  This paper considers a transmitter, which uses random linear coding (RLC) to
encode data packets. The generated coded packets are broadcast to one or more
receivers. A receiver can recover the data packets if it gathers a sufficient
number of coded packets. We assume that the receiver does not abandon its
efforts to recover the data packets if RLC decoding has been unsuccessful;
instead, it employs syndrome decoding in an effort to repair erroneously
received coded packets before it attempts RLC decoding again. A key assumption
of most decoding techniques, including syndrome decoding, is that errors are
independently and identically distributed within the received coded packets.
Motivated by the `guessing random additive noise decoding' (GRAND) framework,
we develop transversal GRAND: an algorithm that exploits statistical dependence
in the occurrence of errors, complements RLC decoding and achieves a gain over
syndrome decoding, in terms of the probability that the receiver will recover
the original data packets.
