Treffer: A Method for Estimating the Scaling Factor of Systematic Turbo Polar Codes Using a Feed Forward Neural Network.

Polar codes can reach the Shannon limit for an unlimited code length over a discrete memoryless channel. As an incomplete polarization process for short lengths, polar coding (PC) performs worse. Configuring the PC in a parallel or serial turbo arrangement is one way to solve this issue. The problem with turbo code is that it overestimates the information sequences sent between the parallel decoders. Consequently, a scaling factor (SF) is proposed to reduce this exaggeration, and the multiplication by SF calculated from the statistical methods increases complexity and processing time. This paper suggests a feedforward neural network (NN) with a single neuron and one hidden layer to scale the overestimating values of extrinsic information instead of multiplication by scaling factor to reduce the latency and improve the performance quality for short-length codes. Stopping criteria such as signed difference ratio (SDR) and sign change ratio (SCR) algorithms are used to avoid needless decoding iterations. In comparison to the original systematic turbo polar code (STPC), the proposed NN scaling method exhibits an enhancement of approximately 0.3 dB at BER=10^-5 over AWGN noise channel. Furthermore, using stopping criteria with the proposed scheme may lower the average number of iterations (ANI) by a factor of 0.3 compared to the previous works based on the correlation coefficient approach. Furthermore, the initiation interval is reduced to one cycle using different optimization techniques like pipelining and array-partitioning. [ABSTRACT FROM AUTHOR]

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