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OFDM interference mitigation algorithms with application to DVB-H

Posted on:2009-07-02Degree:Ph.DType:Dissertation
University:The University of Texas at DallasCandidate:Lu, SiliFull Text:PDF
GTID:1448390002992559Subject:Engineering
Abstract/Summary:
Orthogonal frequency-division multiplexing (OFDM) has gained an increased popularity over the last few years and has been adopted as the modulation scheme of choice for beyond 3G mobile communication systems. This is mainly due to its high robustness to time dispersion effects and its low implementation complexity. DVB-H is the latest development within the set of Digital Video Broadcasting (DVB) standards that use OFDM as a modulation scheme. DVB-H is required to operate under high mobility conditions resulting in significant Inter-Carrier Interference (ICI). Under these conditions, the conventional one-tap per subcarrier channel estimation and equalization scheme is suboptimal since it does not account for ICI effects due to channel time-variation within an OFDM symbol.;To mitigate ICI, we propose in this dissertation a simplified hybrid frequency/time-domain channel estimation algorithm. In addition, we design a reduced-complexity Q-tap frequency-domain equalizer (FEQ) and enhance its performance by integrating it with joint channel and data estimation and an ICI-mitigating pilot/data placement scheme. We also investigate differential OFDM detection as a low-complexity alternative for coherent detection and test its performance in the presence of Doppler for DVB-H system.;Furthermore, we extend our approach to multiple-input multiple-out (MIMO) OFDM systems. We design a novel embedded space-frequency block code (SFBC)-OFDM scheme that performs well under high Doppler. We also propose a novel standard-conformable Cyclic Delay Diversity (CDD) scheme and its associated channel estimation scheme for comb-type OFDM transmissions, which achieves the Cramer-Rao lower bound on channel estimation mean square error.
Keywords/Search Tags:OFDM, Channel estimation, DVB-H, Scheme
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