Title page for ETD etd-071399-133447

Type of Document Dissertation
Author Valenti, Matthew C
Author's Email Address valenti@ieee.org
URN etd-071399-133447
Title Iterative Detection and Decoding for Wireless Communications
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Woerner, Brian D. Committee Chair
Gray, Festus Gail Committee Member
Jacobs, Ira Committee Member
Rappaport, Theodore S. Committee Member
Shaw, John Kenneth Committee Member
  • wireless communications
  • channel coding
  • turbo codes
  • multiuser detection
Date of Defense 1999-07-08
Availability unrestricted
Turbo codes are a class of forward error correction (FEC) codes that offer energy efficiencies close to the limits predicted by information theory. The features of turbo codes include parallel code concatenation, recursive convolutional encoding, nonuniform interleaving, and an associated iterative decoding algorithm. Although the iterative decoding algorithm has been primarily used for the decoding of turbo codes, it represents a solution to a more general class of estimation problems that can be described as follows: a data set directly or indirectly drives the state transitions of two or more Markov processes; the output of one or more of the Markov processes is observed through noise; based on the observations, the original data set is estimated.

This dissertation specifically describes the process of encoding and decoding turbo codes. In addition, a more general discussion of iterative decoding is presented. Then, several new applications of iterative decoding are proposed and investigated through computer simulation. The new applications solve two categories of problems: the detection of turbo codes over time-varying channels, and the distributed detection of coded multiple-access signals.

Because turbo codes operate at low signal-to-noise ratios, the process of phase estimation and tracking becomes difficult to perform. Additionally, the turbo decoding algorithm requires precise estimates of the channel gain and noise variance. The first significant contribution of this dissertation is a study of several methods of channel estimation suitable specifically for turbo coded systems.

The second significant contribution of this dissertation is a proposed method for jointly detecting coded multiple-access signals using observations from several locations, such as spatially separated base stations. The proposed system architecture draws from the concepts of macrodiversity combining, multiuser detection, and iterative decoding. Simulation results show that when the system is applied to the time division multiple-access cellular uplink, a significant improvement in system capacity results.

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