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 …

Present-day communication systems routinely use codes that approach the channel
capacity when coupled with a computationally efficient decoder. However, the decoder is …

This paper presents TurboNet, a novel model-driven deep learning (DL) architecture for
turbo decoding that combines DL with the traditional max-log-maximum a posteriori (MAP) …

With the demand of high data rate and low latency in fifth generation (5G), deep neural
network decoder (NND) has become a promising candidate due to its capability of one-shot …

Turbo codes were the first error-correcting codes that demonstrated reliable communications
near the channel capacity with practically feasible hardware. Due to their excellent error …

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet,
for turbo decoding that integrates DL into the traditional max-log-maximum a posteriori …

Upcoming modern communications are based on 5G specifications and aim at providing
solutions for novel vertical industries. One of the major changes of the physical layer is the …

Recently deep neural networks have been successfully applied in channel coding to
improve the decoding performance. However, the state-of-the-art neural channel decoders …

This paper enhances the performance and training of the Turbo Autoencoder (TurboAE), an
end-to-end jointly trained neural channel encoder and decoder. A novel interleaver for …

Standard decoding approaches rely on model-based channel estimation methods to
compensate for varying channel effects, which degrade in performance whenever there is a …