  To facilitate applications in IoT, 5G, and beyond, there is an engineering
need to enable high-rate, low-latency communications. Errors in physical
channels typically arrive in clumps, but most decoders are designed assuming
that channels are memoryless. As a result, communication networks rely on
interleaving over tens of thousands of bits so that channel conditions match
decoder assumptions. Even for short high rate codes, awaiting sufficient data
to interleave at the sender and de-interleave at the receiver is a significant
source of unwanted latency. Using existing decoders with non-interleaved
channels causes a degradation in block error rate performance owing to mismatch
between the decoder's channel model and true channel behaviour.
  Through further development of the recently proposed Guessing Random Additive
Noise Decoding (GRAND) algorithm, which we call GRAND-MO for GRAND Markov
Order, here we establish that by abandoning interleaving and embracing bursty
noise, low-latency, short-code, high-rate communication is possible with block
error rates that outperform their interleaved counterparts by a substantial
margin. Moreover, while most decoders are twinned to a specific code-book
structure, GRAND-MO can decode any code. Using this property, we establish that
certain well-known structured codes are ill-suited for use in bursty channels,
but Random Linear Codes (RLCs) are robust to correlated noise. This work
suggests that the use of RLCs with GRAND-MO is a good candidate for
applications requiring high throughput with low latency.
