Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results

TL;DR

This paper introduces TurboNet, a model-driven deep learning decoder for turbo codes that combines traditional algorithms with neural networks, achieving improved error correction, reduced complexity, and robustness demonstrated through simulations and over-the-air tests.

Contribution

The paper proposes TurboNet, a novel deep learning-based turbo decoder that integrates max-log-MAP with trainable weights, along with TurboNet+ for complexity reduction and an effective training strategy.

Findings

TurboNet outperforms traditional decoders in error correction.

TurboNet+ reduces computational complexity significantly.

Experimental results confirm TurboNet's robustness and learning ability.

Abstract

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 (MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+'s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet's strong learning ability and great robustness to various scenarios.