1 / 35

Low Rate Feedback MIMO Systems: Code Design

Low Rate Feedback MIMO Systems: Code Design. Hamid Jafarkhani Electrical Engineering & Computer Science Center for Pervasive Communications and Computing University of California, Irvine. Outline. Introduction VQ-Based Beamforming Transmit Beamforming for Time-Selective Fading Channels

stefan
Télécharger la présentation

Low Rate Feedback MIMO Systems: Code Design

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. Low Rate Feedback MIMO Systems: Code Design Hamid Jafarkhani Electrical Engineering & Computer Science Center for Pervasive Communications and Computing University of California, Irvine

  2. Outline • Introduction • VQ-Based Beamforming • Transmit Beamforming for Time-Selective Fading Channels • Transmit Beamforming for MIMO-OFDM Wireless Systems • Noisy Feedback Channels • Conclusions

  3. Receiver Receiver Transmitter Transmitter Open-loop Feedback Close-loop

  4. Different Types of Feedback in a Close-loop System • Perfect feedback • Mean/Covariance feedback • Average SNR information • Quantized phase/magnitude feedback • Quantized direction feedback

  5. Block Diagram of a Transmit Beamforming System Bit stream for Ant-1 Input Bits Encoder Bit Stream for Ant-2 Receiver Transmitter Receiver

  6. Shortcomings of Channel Feedback from Receiver • Channel estimation error at the receiver • Quantization loss • The delay between estimation time and the time that feedback is used • Noise in the feedback channel

  7. Channel Feedback Quality • If the feedback quality drops too low, the beamforming scheme should gradually fall back to the non-beamformed scheme. Perfect Channel Feedback Beamforming No Channel Feedback Space-Time Coding What shall we do in between?

  8. Linear beamforming scheme for STBCs Feedback CSI STBC Encoder (OSTBC/QSTBC) Multiply with Beamforming Matrix P Channel Estimation &Linear Proc. Input Bits Ĉ=PC Decoded Bits

  9. Advantages and Disadvantages • Performance improvement through optimal power loading • Complicated implementation (eigen-analysis) • Beamforming matrix renders high PAPR trellis state machine and beamforming scheme should be jointly defined

  10. A Simplified SOSTTC Beamforming Scheme

  11. CPSTTC System Block Diagram • Based on the channel phase information, the proper inner code is selected • A standard M-TCM structure is used as the outer code

  12. Rate-Limited Feedback:System Block Diagram Feedback Channel Codebook Index Base Band Single Data Stream Multiply with Transmit Weight Select Transmit Weight From Codebook Input Bits Dec Codebook

  13. VQ-Based Beamforming A generalized Lloyd algorithm or a Grassmannian method can be utilized to design the beamformer

  14. Time-Selective Fading • Previous work was based on quasi statici.i.d. block fading • Time-Selective Fading: Jakes’ model • Simplification of Jakes’ model with AR1:

  15. Beamforming Design for Time-Selective Fading Channels • Predictive Vector quantization (PVQ): quantize the residue signal instead of the actual channel direction • Successive Beamforming (SBF): the beamforming codebook is adjusted based on the transmit weight from the previous frames.

  16. PVQ Beamformer • Designing VQ for a Gauss-Markov source • Designing the residue generator and reconstruction units • Designing the optimal predictor To min the given distortion (max SNR)

  17. PVQ Encoder Complex Householder transformation: House(y) is a unitary matrix with the first column being y

  18. PVQ Decoder • Theorem: The linear coefficients that accomplish the highest predictor SNR is a simple delay unit

  19. PVQ Beamformer Properties • Very good performance • Even better performance is possible by using higher order predictors (ARMA) • Codebook is a function of fading speed and the number of antennas • Convergence of the design algorithm is not guaranteed (converges for large N)

  20. SBF System Block Diagram Feedback Channel Delay Codebook Ct Index Base Band Single Data Stream Multiply with Transmit Weight Select Transmit Weight From Codebook Input Bits Dec Codebook Ct Delay

  21. Successive Beamforming (SBF) • Codebook is a function of time: Ct • Beamformer has memory • Synchronized codebook update on both sides without extra feedback information • Flexible implementation: No need to have a different codebook for a different fading speed

  22. SBF Codebook Construction • Proposition: At the t th frame, the SBF codebook is generated as where are constant vectors with unit norm; e1 = [1 0 … 0]T

  23. Numerical Simulations: SNR performance • Nt =4, 2N=16 MISO system • PVQ beamformer provides best performance • SBF algorithm is close to PVQ beamformer. • Both are far better than memoryless Grassmannian beamformer

  24. Numerical Simulation: BER performance • Nt =4, 2N=16. WCDMA system parameters • PVQ beamformer provides best performance. • SBF algorithm is close to PVQ beamformer. • Performance gain is larger at slow fading speed and smaller SNR. • Both are better than memoryless Grassmannian beamformer.

  25. Frequency-Selective Fading

  26. System Model • Channel taps: Exponential power decay • Doppler shift on L channel taps: AR1 fading model

  27. Existing OFDM Beamformers • Independent beamforming on each subcarrier using memoryless Grassmannian codebook. Huge feedback bits + Bad performance • Spherical linear interpolator OFDM beamformer Less feedback bits + Worse performance

  28. Our Approach • Exploit the time domain and frequency domain correlations. • Transmit beamforming based on successive beamforming (SBF).

  29. Time Domain Round Robin SBF

  30. Frequency Domain Round Robin SBF

  31. Frequency Domain Cluster SBF

  32. Numerical Results (IEEE 802.11a) • TDRSBF and FDRSBF outperform full feedback Grassmannian beamformer. (40 bits > 192 bits, only 1dB away from perfect CSI). • FDRSBF algorithm is affected by channel delay spread. Whereas TDRSBF is insensitive to channel delay spread. Hiperlan2 Indoor fading model C (Trms = 150ns, v=3m/s)

  33. Numerical Results (Ergodic Capacity) • TDRSBF and FDRSBF outperform full feedback Grassmannian beamformer (FFGBF). (40 bits > 192 bits, 0.5dB from perfect CSI). Hiperlan2 Indoor fading model A (Trms = 150ns, v=3m/s)

  34. Results • We have developed beamforming algorithms to exploit the mutual correlation in the fading channel. The proposed algorithms have very limited feedback requirements. • The TDRSBF algorithm is sensitive to mobile Doppler shift. It performs well at slow fading scenarios. • The FDRSBF algorithm is more severely affected by channel delay spread rather than mobile Doppler shift. It performs better than the TDRSBF algorithm at fast fading or small delay spread environments.

  35. Conclusions • A VQ-based beamformer framework is general enough to deal with different scenarios • We have designed VQ beamformers for • Time-selective channels • Frequency-selective channels • Noisy feedback channels • Can be combined with space-time coding

More Related