Mitigating the Effects of Intersymbol Interference: Algorithms and Analysis

In addition to asymptotic analysis of receivers for ISI channels, we also propose a joint maximum likelihood detection and decoding scheme for use when the channel is unknown to the receiver. Rather than employing training data to generate an estimate of the channel, the proposed receiver views the channel taps as stochastic quantities drawn from a known prior distribution and uses Bayesian techniques to compute estimates of the transmitted symbols. To implement the proposed receiver, we employ a stacklike algorithm, which estimates the transmitted bits by navigating the tree generated by the combined code and channel. We describe the derivation of the Bayesian metric and explore the performance loss incurred as a result of the lack of channel knowledge. In addition, we empirically characterize the robustness of the Bayesian detector to variations in the parameters of the prior distribution.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Nelson, Jill Karen

Contributors dc:contributor

• Andrew Singer

• Engineering, Electronics and Electrical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3199099

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80935