Analysis of Selective-Repeat ARQ via Matrix Signal-Flow Graphs

In this paper, we present a new method to analyze the throughput and delay of the selective-repeat (SR) automatic repeat-request (ARQ) protocol. Previous work on SR ARQ has concentrated on reliable feedback or two-state Markovian feedback errors. We solve a wider class of problems by characterizing both the forward and reverse channels by general hidden Markov models (HMMs). The moment-generating function (MGF) technique is used to find throughput and delay. To calculate the MGF, we construct matrix signal-flow graphs for the hidden Markov process. This procedure can be useful for a variety of other HMM problems, and is of interest by itself. Practical issues such as erasure errors and timeouts are included in our analyses, which are verified by extensive simulations