Spatial techniques in WiFi 802.11ac

1. 1. Overview on basic MIMO techniques
2. Single User Beamforming
3. Multi User MIMO

2. Mohamed Seif

3. STBC
 WiFi 802.11ac uses STBC (Alamouti Scheme).
 STBC is a transmit diversity scheme that comes with a
robust performance achieved with low cost.
Figure: Alamouti’s Scheme for 2*1

4. STBC (cont’d)

5. STBC (cont’d)
 In 802.11ac, four STBC modes were defined (2x1, 4x2,
6x3 or 8x4).
Figure: Alamouti’s Scheme for 4*2.

6. Spatial Multiplexing
Figure: 2*2 MIMO system.

8. Motivation
 Transmit beamforming is used to enhance the reception
of signals.
Figure: Depicts the difference between two
schemes.

9. Motivation (cont’d)
 In broader sense, higher SINR.
 In Transmit beamforming, parallel channel paths are
established such that each symbol is transmitted.
Figure: Conventional MIMO
system equivalent channel.
Figure: TX beamforming
equivalent channel (Ideal).

10. SVD approach
Where X is either U or V

11. SU-Beamforming Pre-coding
Figure: Equivalent channel model after pre-coding.
U

12. SU-Beamforming Pre-coding

13. Receiver design
 The received signal is then
 ZF or MMSE technique is applied to estimate the
symbols.

14. Figure: Tx Beamforming gain over conventional MIMO
in terms of un coded BER.
Results

15. Channel Correlation and Interference
Figure: Channel correlation can degrade the
beamforming gain.

16. Figure: Effect of channel correlation.
Results

17. Channel Feedback
 The Access Point (AP) does not have any Channel State
Information about the channel (CSI).
 Transmit Beamforming requires CSI in order to apply the
weighting matrix V.
 Channel feedback techniques are used.

18. Channel Feedback (cont’d)
 Communication system is a closed loop system.
 TDD is commonly used.
 Compressed feedback technique is used to reduce
overhead.
Figure: Closed loop system.

19. Compressed Channel Feedback

20. Compressed Channel Feedback (cont’d)

21. Compressed Channel Feedback (cont’d)

22. Histogram and Optimization
 Variable feedback rate may be investigated using
source coding.
Ref. W.H. Chin, C. Yuen, Design of differential quantization for low bitrate channel state
information feedback in MIMO-OFDM systems. IEEE VTC 2008 Spring.

23. Results

25. Multi-User MIMO (MU-MIMO)
 Capacity increasing.
 Interference Problem.
Figure: MU-MIMO scheme.

26. MU-MIMO modeling

27. MU-MIMO Pre-coding
 ZF Pre-coding (channel inversion)
 Regularizing the inversion
Figure: Block diagram for MU-MIMO pre-coding process.
Ref. An Introduction to the Multi-User MIMO Downlink, IEEE comsoc.

28. Receiver design

29. Receiver design (cont’d)
 The estimated symbol is then
Where,

30. Figure: Comparison between the two different
pre-coding techniques for MU-MIMO
Performance of the two schemes will converge at higher
SNR.
Results

31. Figure: Effect of feedback delay in MU-MIMO.
Results

32. THANK YOU!