论文标题
通信系统端到端学习的渠道模型：调查
Channel model for end-to-end learning of communications systems: A survey
论文作者
论文摘要
基于多个独立处理块链的传统沟通模型对效率有限，并引入人造障碍。因此，每个单独优化的块不能保证系统的端到端性能。最近，已经提出了通过机器学习（ML）端到端的通信系统学习，以在所有组件上共同优化系统指标。这些方法显示了性能的改进，但有一个限制，即需要一个可区分的通道模型。在这项研究中，我们总结了减轻此问题的现有方法。我们认为，这项研究将更好地了解该主题，并洞悉该领域的未来研究。
The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end performance of the system. Recently, end-to-end learning of communications systems through machine learning (ML) have been proposed to optimize the system metrics jointly over all components. These methods show performance improvements but has a limitation that it requires a differentiable channel model. In this study, we have summarized the existing approaches that alleviates this problem. We believe that this study will provide better understanding of the topic and an insight into future research in this field.
