论文标题
用于涡轮代码的模型驱动的DNN解码器：设计，仿真和实验结果
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
论文作者
论文摘要
本文介绍了一种新型的模型驱动的深度学习（DL）架构，称为Turbonet，用于涡轮解码，将DL集成到传统的Max-Log-Maximum A Parsteriori（MAP）算法中。 TurbOnet继承了Max-Log-Map算法和DL工具的优越性，因此具有出色的误差校正能力，并且训练成本低。为了设计涡轮增压器，原始的迭代结构是作为深神经网络（DNN）解码单元展开的，在该单元中，将可训练的权重引入最大log-Map算法并通过监督学习进行了优化。为了有效地训练涡轮增压器，仔细设计了损失功能，以防止棘手的梯度消失问题。为了进一步降低涡轮增压器的计算复杂性和训练成本，我们可以将其修剪成涡轮增压器+。与现有的Black-Box DL方法相比，TurbOnet+在计算复杂性方面具有相当大的优势，并且有利于显着降低解码开销。此外，我们还提出了一种简单的培训策略，以解决过度拟合问题，从而可以对拟议的turbonet+进行有效的培训。仿真结果证明了Turbonet+在误差校正能力，信噪比概括和计算开销方面的优势。此外，在5G快速原型制度系统的帮助下，还建立了实验系统以进行空中（OTA）测试，并证明了Turbonet强大的学习能力和对各种情况的良好鲁棒性。
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.
