  With the rise of critical machine-to-machine applications, next generation
wireless communication systems must be designed with strict constraints on the
latency and reliability. A key question in this context relates to channel
state estimation, which allows the transmitter to adapt the code rate to the
channel. In this work, we characterize the trade-off between the estimation
sequence length and data codeword length: shorter channel estimation leaves
more time for the actual payload transmission but reduces the estimation
accuracy and causes more decoding errors. Using lower coding rates can mitigate
this effect, but may result in a higher backlog of data at the transmitter. We
analyze this trade-off using queueing analysis on top of accurate models of the
physical layer, which also account for the finite blocklength of the channel
code. Based on a novel closed-form approximation for the error probability
given the rate, we show that finding the optimal rate adaptation strategy
becomes a convex problem. The optimal rate adaptation strategy and the optimal
training sequence length, which both depend on the latency and reliability
constraints of the application, can improve the delay performance by an order
of magnitude, compared to suboptimal strategies that do not consider those
constraints.
