Delay Performance of Wireless Communications With Imperfect CSI and Finite-Length Coding

With the rise of critical machine-to-machine applications, next generation wireless communication systems must meet challenging requirements with respect to 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 state. In this paper, we characterize the tradeoff between the training sequence length and data codeword length: shorter channel estimation leaves more time for the 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. In order to optimize the training sequence length and the rate adaptation scheme with respect to the delay performance, we employ queuing analysis on top of accurate models of the physical layer. We obtain an analytically tractable solution to the problem by deriving a closed-form approximation for the decoding error probability due to imperfect channel knowledge and finite-blocklength channel coding. The optimized training sequence length and rate adaptation strategy can reduce the delay violation probability by an order of magnitude, compared with suboptimal strategies that do not consider the delay constraints.