On the Performance of Short Block Codes over Finite-State Channels in the Rare-Transition Regime

As the mobile application landscape expands, wireless networks are tasked with supporting different connection profiles, including real-time traffic and delay-sensitive communications. Among many ensuing engineering challenges is the need to better understand the fundamental limits of forward error correction in non-asymptotic regimes. This article characterizes the performance of random block codes over finite-state channels and evaluates their queueing performance under maximum-likelihood decoding. In particular, classical results from information theory are revisited in the context of channels with rare transitions, and bounds on the probabilities of decoding failure are derived for random codes. This creates an analysis framework where channel dependencies within and across codewords are preserved. Such results are subsequently integrated into a queueing problem formulation. For instance, it is shown that, for random coding on the Gilbert-Elliott channel, the performance analysis based on upper bounds on error probability provides very good estimates of system performance and optimum code parameters. Overall, this study offers new insights about the impact of channel correlation on the performance of delay-aware, point-to-point communication links. It also provides novel guidelines on how to select code rates and block lengths for real-time traffic over wireless communication infrastructures

Performance Analysis of Block Codes over Finite-state Channels in Delay-sensitive Communications

As the mobile application landscape expands, wireless networks are tasked with supporting different connection profiles, including real-time traffic and delay-sensitive communications. Among many ensuing engineering challenges is the need to better understand the fundamental limits of forward error correction in non-asymptotic regimes. This dissertation seeks to characterize the performance of block codes over finite-state channels with memory and also evaluate their queueing performance under different encoding/decoding schemes. In particular, a fading formulation is considered where a discrete channel with correlation over time introduces errors. For carefully selected channel models and arrival processes, a tractable Markov structure composed of queue length and channel state is identified. This facilitates the analysis of the stationary behavior of the system, leading to evaluation criteria such as bounds on the probability of the queue exceeding a threshold. Specifically, this dissertation focuses on system models with scalable arrival profiles based on Poisson processes, and finite-state memory channels. These assumptions permit the rigorous comparison of system performance for codes with arbitrary block lengths and code rates. Based on this characterization, it is possible to optimize code parameters for delay-sensitive applications over various channels. Random codes and BCH codes are then employed as means to study the relationship between code-rate selection and the queueing performance of point-to-point data links. The introduced methodology offers a new perspective on the joint queueing-coding analysis for finite-state channels, and is supported by numerical simulations. Furthermore, classical results from information theory are revisited in the context of channels with rare transitions, and bounds on the probabilities of decoding failure are derived for random codes. An analysis framework is presented where channel dependencies within and across code words are preserved. The results are subsequently integrated into a queueing formulation. It is shown that for current formulation, the performance analysis based on upper bounds provides a good estimate of both the system performance and the optimum code parameters. Overall, this study offers new insights about the impact of channel correlation on the performance of delay-aware communications and provides novel guidelines to select optimum code rates and block lengths