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

This work investigates the integration of semi-blind channel estimation and shortening methods within multi-antenna orthogonal frequency division multiplexing (OFDM) systems, enhancing their performance over realistic channel models. By employing Alamouti space-time coding and innovative linear precoding strategies, the proposed system maintains capacity and reliability in highly variable environments. Results showcase the efficiency of the semi-blind approach in reducing bit error rates while effectively managing frequency-selective channels. The analysis incentivizes future exploration of training-based methodologies for improved channel estimation.

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

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

  2. General Background/Motivation Multiple antenna communication systems Higher capacity Leveraging multiple antennas Spatial Diversity Space-Time Coding Frequency selective channels Modeled as FIR filter Orthogonal Frequency Division Multiplexing (OFDM)

  3. Channel Shortening Needed in order to preserve cyclic convolution in OFDM Designed as FIR filter – compresses channel energy Alamouti Coding • Orthogonalizes transmitted symbol

  4. Objective Evaluate a combination of blind channel shortening and semi-blind channel estimation in a multi-antenna ST-OFDM system over a realistic channel model (3GPP TR 25.996 spatial channel model).

  5. System Model – TX Θ1 and Θ2 - Linear precoders [JxK] M(.) - Alamouti space-time encoding OFDM IFFT +CP IFFT +CP

  6. System Model – Channel and RX w Channel Shortener -CP FFT D1 and D2 - effective frequency domain channels w(n) is AWGN M(.) removes space-time coding Γ equalizes z(n) to obtain symbol estimates

  7. Channel Shortening for Equal Channels

  8. Constellation Comparison – Two Channels Bit Error Rate = 5.3191e-005 Bit Error Rate = 9.5745e-005 Bit Error Rate = 0.0089 Bit Error Rate = 4.5745e-004

  9. Channel Estimate Comparison

  10. Conclusions Computation of SVD in estimation very complex – O(N3) Iterative channel estimation assumes very slowly changing channel Blind channel shortener based on ergodic statistics – requires large sample size We are exploring training based approaches

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