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

This work analyzes the throughput of Go-Back-N (GBN) in block fading, a model frequently used for slow fading wireless channels. We devise hidden Markov models and block transition probabilities for the block fading channel, allowing us to calculate the throughput of GBN with reliable feedback, as well as unreliable feedback. The advantages of this approach include generality: it applies to two-state as well as multi-state models for forward and reverse channels. Also, the results for both reliable and unreliable feedback are expressed in terms of probability matrices, which can be used as convenient building blocks in the analysis and simulation of larger systems. Simulations verify our analysis.