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With the rise of critical machine-to-machine applications, next generationwireless communication systems must be designed with strict constraints on thelatency and reliability. A key question in this context relates to channelstate estimation, which allows the transmitter to adapt the code rate to thechannel. In this work, we characterize the trade-off between the estimationsequence length and data codeword length: shorter channel estimation leavesmore time for the actual payload transmission but reduces the estimationaccuracy and causes more decoding errors. Using lower coding rates can mitigatethis effect, but may result in a higher backlog of data at the transmitter. Weanalyze this trade-off using queueing analysis on top of accurate models of thephysical layer, which also account for the finite blocklength of the channelcode. Based on a novel closed-form approximation for the error probabilitygiven the rate, we show that finding the optimal rate adaptation strategybecomes a convex problem. The optimal rate adaptation strategy and the optimaltraining sequence length, which both depend on the latency and reliabilityconstraints of the application, can improve the delay performance by an orderof magnitude, compared to suboptimal strategies that do not consider thoseconstraints.

Delay Performance of Wireless Communications with Imperfect CSI and Finite Length Coding