A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion

Abstract

A novel algorithm for decoding a general rate K/N convolutional code based on recurrent neural network (RNN) is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER) performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA). The simulation results show that this novel algorithm can achieve the same bit error rate and has a lower decoding complexity. Most importantly this algorithm allows parallel signal processing, which increases the decoding speed and accommodates higher data rate transmission. These characteristics are inherited from a neural network structure of the decoder and the iterative nature of the algorithm, that outperform the conventional VA algorithm.

Analysis of efficiency of the bioinspired method for decoding algebraic convolutional codes

“…The methods for soft decoding of convolutional codes based on the search in the code trellis and the application of neural networks [15][16][17][18][19] can be used only for convolutional codes with a small code constraint length (V ≤ 9) due to the rapid growth of computational complexity. According to the research results, the presented method ensures decoding of the convolutional code with a quite large code constraint length (V ≤ 16).…”

“…In paper [18], the method for decoding the convolutional codes with an arbitrary encoding rate based on recurrent neural networks was proposed and encoding optimization strategies were given. The research conducted in the work showed that this decoding method provides reliability of information transmission at the level of the Viterbi method, but with a lower computational complexity and the possibility of parallel information processing.…”

Performance Analysis of Deep Learning based on Recurrent Neural Networks for Channel Coding

“…Different ideas around the use of ANNs for decoding emerged in the 1990s with works such as [16]- [18] for decoding block and hamming codes. Subsequently, ANNs were used for decoding convolutional codes in [19], [20]. In [21], the author used MLPs to generate Low-Density Parity-Check (LDPC) codes.…”

Efficient Information Set Decoding Based on Genetic Algorithms

“…Furthermore the deployment of Artificial Neural Networks (ANN), to train the system for higher fault tolerance in OFDM is used by Praveenkumar [8]. There are also other works [9][10][11] based on AI trying to solve problems related to coding theory.…”