Chapter 3 Proposed Algorithm
3.2 Timing Synchronization
3.2.2 Fine Timing Synchronization
In real systems, channel effect is much complicated. The correlation between
,
P and phase error might not as smooth as the model, neither does the correlation i L
between P and i L, P rot . To make a better phase error estimation, we need to do _ i L, fine timing synchronization to confirm:
As mention before, we have Rotated ini_ D with phase error φini,Rotated rot with phase error _ D φrot, andRotated_ fin with phase error D φfin.
Rotated is calculated to get D Ephase by equation(3.26). Ephase finds the better phase which suppose to be Rotated_ fin by three rotated value. If it is not, means D we need to modify the estimated phase error φfin.
Start Figure 3-14: Modification of phase error φfin
Chapter 4 Simulation
E use simulation to evaluate the receiver’s performance both in the AWGN channel and in a Rayleigh fading channel with additional channel effect. In the AWGN channel, as equation(3.1) mention before,
i j( ) ij
R =S k +N ; On the other hand, in the Rayleigh fading channel with additional channel effect, we can express the received signal sample as:
(
( ) ( , ) *)
i(2 ft ) sin (( n) ) ( ) ( )i j ij ij ij
k T n
R S k H k e c N k I k
k
π θ
τ Δ + − Δ
= ⊗ ∗ + + (4.1)
Where the H k is the multipath with time-variant model, the exponential term is ij( )
CFO effect, the sinc part is SCO effect, N k is AWGN, and ( )ij( ) I k is impulsive ij interference.
4.1 Simulation Platform
MATLAB is chosen as simulation language, due to its ability to mathematics, such as matrix operation, numerous math functions, and easily drawing figures. A MIMO-OFDM system based on IEEE 802.11n Wireless LANs, TGn Sync Proposal Technical Specification [7], is used as the reference simulation platform. The major parameters are shown in Table 4-1.
W
Table 4-1 Simulation parameters
PSDU Length 1024 Bytes Carrier Frequency 2.4 GHz
Bandwidth 20 MHz
IFFT / FFT Period 3.2 sμ
4.2 Simulation Result
4.2.1 Packet Synchronization Simulation Condition
In this section, the simulation environment is as follow:
Table 4-2 Packet synchronization simulation condition Condition Value
T-variant Jakes model
Velocity 60 km/hr
Interference Cooker ignition Burst Duration
( pulses per burst) 20 Pulse Spacing 1.5 0.5 s± μ
SIR -10 dB
31
4.2.1.1 Packet Detection
Figure 4-1 present the symbol location when packet detected under different SNR condition. The symbol location indicates the start of packet. A negative symbol location means packet is not present; there is only so-called leading noise.
When SNR<0, the packet detection shows false alarm, including detected at wrong time or detected when there is no packet, and the range of detection is too wide to be reliable. When SNR>0, the range of detection become narrow down to be acceptable one and the packet detection become more reliable.
-5 0 5 10 15 20 25 30
Figure 4-1: Packet detection distribution versus different SNR
48 49 50 51 52 53 54 55 56 57
Figure 4-2: The probability of packet detection under SNR 0
4.2.1.2 Symbol Boundary Detection
Figure 4-3 present the sample location when packet detected under different SNR condition. The packet and the symbol boundary are detected simultaneously.
Symbol location shows the packet start timing and similarly sample location help us to locate the symbol boundary. A multiphase generator is used to generate 22 different phases between one clock cycle, in other word, it means sample location is symbol location to multiply 22.
When SNR<5, because of the connection between packet detection and symbol boundary detection, symbol boundary detection suffer serious index errors due to packet detected errors, including packet loss (SNR<0) and packet detection false alarm (SNR<5) and weak signal power (as shown in Figure 4-4).
When SNR>5, the decrease of the packet detected errors result in the symbol boundary detection errors reduce apparently. We can also know from Figure 4-3 and Figure 4-4, the range of symbol boundary detection become narrow down to be acceptable one when SNR>5.
Figure 4-3: Symbol boundary detection distribution versus different SNR -5 0 5 10 15 20 25 30 500400
700600 900800
10000 20 40 60 80 100
SNR (dB) Sample Location
Probability (%)
33
Figure 4-4: Symbol index error analysis
4.2.1.3 Packet Synchronization
Figure 4-5 present the system performance of packet synchronization with two different channel types. One is AWGN channel; it means the channel suffer from only AWGN. The other is complex channel; the condition of complex channel is described in section 4.2.1.
The legend “Packet sync. on” means the performance of the proposed packet synchronization algorithm. Relatively, “Packet sync. off” means the performance of the ideal packet synchronization, including ideal packet detection and ideal symbol boundary detection, that is, to know exactly the timing of packet start and send the exactly correct symbol into FFT.
In AWGN channel, the proposed algorithm and the ideal packet synchronization have the similar curves; they have almost the same performance. In Complex channel, although the curves are not smooth, basically they are still similar. Eventually the result in about 2.7dB lost when PER <0.1, but it may be cause by using too few packet numbers in the simulation.
-5 0 5 10 15
14 16 18 20 22 24 26 28
Figure 4-5: System performance of packet synchronization under AWGN and Complex channels
4.2.2 Timing Synchronization
As mention before, the multiphase generator is used to generate 22 phases between one clock cycle. In other word, the phase error 22 means that signal is delay one cycle, and the phase error 0 means that sign is at ideal phase.
With different initial phase errors, after timing synchronization, including coarse and fine timing synchronization, the final phase errors are convergence into 2 phases.
As shown in Figure 4-6.
Figure 4-6: The final phase errors with AWGN and Multipath (TGn E)
35
First, consider with the 4*4 MIMO-OFDM system with 16 QAM modulation and TGn channel D (RMS=50ns, Tap=8). The performances are shown in Figure 4-7.The legend ideal sampling means to get each sample at right phase (phase error 0).
One symbol locked sampling means use the proposed algorithm in section 3.2 with an unknown initial phase errors to get sample.
Figure 4-7-(a) shows the required SNR for 10% PER is 16 dB, lost about 0.2 dB when compare with the ideal sampling. Take the SCO effect into consideration, the required SNR for 10% PER with SCO 200 ppm is about 16.7dB, lost about 0.7 dB when compare with no SCO effect. Figure 4-7-(b) shows the proposed algorithm can tolerance SCO effect about 200 ppm.
Figure 4-8 present the performance of 4*4 MIMO-OFDM system with 16 QAM modulation and TGn channel E (RMS=100ns, Tap=15). Figure 4-8-(a) shows the required SNR for 10% PER is 25 dB, lost about 1.6 dB when compare with the ideal sampling. Take the SCO effect into consideration, the required SNR for 10% PER with SCO 200 ppm is about 27dB, lost about 2 dB when compare with no SCO effect.
Figure 4-8-(b) shows the proposed algorithm can tolerance SCO effect about 200 ppm.
In Figure 4-8-(b) the required SNR for 10% PER with SCO 200 ppm is about 26dB, instead of 27 dB in Figure 4-8-(a). It could be cause by not enough packets since the curve is not smooth Figure 4-8-(a) or specific initial phase errors.
Then, consider with the 4*4 MIMO-OFDM system with 64 QAM modulation and TGn channel D (RMS=50ns, Tap=8). The performances are shown in Figure 4-9.
Figure 4-9-(a) shows the required SNR for 10% PER is about 20.5 dB, lost about 0.8 dB when compare with the ideal sampling. Take the SCO effect into consideration, the required SNR for 10% PER with SCO 200 ppm is about 22dB, lost about 1.5 dB when compare with no SCO effect. Figure 4-9-(b) shows the proposed algorithm can tolerance SCO effect about 200 ppm.
Figure 4-10 present the performance of 4*4 MIMO-OFDM system with 64 QAM modulation and TGn channel E (RMS=100ns, Tap=15). Figure 4-10-(a) shows the required SNR for 10% PER is about 35 dB, lost about 2.8 dB when compare with the ideal sampling. Take the SCO effect into consideration, the required SNR for 10%
PER with SCO 200 ppm is about 35.9 dB, lost about 0.9 dB when compare with no SCO effect. Figure 4-10-(b) shows the proposed algorithm can tolerance SCO effect about 200 ppm.
11 12 13 14 15 16 17 18 19 10-1
100
SNR
PER
16QAM,TGn channel D (RMS 50 ns, Tap 8)
Ideal sampling with SCO 0 ppm
One symbol locked sampling with SCO 0 ppm Ideal sampling with SCO 200 ppm
One symbol locked sampling with SCO 200 ppm
(a) PER vs. SNR
Figure 4-7: The system performance of 4*4 MIMO-OFDM with 16 QAM, TGn channel D
37
16QAM,TGn channel E (RMS 100 ns, Tap 15)
Ideal sampling with SCO 0 ppm
One symbol locked sampling with SCO 0 ppm Ideal sampling with SCO 200 ppm
One symbol locked sampling with SCO 200 ppm
(a) PER vs. SNR
Figure 4-8: The system performance of 4*4 MIMO-OFDM with 16 QAM, TGn channel E
15 16 17 18 19 20 21 22 23 10-1
100 64QAM, TGn channel D (RMS 50 ns, Tap 8)
SNR
PER
Ideal sampling with SCO 0 ppm
One symbol locked sampling with SCO 0 ppm Ideal sampling with SCO 200 ppm
One symbol locked sampling with SCO 200ppm
(a) PER vs. SNR
Figure 4-9: The system performance of 4*4 MIMO-OFDM with 64 QAM, TGn channel D
39
64QAM, TGn channel E (RMS 100 ns, Tap 15)
Ideal sampling with SCO 0 ppm
One symbol locked sampling with SCO 0 ppm Ideal sampling with SCO 200 ppm
One symbol locked sampling with SCO 200 ppm
(a) PER vs. SNR
Figure 4-10: The system performance of 4*4 MIMO-OFDM with 64 QAM, TGn channel E
Chapter 5
Hardware Implementation
n synchronization scheme for 4*4 MIMO-OFDM systems is implemented.
Section 5.1 gives the architecture of the 4× 4 packet synchronization algorithm. The timing synchronization scheme is described in section 5.2. The hardware specifications, area report and power report of the proposed 4× 4 synchronization algorithm are summarized in section 5.3 .
One Symbol Locked Acquisition All-Digital
Interpolator (ADCM) 2x fixed
clock
ADC (I & Q)
9 bits
RF signals
The proposed solution
Symbol Boundary Detection
Packet Detection Buffer
Buffer Buffer Buffer
9 bits 1 bit
16 bits 16 bits 16 bits 5 bits
Figure 5-1: Data flow chart for hardware
A
5.1 Packet Synchronization
5.1.1 Packet Detection
Figure 5-2 illustrates the architecture of the packet detection. There are four sets calculators for each antenna to calculate u , as show in equation (3.5). One set i calculator for geometric mean is used to come out u and the packet detection controller architecture was show in Figure 3-6.
Xn
Xn-1
/
Packet Detection Controlier
∑
∑ | |2
| |2 Xn-15
……
ui
Geometric mean
u
Figure 5-2: Hardware design of packet detection scheme
5.1.2 Symbol Boundary Detection
Figure 5-3 illustrates the architecture of the symbol boundary detection. There are four sets calculator for each antenna to calculate the boundary coefficients P k , i L, ( )
and the definition of Q k is show in equation (3.9). Then, L( ) Symbolˆ index Decision, which show in equation (3.11), generate a Symbolˆ index and pass boundary coefficient to timing synchronization.
∑
∑
∑
Figure 5-3: Hardware design of symbol boundary detection
43
5.2 Timing Synchronization
Figure 5-4 illustrates the architecture of the timing synchronization. There are four sets data buffer for each antenna, one packet detection and one symbol boundary detection. After received the three boundary coefficient from symbol boundary detection, the timing synchronization will locked the phase during one symbol by doing coarse and fine timing synchronization, the detail is show in section 3.2.
Pi,D-1
Pi,D
Pi,D+1
Slope-based acquisition
Pi,14
Pi,15
Pi,16
Pi,2
Pi,4
Pi,1
Figure 5-4: Hardware design of timing synchronization
5.3 Summary
Table 5-1 Hardware specifications Hardware specifications
Application IEEE 802.11n MIMO-OFDM
Space-Time Coding 4×4 STBC
FFT/IFFT Size 64 point
Support Antenna Configuration 4 Tx, 1~4 Rx
Support Modulation Type BPSK, QPSK, 16-QAM, 64-QAM
Technology UMC 0.13 mμ
System Clock 20 MHz
The hardware specifications, area report and power report are listed in Table 5-1, Table 5-2, and Table 5-3, respectively.
Table 5-2 Area report Area Report
Combinational area 919236.062500 Noncombinational area 235669.484375 Total cell area 1155449.000000
Table 5-3 Power report Power Report
Global Operating Voltage 1.08(V)
Cell Internal Power 10.3144 mW (82%) Net Switching Power 2.2623 mW (18%) Total Dynamic Power 12.5767 mW (100%)
Cell Leakage Power 1.0648 mW
Chapter 6
Conclusion and Future Work
N this thesis, based on the share architecture and the preamble structure of IEEE 802.11n standard, a synchronization algorithm for IEEE 802.11n WLANs over TGN channels is proposed. A realistic channel model was employed, which includes the effects of physical channel, filtering, carrier offset, sampling clock offset, and impulsive inference. Loss in system performance due to synchronization error was used as a performance criterion.
6.1 Conclusion
In section 3.1.1, an autocorrelation based packet detection was introduced. The comparisons are shown in Table 6-1.
Table 6-1: Comparisons among state-of-the-art packet detection algorithm
[16] [17] [18] This work
Modulation CPM OFDM OFDM OFDM
Correlator N/A 1 3 1
Detection method One-shot One-shot One-shot FSM
Correlation window N/A 32 sample 32 sample 16 sample Redetection period 3 symbols N/A N/A 1 symbol
Required SNR 10 dB 10 dB 2.5 dB 0 dB
Packet loss rate 10% N/A 12% <1%
I
In section 3.1.2, cyclic extension of L-STS is used to synchronize the symbol boundary. Since the estimator uses the inherent information of the OFDM preamble form the buffer, no additional training sequence is needed. The correlation window size B is set as 13 sample. The comparisons are shown in Table 6-2.
Table 6-2: Comparisons among state-of-the-art packet symbol boundary detection algorithm
[22] [24] This work
System platform OFDM OFDM OFDM
Hardware Integration Autocorrelation
Moving-average Crosscorrelation Required Sample 16 sample 16/64 sample 13 sample
Required SNR N/A 10 dB 5 dB
In section 3.2, a timing synchronization algorithm using the boundary coefficient to made decision was proposed. In other words, the timing synchronization shared the hardware architecture with symbol detection. A multiphase generator with 22 phases was used. Each of coarse and fine timing synchronization used half a L-STS, equal to 8 samples, the overall timing synchronization used a L-STS. The comparisons were shown in Table 6-3.
Table 6-3: Comparisons among state-of-the-art timing synchronization algorithm
[25] [26] [27] [28] This work
Digital All-Digital All-Digital
Method DAC +
47
6.2 Future Work
A wireless communication system may experiment several kind of noise. In most of the cases, like thermal, atmospheric, or galactic noise, it can be represented by a Gaussian model. However, man-made noise that appears in urban environments cannot be assumed to be Gaussian. There are still some problems need to be solve. An exhaustive impulsive interference mode should be modified. Preamble and data compensations must be done.
As to the system, high QAM constellation likes 256-QAM for higher data rate is going to be deployed. Then, more antennas of transmitter and receiver like 8*8 are taken into consideration. Even huge FFT/IFFT (size bigger than 1000) is also a good research topic.
Bibliography
[1] T. Ojanperá and R. Prasad, "An overview of wireless broadband communications", IEEE Commun. Mag., vol. 35, pp. 28-34, Jan. 1997.
[2] T. Pollet, M. Van Bladel, and M. Moeneclaey, “BER sensitivity of OFDM systems to carrier frequency offset and Wiener phase noise”, IEEE Trans.
Commun., vol. 43, pp. 191-193, Feb./Mar./Apr. 1995 .
[3] M. Gudmundson and P.O. Anderson, “Adjacent channel interference in an OFDM system”, in Proc. Vehicular Technol. Conf., Atlanta, GA, May 1996, pp.
918-922.
[4] H. Minn, V. K. Bhargava, K. B. Letaief, “A robust timing and frequency synchronization for OFDM systems”, IEEE Trans. on wireless Commun., vol. 2, pp. 822-839, No. 4, July 2003.
[5] Hao Zhou, Malipatil A.V., Yih-Fang Huang, “Synchronization Issues in OFDM Systems”, Circuits and Systems, APCCAS 2006, pp. 988-991, Dec 2006
[6] M. Speth, S. Fechtel, G. Fock, and H. Meyr, “Optimum Receiver Design for Wireless Broad-Band Systems Using OFDM – Part I”, in IEEE Trans. on Comm., vol. 47, no. 11, pp. 1668-1677, Nov.1999.
[7] 802.11n standard, “TGn Sync Proposal Technical Specification”, IEEE 802.11-04/0889r7, July 2005.
[8] A. Palin, Symbol synchronization in DVB-T OFDM system, Master of Science Thesis, Tampere University of Technology, 1997.
[9] T. M. Schmidl, and D. C. Cox. “Robust Frequency and Timing Synchronization for OFDM-based WLANs”, IEEE Trans. Communications, vol.45, No 12, pp.
1613-1621. Dec. 1997.
[10] P. Dent, G. Bottomley and T. Croft, “Jakes Fading Model Revisited”, IEE Electronics Letters, p.1162~p.1163, June 1993
[11] W. C. Jakes, Ed., “Microwave Mobile Communications”, IEEE Press, Piscataway, NJ, 1974
49
[12] J. Lago-Fernández. and. J. Salter J. Lago-Fernandez and J. Salter, “Modelling impulsive interference in DVB-T: statistical analysis, test waveforms & receiver performance”, R&D white Paper, WHP080, April 2004.
[13] M.C. Jeruchim, P. Balaban, K.S. Shanmugan, Simulation of Communication Systems: Modelling, Methodology, and Techniques, 2nd Ed., Kluwer Academic / Plenum Publishers, Aug 2000.
[14] K. L. Blackard, T.S. Rappaport, and C.W. Bostian, “Measurements and Models of Radio Frequency Impulsive Noise for Indoor Wireless Communications”, IEEE Journal on Selected Areas in Comms., Vol. 11, No. 7, Sep 1993.
[15] IEEEE P802.11 Wireless LANs, “Indoor MIMO WLAN Channel Models (Draft Document in Progerss_Rev1)”, IEEE 802.11-03/161r1, July 11, 2003.
[16] Penrod R., Fitz M.P., Weijun Zhu, Takeshita O., “low complexity packet detection algorithm for a CPM modem” Signals, Systems and Computers, 2004.
Conference Record of the Thirty-Eighth Asilomar Conference on,Vol. 1, pp.
1062-1067 Nov. 2004
[17] Ting-Jung Liang, Xin Li, Irmer R., Fettweis G., “Synchronization in OFDM-based WLAN with transmit and receive diversities” Personal, Indoor and Mobile Radio Communications, 2005. PIMRC 2005. IEEE 16th International Symposium on, Vol. 2, pp. 740-744, Sep 2005
[18] Ying Tan, Leyonhjem S., Faulkner M., ”Timing Synchronization for OFDM-WLANs with Time Averaging Scheme”, Vehicular Technology Conference, 2006. VTC 2006-Spring. IEEE 63rd, Vol. 4, pp. 1942-1946, 2006 [19] Chia-Hsiang Yang, Yu-Hsuan Lin, Shih-Chun Lin, Tzi-Dar Chiueh, “Design of a
low-complexity receiver for impulse-radio ultra-wideband communication systems”, Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on, Vol. 4, pp.23-26, May 2004
[20] Calderbank A.R., Georghiades C.N., ”Coding for the unsynchronized optical OPPM channel”,Communications, 1993. ICC 93. Geneva. Technical Program, Conference Record, IEEE International Conference on,Vol. 1, pp.557-561, May 1993
[21] K. Witrisal, G. Leus, M. Pausini, and C. Krall, “Equivalent system model and equalization of differential impulse radio UWB systems,” IEEE J. Sel. Areas Commun., vol. 23, no. 9, pp. 1851-1862, Sept. 2005.
[22] Hsi-Chou Hsu, Jyh-Horng Wen, “Timing Synchronization in Ultra-Wideband Systems with Delay Line Combination Receivers”, Communications Letters, IEEE, Vol. 11, pp. 264-266, March 2007
[23] Yi-Ju Chen, Yi-Ching Lei, Tzi-Dar Chiueh, “Baseband transceiver design for the DVB-terrestrial standard”, Circuits and Systems, 2004. Proceedings. The 2004
IEEE Asia-Pacific Conference on, Vol. 1, pp.389-392, Dec. 2004
[24] Manusani S.K., Kshetrimayum R.S., Bhattacharjee R., “Robust Time and Frequency Synchronization in OFDM based 802.11a WLAN systems”, Annual India Conference, 2006, pp. 1-4 , Sep. 2006
[25] J.Y. Yu, C.C. Chung, H.Y. Liu, and C.Y. Lee, “Power Reduction with Dynamic Sampling and All-Digital I/Q-Mismatch Calibration for A MB-OFDM UWB Baseband Transceiver,” IEEE Symposium on VLSI Citcuits, June 2006.
[26] S. Sibecas, C.A. Emami, G. Stratis, and G. Rasor, “Pseudo-Pilot OFDM scheme for 802.11a and R/A in DSRC Applications,” IEEE VTS-Fall, VTC, Oct 2003.
[27] A.I. Bo, G.E. Jian-hua, and W. Yong, “Symbol Synchronization Technique in COFDM Systems,” IEEE Trans. Broadcasting, vol. 50, pp. 56-62, March 2004.
[28] Terng-Yin Hsu, You-Hsien Lin and Ming-Feng Shen, ” Synchronous Sampling Recovery with All-Digital Clock Management in OFDM Systems”, 2007