1. Advisor : Tzi-Dar Chiueh Student : Jui-Ping Lien
Progress Report of OFDM-based Agile Baseband Transceiver for Spectrum Pooling Wireless Systems
Advisor : Tzi-Dar Chiueh
Student : Jui-Ping Lien
Date : Nov 29th , 2004

2. Outline Overview of Spectrum Pooling System Transmit Power Spectrum
Feature
Specification
Transmit Power Spectrum
Two approaches of reducing signal sidelobe
Simulation Result
SISO perfect channel estimation in AWGN
Conclusion
Future Work
Reference

3. Overview of Spectrum Pooling System

4. Flexible Spectrum
Coexist with LU in the same frequency band without interfering LU.
Use idle bands which LU does not use.
Band Allocation: totally 15 combinations LU RU LU LU LU RU RU LU
sub-band No. #1 #2 #3 #4
LU/RU: licensed/rental user

5. Adaptive Transmission
Pros
Enhance the capacity
Cons
Increase the complexity
Assume transmitter knows CSI (channel state information)
Adaptive transmission using water-filling techniques [3]
Power allocation
Bit loading (adaptive modulation)
SBLA ( Simple Blockwise Loading Algorithm ) [1]

6. MIMO Scheme 2x2 MIMO: enhance higher data rate VBLAST
SVD-based (Singular Value Decomposition)
Pros: support adaptive modulation
Cons: Assume transmitter knows CSI
Channel matrix
Transmit signal
Received signal
[2] [3]

7. System specifications
FFT size
256
Bandwidth
100MHz (use 80MHz actually)
Sampling rate
80MHz (4 times faster than a)
Sub-carrier spacing
312.5KHz (=80 MHz/256)
FFT period
3.2us (=1/312.5 KHz)
Guard interval duration
0.8us
OFDM symbol duration
4.0us (= us)
Sub-carrier in use
variable
Modulation type
BPSK, QPSK, 16QAM, 64QAM
Operating frequency
Upper UNII bands

8. Transmit Power Spectrum Analysis

9. Transmitter Block Diagram
CSI

10. Transmit Spectrum Mask
802.11a spec has transmit spectrum mask
Two approaches to reduce sidelobes of transmitted waveform
Time domain windowing
Frequency domain filtering
RU #1234
RU #13

11. Time domain windowing Raised cosine windowing
Larger roll-off factor improves the spectrum, at the cost of decreased delay spread tolerance.

12. Frequency domain filtering (1/2)
Filter Requirement
Interpolator filer
Band-limited filter: reduce out-of-band spectrum
Reconfigurable filter
Complex filter: for asymmetric spectrum case

13. Frequency domain filtering (2/2)
Item
Elliptic Filter (IIR)
Filter Coefficient
Case 1
N=6
Wp=19/80
Ws = 23/80
Rp=0.1 dB
Rs=40 dB
RU#23 2
RU#1, RU#2, RU#12 3
RU#3, RU#4, RU#34 4
N=7
Wp=29/80
Ws = 33/80
Rp=0.1dB
RU#13, RU#123 5
complex conjugate
of item4
RU#24. RU#234 6
Wp=39/80
Ws = 43/80
Rp=0.1, Rs=40
RU#14, RU#124, RU#134, RU#1234

14. Simulation Transmit Spectrum -- 1 sub-band (1/3)
β=0, 1/160, 1/40
β=0, 1/160, 1/40

15. Simulation Transmit Spectrum -- 2 sub-bands (2/3)

16. Simulation Transmit Spectrum -- 3 or 4 sub-bands (3/3)

17. Simulation of Perfect Channel Estimation in AWGN

18. Receiver Architecture
Inner receiver

19. SISO Perfect Channel Estimation in AWGN Channel
Simulation Environment
More available sub-band
→ higher data rate
→ higher same SNR per OFDM symbol
RU#1
RU#1234

20. Simulation Result (1/2) 16 QAM of 15 cases
4 modulation types of RU#1234

21. Simulation Result (2/2)

22. Conclusion Features of SP system has been introduced.
Since transmit power spectrum should achieve the mask requirement, we use both techniques
time domain raised cosine windowing (overlap 1 pt )
frequency domain filtering
SISO perfect channel estimation in AWGN has been simulated.

23. Future Work Extend SISO to MIMO scheme
Build up timing recovery block of receiver
Support adaptive modulation
System modification according to final specification

24. Reference [1] Rainer Gr¨unheid, Edgar Bolinth, and Hermann Rohling,
“A Blockwise Loading Algorithm for the Adaptive Modulation Technique in OFDM Systems,” in IEEE VTC-2001, Atlantic City, USA, October 2001.
[2]
[3] Juha Heikala, John Terry, “OFDM Wireless LANs: A Theoretical and Practical Guide” 2002