  Channel Coding has been one of the central disciplines driving the success
stories of current generation LTE systems and beyond. In particular, turbo
codes are mostly used for cellular and other applications where a reliable data
transfer is required for latency-constrained communication in the presence of
data-corrupting noise. However, the decoding algorithm for turbo codes is
computationally intensive and thereby limiting its applicability in hand-held
devices. In this paper, we study the feasibility of using Deep Learning (DL)
architectures based on Recurrent Neural Networks (RNNs) for encoding and
decoding of turbo codes. In this regard, we simulate and use data from various
stages of the transmission chain (turbo encoder output, Additive White Gaussian
Noise (AWGN) channel output, demodulator output) to train our proposed RNN
architecture and compare its performance to the conventional turbo
encoder/decoder algorithms. Simulation results show, that the proposed RNN
model outperforms the decoding performance of a conventional turbo decoder at
low Signal to Noise Ratio (SNR) regions
