Channel Estimation for Mobile OFDM

1. Channel Estimation for Mobile OFDM Zhang Nan (62427P)

2. OUTLINE • Basics of OFDM Channel Estimation • Challenges of Channel Estimation in Mobile OFDM • Channel Estimation Techniques • Performance Evaluation • Conclusion

3. OFDM Overview • Orthogonal Frequency Division Multiplexing • To split a high-rate data stream into a number of lower rate streams that are transmitted simultaneously over a number of subcarrier • In OFDM systems, the spectrum of individual subcarrier is overlapped with minimum frequency spacing, which is carefully designed so that each subcarrier is orthogonal to the other subcarriers. The bandwidth efficiency of OFDM is another advantage. • In the guard time , the OFDM symbol is cyclically extended to avoid intercarrier interference.

4. Advantages of OFDM • Immunity to delay spread • Symbol duration >> channel delay spread • Guard interval • Resistance to frequency selective fading • Each subchannel is almost flat fading • Simple equalization • Each subchannel is almost flat fading, so it only needs a one-tap equalizer to overcome channel effect. • Efficient bandwidth usage • The subchannel is kept orthogonality with overlap.

5. Challenges of OFDM (1/2) • Synchronization • Symbol synchronization • Timing errors • Carrier phase noise • Frequency synchronization • Sampling frequency synchronization • Carrier frequency synchronization

6. Challenges of OFDM (2/2) • High Peak to Average Power Ratio (PAPR) • It increased complexity of the analog-to-digital and digital-to-analog converters. • It reduced efficiency of the RF power amplifier.

7. OFDM System Architecture

8. Three Large Groups of Channel Estimation Techniques(1/3) • Channel estimation allows the receiver to approximate the effect of the channel on the signal. • Pilot Assisted • It is the most straightforward way where symbols or tones known to the receiver, called pilots. • Has a good performance in fast fading environments

9. Three Large Groups of Channel Estimation Techniques(2/3) • Blind (without pilots) • Based on channel statistics employment rather than on that of pilots. • No Training sequences required • Most existing blind channel estimation methods are based on second- or higher order statistics. It features relatively low complexity and a very fast convergence rate. • Hard to implement on real time systems.

10. Three Large Groups of Channel Estimation Techniques(3/3) • Semi-Blind (with initial pilot-based channel estimation and next channel tracking) • Assumes an intermediate position and relies partly on pilots and partly on the use of channel statistics. • A semi-blind competitive neural network based method of time-varying channel estimation is tested in this work.

11. Alogrithm • Consider a multipath radio channel. • Assume the Jakes model on each path. • CNN based channel estimator

12. Competitive Neural Networks • One of the most famous self-organizing in the neural networks • A simple competitive network. • One common learning rule simply adds the difference between the winning neuron and the input sequence to the winning neuron.

13. CNN Based OFDM Channel Estimator (1/2) • The winner neuron is selected according to the Kohonen updated rule • The dynamics of others neurons non-winners are defined as

14. CNN Based OFDM Channel Estimator (2/2) • An estimate of the channel frequency response can be obtained from the weights of the neurons

15. Simulation Result • SNR=0

16. Simulation Result • SNR=5

17. Simulation Result • SNR=10

18. Simulation Result • SNR=15

19. Simulation Result • SNR=20

20. Simulation Result • SNR=25

21. Simulation Result • SNR=30

22. MSE • The MSE measures the average of the square of the error which can be calculated as

23. MSE v.s. SNR