论文标题
学习增强的流式代码对于可变大小的消息大致最佳
Learning-Augmented Streaming Codes are Approximately Optimal for Variable-Size Messages
论文作者
论文摘要
尽管要丢失数据包，但实时流沟通仍需要高质量的服务。流码是最适合此设置的一类代码。流式代码的一个关键挑战是它们在“在线”设置中运行，其中要传输的数据量会随着时间而变化，并且未提前知道。缓解可变性的不利影响需要在一个时间插槽中传播到以后的数据包上的数据，而扩散的最佳策略取决于到达模式。因此，仅代数编码技术不足以设计速率最佳代码。我们将代数编码技术与一种学习增强算法相结合，用于扩展，以设计第一个大约速率最佳的流码码，以适用于一系列对实际应用很重要的参数制度。
Real-time streaming communication requires a high quality of service despite contending with packet loss. Streaming codes are a class of codes best suited for this setting. A key challenge for streaming codes is that they operate in an "online" setting in which the amount of data to be transmitted varies over time and is not known in advance. Mitigating the adverse effects of variability requires spreading the data that arrives at a time slot over multiple future packets, and the optimal strategy for spreading depends on the arrival pattern. Algebraic coding techniques alone are therefore insufficient for designing rate-optimal codes. We combine algebraic coding techniques with a learning-augmented algorithm for spreading to design the first approximately rate-optimal streaming codes for a range of parameter regimes that are important for practical applications.
