduce the computational load of the turbo-decoding process.Comprehensive evaluations demonstrate that our RNN architecture outperforms existing deep learning models in the context of turbo decoding and with a significantly lower computational footprint. This research contributes to the field by providing a scalable, efficient, and less computationally intensive turbo-decoding method, particularly suited for the next-generation cloud systems underlying 5G and beyond communication technologies.