While many communications media, such as wireless and certain classes of wireline channels, typically lead to bursty errors, most decoders are designed assuming memoryless channels. Consequently, communication systems generally rely on interleaving over tens of thousands of bits to match decoder assumptions. Even for short high rate codes, awaiting sufficient data in interleaving and de-interleaving is a significant source of unwanted latency. We construct an extension to the recently proposed Guessing Random Additive Noise Decoding (GRAND) algorithm, which we call GRAND-MO for GRAND Markov Order. By foregoing interleaving and instead making use of the bursty nature of noise, low-latency communication is possible with block error rates outperforming their interleaved counterparts by a substantial margin. We establish that certain well-known binary codes with structured code-word patterns are ill-suited for use in bursty channels, but Random Linear Codes (RLCs) prove robust to correlated noise. We further demonstrate that by operating directly on modulated symbols rather than de-mapped bits, GRAND-MO achieves further performance and complexity gains by exploiting information that is lost in demodulation. As a result, GRAND-MO provides one potential solution for applications that require ultra-reliable low latency communication.

中文翻译：

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保留突发并放弃交织器


虽然许多通信介质（例如无线和某些类别的有线信道）通常会导致突发错误，但大多数解码器都是假设无记忆信道而设计的。因此，通信系统通常依赖于数万比特的交织来匹配解码器假设。即使对于短的高速率代码，在交织和解交织中等待足够的数据也是不必要的延迟的重要来源。我们构建了最近提出的猜测随机加性噪声解码（GRAND）算法的扩展，我们将其称为 GRAND-MO（GRAND Markov Order）。通过放弃交织并利用噪声的突发性质，低延迟通信是可能的，并且块错误率大大优于交织的对应物。我们确定某些具有结构化码字模式的众所周知的二进制码不适合在突发信道中使用，但随机线性码（RLC）证明对相关噪声具有鲁棒性。我们进一步证明，通过直接对调制符号而不是解映射位进行操作，GRAND-MO 通过利用解调中丢失的信息实现了进一步的性能和复杂性增益。因此，GRAND-MO 为需要超可靠低延迟通信的应用提供了一种潜在的解决方案。