1 / 9

Semi-Blind Equalization for OFDM using Space-Time Block Coding and Channel Shortening

Semi-Blind Equalization for OFDM using Space-Time Block Coding and Channel Shortening. Alvin Leung Yang You EE381K-12 March 04, 2008. General Background/Motivation. Multiple transmit/receive antennas for communication systems Higher capacity than single transmit/receive systems

denise
Télécharger la présentation

Semi-Blind Equalization for OFDM using Space-Time Block Coding and Channel Shortening

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Semi-Blind Equalization for OFDM using Space-Time Block Coding and Channel Shortening Alvin Leung Yang You EE381K-12 March 04, 2008

  2. General Background/Motivation Multiple transmit/receive antennas for communication systems Higher capacity than single transmit/receive systems Leveraging multiple antennas Space-time block coding Frequency selective channels Multicarrier modulation Divide channel into many flat fading bins Channel estimation

  3. System Model 2 TX Antennas x 1 RX Antenna (MISO) R = (h0 * s0) + (h1 * s1) + n Multipath channel model with additive Gaussian noise

  4. Orthogonal Frequency Division Multiplexing channel carrier magnitude subchannel frequency http://www.ece.utexas.edu/~rheath/research

  5. Training vs Blind vs Semi-BlindChannel Estimation Must estimate the channel to recover the signal Training-based Use pilot symbols/tones Consume bandwidth, capacity loss Blind Use inherent structure of signal Constrained to special cases, less accurate Semi-blind Use minimal amount of known symbols w/ inherent structure of signal

  6. Low Complexity MIMO Blind, Adaptive Channel Shortening[R. K. Martin et al. 2005] OFDM requires cyclic prefix (CP) be longer than channel Can shorten the channel to ensure this condition Blind technique induces channel shortening by restoring the redundancy of CP Low complexity; shorten multiple channels simultaneously Unclear how to leverage multiple transmit antennas or estimate channel copy copy s y m b o l ( i+1) s y m b o li CP CP CP: Cyclic Prefix v samples N samples http://www.ece.utexas.edu/~rheath/research

  7. Alamouti Coding[S. Alamouti 1998] • Rate-1 code using simple coding matrix at transmitter and combiner at receiver • Can be generalized to 2 transmitters and M receivers • Adds structure and redundancy to received signal for channel estimation and robustness

  8. Semi-Blind Channel Estimation[S. Zhou et al. 2002] Scalar ambiguity exists if channel estimated blindly from Alamouti structure Only two known symbols necessary to resolve true channel Less wasted symbols than training based

  9. Proposed Project We leverage Martin’s blind channel shortening algorithm to reduce the channel length, allowing a shorter cyclic prefix Channel is estimated with semi-blind technique based on Alamouti with two known symbols Channel estimate used for frequency domain equalization, recover transmitted signal

More Related