论文标题
基于图形搜索的极性代码设计
Graph Search based Polar Code Design
论文作者
论文摘要
众所周知，要实现其全部潜力，极性代码的设计必须根据其预期的解码算法进行量身定制。虽然对于连续的取消（SC）解码，因此可以使用理论上最佳的信息，但只能使用广泛的蒙特卡洛模拟来优化其他解码算法的代码设计（例如信念传播（BP）解码）。我们建议将极地代码的设计过程视为图形搜索问题，从而更加系统地接近它。基于这种形式主义，与最先进的遗传算法（Genalg）和深度学习设计算法相比，设计时间复杂性可以显着降低。此外，可以有效地找到与速率兼容的极性代码的序列。最后，我们分析了所提出的算法的复杂性和构造代码的误差性能。
It is well known that to fulfill their full potential, the design of polar codes must be tailored to their intended decoding algorithm. While for successive cancellation (SC) decoding, information theoretically optimal constructions are available, the code design for other decoding algorithms (such as belief propagation (BP) decoding) can only be optimized using extensive Monte Carlo simulations. We propose to view the design process of polar codes as a graph search problem and thereby approaching it more systematically. Based on this formalism, the design-time complexity can be significantly reduced compared to state-of-the-art Genetic Algorithm (GenAlg) and deep learning-based design algorithms. Moreover, sequences of rate-compatible polar codes can be efficiently found. Finally, we analyze both the complexity of the proposed algorithm and the error-rate performance of the constructed codes.
