1. CHANNEL ESTIMATION FOR MIMO- OFDM COMMUNICATION SYSTEM PRESENTER: OYERINDE, OLUTAYO OYEYEMI SUPERVISOR: PROFESSOR S. H. MNENEY AFFILIATION:SCHOOL OF ELECTRICAL, ELECTRONIC AND COMPUTER ENGINEERING, UKZN SOUTH AFRICA

2. Slide 2 © CSIR 2006 www.csir.co.za OUTLINE INTRODUCTION MIMO SYSTEM OFDM SYSTEM MIMO-OFDM SYSTEM NEED FOR CHANNEL ESTIMATION FOR MIMO-OFDM SYSTEM PROPOSED ITERATIVE CHANNEL ESTIMATION SCHEME FOR MIMO-OFDM VSSNLMS ALGORITHM PERFORMANCE OF VSS-NLMS ALGORITHM CONCLUSION

3. Slide 3 © CSIR 2006 www.csir.co.za INTRODUCTION MIMO SYSTEM WHAT IS MIMO SYSTEM? WHY MIMO IN COMMUNICATION SYSTEM? CAPACITY OF MIMO SYSTEM

4. Slide 4 © CSIR 2006 www.csir.co.za INTRODUCTION OFDM SYSTEM ORIGIN OF OFDM BASIC IDEA OF OFDM SYSTEM : Fig.1: Comparison between (a) Conventional FDM and (b) OFDM PRESENT STATUS:

5. Slide 5 © CSIR 2006 www.csir.co.za INTRODUCTION ADVANTAGES OF OFDM SYSTEM The following are the advantages of OFDM systems: High spectral efficiency Simple implementation using FFT (fast Fourier transform) Low receiver complexity, robust ability for high-data-rate transmission over multipath fading channel High flexibility in terms of link adaptation Low complexity multiple access schemes such as orthogonal frequency division multiple access.

6. Slide 6 © CSIR 2006 www.csir.co.za INTRODUCTION BASEBAND OFDM TRANSCEIVER A typical baseband OFDM transceiver is shown below Fig. 2: Typical baseband OFDM transceiver

7. Slide 7 © CSIR 2006 www.csir.co.za MIMO-OFDM SYSTEM ORIGIN OF MIMO-OFDM A broadband MIMO-OFDM system model is shown below Fig. 3: A broadband MIMO-OFDM System The data stream b[n, k] is first encoded by a space-time or space-frequency encoder. Then, the coded data is divided into M T substreams with each substream forming an OFDM block transmitted through one transmit antenna.

8. Slide 8 © CSIR 2006 www.csir.co.za THE NEED FOR CHANNEL ESTIMATION FOR MIMO-OFDM SYSTEM MIMO-OFDM RECIEVER: At the receiver, the received signals at multiple receive antennas are decoded using channel state information obtained through a training-based symbol (resulting in coherent detection of the transmitted symbol). Therefore, channel state information in terms of channel impulse response (CIR) or channel frequency response (CFR) is critical to achieve the advantages (diversity gains and the expected increase in data rate) of a MIMO-OFDM system.

9. Slide 9 © CSIR 2006 www.csir.co.za PROPOSED ITERATIVE CHANNEL ESTIMATION SCHEME FOR MIMO-OFDM Fig. 4: Proposed Iterative channel estimation scheme

10. Slide 10 © CSIR 2006 www.csir.co.za PROPOSED ITERATIVE CHANNEL ESTIMATION SCHEME FOR MIMO-OFDM Temporal Channel Transfer Function Estimator Based on VSSNLMS algorithm Channel Impulse Response Estimator Based on Fast Data Projection Method for Stable Subspace Tracking algorithm by (X. G. Doukopoulos and G. V. Moustakides 2005) Channel Impulse Response Predictor Based on VSSNLMS algorithm

11. Slide 11 © CSIR 2006 www.csir.co.za VSSNLMS ALGORITHM VARIABLE STEP SIZE NLMS: The VSSNLMS algorithm is given by(1) and the step-size expression by (3) and (4): In (3),  is a small positive constant that controls the adaptive behavior of the step-size sequence.,where

12. Slide 12 © CSIR 2006 www.csir.co.za PERFORMANCE OF VSS-NLMS ALGORITHM The VSS-NLMS algorithm has been used to simulate channel estimation for single input single output system shown below Fig. 5: The system model

13. Slide 13 © CSIR 2006 www.csir.co.za SIMULATION RESULTS RESULT IN FIG. 6: This is based on ensemble error variance for the channel estimation RESULT IN FIG. 7: This is obtained based on the following performance index - normalized means square error, given as:

14. Slide 14 © CSIR 2006 www.csir.co.za FIG 6: Simulated ensemble error variance for the channel estimation algorithms, at each symbol interval n in a frame of training and data symbols. Simulation setup: SNR = 10 dB;  =0.02,  L=sigma, fd /fs, and =lamda as shown above the plots. Fig. 6

15. Slide 15 © CSIR 2006 www.csir.co.za FIG. 7: Normalized MSE for the channel estimation algorithms, at each SNR in a frame of training and data symbols. Simulation setup:  =0.02,  L=sigma, fd /fs, and = lamda as shown above the plots Fig 7

16. Slide 16 © CSIR 2006 www.csir.co.za CONCLUSION It is obvious from the above results that VSSNLMS which is less complex than RLS and exhibited close convergence rate to RLS for the channel estimation simulated for single input single output (SISO) communication system will be a good candidate for CTF estimator and CIR predictor for the proposed iterative channel estimator for MIMO-OFDM communication systems.

17. Slide 17 © CSIR 2006 www.csir.co.za THANK YOU