Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485844
Title: Code-aided iterative techniques in OFDM systems
Author: Zhang, Yu
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2007
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
Inspired by the 'turbo principle', this thesis deals with two iterative technologies in orthogonal frequency division multiplexing (OFDM) systems: iterative interference cancelation in space-frequency block coded OFDM (SFBC-OFDM) and iterative channel estimation/tracking in OFDM Access (OFDMA) with particular application to Worldwide Inter-operability for Microwave Access (WiMAX) systems. The linear matched filter (MF) decoding in SFBC-OFDM is simple yet obtains maximumlikelihood (ML) performance based on the assumption that the channel frequency response remains constant within a block. However, frequency response variations gives rise to inter-channel interference (lCI). In this thesis, a parallel interference cancelation (PIC) approach with soft iterations will be proposed to iteratively eliminate ICI in G4 SFBC-OFDM. Furthermore, the information from outer convolutional decoder is exploited and fed back to aid the inner PIC process to generate more accurate coded bits for the convolutional decoder. Therefore, inner and outer iterations work in a collaborative way to enhance the performance of interference cancelation. Code-aided iterative channel estimation/tracking has the ability of efficiently improving the quality of estimation/tracking without using additional pilots/training symbols. This technique is particularly applied to OFDMA physical layer of WiMAX systems according to the Institute of Electrical and Electronics Engineers (IEEE) 802.16 standard. It will be demonstrated that the performance of the pilot-based channel estimation in uplink (UL) transmission and the channel tracking based on the preamble symbol in downlink (DL) transmission can be improved by iterating between the estimator and the detector the useful information from the outer convolutional codes. The above two issues will be discussed in Chapter 5 and Chapter 6, and before this, Chapter 2 to Chapter 4 will introduce some background techniques that are used throughout the thesis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.485844  DOI: Not available
Share: