Adaptive time synchronization for VHT Wireless LAN Nanda Kishore Chavali
P. Venkata Krishna Reddy
Uurmi Systems Pvt Ltd, 8-2-268/28/N, Road No. 2, Banjara Hills, Hyderabad, 500 034, India. +91-04023542715
Uurmi Systems Pvt Ltd, 8-2-268/28/N, Road No. 2, Banjara Hills, Hyderabad, 500 034, India. +91-04023542715
[email protected]
[email protected]
ABSTRACT IEEE 802.11ac is an upcoming standard for wireless LAN physical layer and MAC layer for very high throughput (VHT) communication with targeted throughput greater than 1 Gbps [1] below 6 GHz band. The IEEE 802.11ac (also termed as VHT wireless LAN) physical layer is based on orthogonal frequency division multiplexing (OFDM) modulation and signal processing with multiple antennas at transmitter and receiver (MIMO). A device compliant to IEEE 802.11ac standard should be backward compatible to wireless LAN standards IEEE 802.11a and IEEE 802.11n. The VHT Physical layer supports legacy frame format, high throughput (HT) mixed frame format, HT green-field frame format and VHT mixed format [2, 3]. The VHT wireless LAN receiver has to synchronize for any of these frame formats. The OFDM modulation is sensitive to timing synchronization in multipath fading channel. The cyclic shift diversity (CSD) used in VHT wireless LAN introduces pseudo multipath at the receiver and causes error in the symbol boundary estimate. Hence, there is a need for a robust and adaptive time synchronization that suits for all frame formats in VHT wireless LAN. Without the accurate time synchronization, there will be inter symbol interference (ISI) and inter carrier interference (ICI) in the demodulated data, and cause many bit errors in the decoded data. In this paper, we propose a robust and adaptive timing synchronization technique for VHT Wireless LAN device compliant to IEEE 802.11ac standard. We will show the need for the adaptive window length in time synchronization theoretically. The proposed method works well for all frame formats and BW options of the IEEE 802.11ac standard. We will give simulation results to show the usefulness of the method.
Categories and Subject Descriptors C.2.1 [Computer-Communication Networks]: Architecture and Design – wireless communication.
Network
VHT Wireless LAN; adaptive time synchronization; Wireless LAN systems; IEEE 802.11ac;
1. INTRODUCTION In the recent past, IEEE 802.11 wireless LAN (WLAN) has emerged as one prevailing wireless technology throughout the world and will also play an important role in the future fourthgeneration wireless and mobile communication systems. The original IEEE 802.11 standard included three physical layer options: infrared (IR), 2.4 GHz frequency hopped spread spectrum (FHSS), and 2.4 GHz direct sequence spread spectrum (DSSS). The IEEE 802.11b enhanced DSSS with complementary code keying (CCK), increasing the data rate to 11 Mbps. The development of 802.11a introduced orthogonal frequency division multiplexing (OFDM) to 802.11. Subsequently, the 802.11 working group developed the 802.11g amendment, which incorporates the 802.11a OFDM PHY in the 2.4 GHz band. In addition, backward compatibility and interoperability is maintained between 802.11g and the older 802.11b devices. With the development of IEEE 802.11n, the data rate is increased to 300 Mbps in 20 MHz and 600 Mbps in 40 MHz. The upcoming IEEE 802.11ac very high throughput (VHT) wireless LAN increases the data rate beyond 1Gbps in 5 GHz band. The device compliant to IEEE 802.11ac is backward compatible to previous wireless LAN standards based on OFDM modulation and supports NON-HT or Legacy, HT-MF, HT-GF and VHT frame formats [2, 3]. The problem with the OFDM modulation is that it is very sensitive to time and frequency synchronization under variable channel delay spreads and fading. Without proper time synchronization, the orthogonality of the subcarriers will be lost and there will be inter symbol interference (ISI) and inter carrier interference (ICI). L-STF
L-LTF
L-SIG
L-Data
L-STF
L-LTF
L-SIG
HT-SIG
HT-STF
HT-LTF
HT-SIG
HT-Data
L-STF
L-LTF
L-SIG
VHT-SIG-A
HT-STF
HT-LTF
HT-Data
General Terms Algorithms, Performance, Design, Experimentation, Theory, Verification.
Keywords Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ICACCI’12, August 3-5, 2012, Chennai, T Nadu, India. Copyright 2012 ACM 978-1-4503-1196-0/12/0010…$10.00.
VHT-STF
VHT-LTF
VHT-SIG-B
VHT-Data
Figure 1. IEEE 802.11ac PPDU frame formats. In general, the time synchronization methods are based on the correlation between the received preamble and the transmitted preamble [4, 5]. In these methods, the sample index corresponding to the peak of the correlation is taken as the symbol boundary. But, the estimated symbol boundary will be shifted when the first tap of the channel impulse response is not dominant. The fine time synchronization based on the position of the maximum correlation energy is described in [8 - 10]. However
in VHT wireless LAN, since the same preamble is transmitted from the different antennas with different cyclic shifts it will spread the computed correlation by unknown amount at the receiver and estimated symbol boundary may go wrong. This phenomenon is called pseudo multipath. The time synchronization techniques based on the correlation methods will not give correct results because of the existence of pseudo multipath in VHT wireless LAN. Any system based on OFDM modulation with cyclic prefix length L should be able to tolerate a delay spread of less than or equal to L. As the VHT wireless LAN system uses a cyclic prefix of 0.8 micro seconds, it is supposed to work in a multipath channel with the maximum delay spread of 0.8 micro seconds. The coarse symbol boundary estimated using the symbols of short training field will not be accurate because each symbol of short training symbol has duration of 0.8 micro seconds and transmitted symbols from different antennas are cyclically shifted by a suitable value. To resolve the ambiguity of symbol boundary, a fine synchronization is usually done. In the fine time synchronization, the correlation of received signal with the reference signal is computed during the long training field. The index corresponding to the peak of energy of the correlation computed over window corresponding to GI length will then give the fine symbol boundary. But, the VHT wireless LAN system suffers from the problem of pseudo multipath due to cyclic shift applied at the transmitter. These cyclic shifts are different for different parts of the frame and for different frame formats, which is unknown at the receiver. The maximum amount of cyclic shift depends on the number of space time streams [1]. In addition, the maximum delay spread is a variable and changes for each channel realization. Therefore, the fine symbol boundary estimation based on the fixed energy window will not give accurate symbol boundary for VHT wireless LAN system. Hence, there is a need for adaptive time synchronization method. The method should work for all delay spreads up to maximum delays spread, for different cyclic shifts and for different frame formats. The synchronization method should also take care of channels with smaller delay spreads. In this paper, we propose an adaptive time synchronization method exploiting the pseudo multipath in the correlation for VHT wireless LAN. We will show that the proposed method works for all frame formats and bandwidth options. We provide simulation results to show the usefulness of the method. We also give comparative results for the proposed method and different methods in wireless multipath fading channel models. At the receiver, before adaptive time synchronization, we perform the energy detection and automatic gain controller (AGC) gain estimation, coarse symbol boundary estimation, coarse carrier frequency error estimation and detection of long training field. AGC gain is estimated such that the received signal will occupy the full analog to digital converter (ADC) bit width on each receiver chain. In the coarse symbol boundary estimation, the received signal on each receive chain is cross correlated with the reference short symbol for one short symbol duration and the magnitudes of correlated outputs on all antennas are added. The index corresponding to the maximum amplitude of the added correlation gives the coarse symbol time synchronization. Then, detection of long training field, also called short to long (S2L) detection and coarse carrier frequency offset estimation are started in parallel. The S2L detection is observed if the maximum normalized correlation of received short symbols falls below certain value. Once S2L is detected we will start the proposed adaptive time synchronization method.
The rest of the paper is organized as follows. In Section II, we describe IEEE 802.11ac MIMO-OFDM based WLAN system model. In Section III, the proposed adaptive time synchronization method is presented. We also give motivation for the proposed method. In Section IV, we will show theoretically, the need for adaptive window length in the time synchronization. Simulation results are given in Section V. Finally, we give conclusions in Section VI.
2. SYSTEM MODEL We consider a MIMO-OFDM system with NT transmit antennas (TXs) and NR receive antennas (RXs) based on the IEEE 802.11ac [1]. All the TXs will transmit PHY protocol data units (PPDUs) simultaneously to achieve high data rates. In order to facilitate the receiver to accomplish AGC, synchronization and channel estimation, preamble sequence is transmitted at the beginning of each PPDU. The PPDU structure for VHT mixed format frame is shown in Fig. 1. The preamble consists of a legacy preamble and very high throughput (VHT) preamble. Each part comprises a short training field and a long training field. Legacy short training field (L-STF) consists of ten identical legacy short training symbols (L-STSs), and legacy long training field (L-LTF) consists of two identical long training symbols (L-LTSs) and a guard interval (GI). In order to avoid undesired beamforming and achieve spatial diversity, CSD scheme is deployed at the transmitter[1,3]. In this scheme, transmitted signals from each TX are cyclic shift delayed versions of its original modulated signal. For example, if TXi has a cyclic shift delay of d i , the transmitted OFDM symbol from TXi is described as:
s ( n − d i ) si ( n ) = s ( n − d i − N )
0 ≤ n ≤ N + di N + di ≤ n ≤ N
(1)
Where s(n) is the signal at the output of TXi inverse discrete Fourier transform (IDFT) before CSD, si (n) is the signal from TXi after CSD, and N denotes DFT size. The CSD value of TX1 is set to 0, and other TXs have different CSD values as in [1]. The cyclic prefix of length L is then applied to si (n) .
si ( n + N − L ) xi ( n ) = si ( n − L )
0 ≤ n ≤ L −1 L ≤ n ≤ N + L −1
(2)
The received signal at the RXj in the presence of multipath channel and additive white Gaussian noise (AWGN) can be described as: N T L −1
r j (n) = ∑ ∑ h ji (l ) xi (n − l ) + w j (n)
(3)
i =1 l =0
3. PROPOSED ADAPTIVE TIME SYNCHRONIZATION METHOD The steps of proposed adaptive time synchronization are described below. Compute the correlation between the received
signal on mth receive chain rm (n) and a reference signal s(n) (L-LTF in time domain) during the long training field for different lags, N −1
pm (k ) = ∑ rm (n + k ) s * (n)
(4)
n =0
where N is the length of the reference long symbol. Add the magnitude square of the correlation computed in (4) on all receive chains, Nr
P (k ) = ∑ p m ( k )
2
(5)
m =1
where N r is the number of receiver chains. Conventional methods use fixed window length to find the energy on correlation sum given in (5). We prove in the next section that the optimum window length should include delay spread and maximum cyclic shift applied at the transmitter chain. To estimate the width of the delay spread including cyclic shift if any, we perform the smoothing on P(k ) using the window length WS . WS
Q(k ) = ∑ P(k + m)
(6)
m =1
The parameter WS is tunable and we have taken its value as 0.2 micros seconds. We then find the peak of Q (k ) , a sample index
d l on left side to peak where Q (k ) crossing threshold Tl and a sample index d r on the right side to peak where Q (k ) falling below threshold Tr . Using d l and d r estimate the best window length for energy computation as
WE = d r − d l + 1
d opt = d opt + CS _ SAMPLES
(10)
where CS _ SAMPLES is the maximum cyclic shift in samples applied at the transmitter chain. In channels with very small delay spread, the energy computed in (8) may give rise to some plateau. Because of this plateau, in the presence of noise, the decision taken in (9) may go wrong. To resolve this ambiguity, we propose a modification in the comparison operation in (9) as follows. While moving from left, to decide the present sample is larger than previous maximum, the current sample should be greater than by some percent (say 0.5%) of previous maximum. Our intuition here is matched with simulation results. As long training filed satisfies conjugate symmetric property, the complexity of the method can be reduced by employing N/2 samples in the correlation computation of (4). To see how proposed method works in real time, we give now motivation using simulations. Figure 2 shows the plot of the normalized correlation of the received long symbol for different lags by transmitting VHT frame considering two transmitter chains. The two peaks in correlation profile are due to different cyclic shifts on space time streams at the transmitter. At the receiver, the number of space time streams is unknown and the delay spread of the channel varies. To capture the total delay spread including cyclic shift delay best energy window length should be estimated. We can observe here that it is difficult to find the width of the correlation spread accurately using Figure 2, so we used smoothed normalized correlation. Figure 3 shows the plot of the smoothed normalized correlation for different lags. We find the maximum value of the smoothed normalized correlation and set the left and right threshold using the maximum value, for estimating left and right boundaries. Using left and right boundaries we can estimate the best window length or spread of the correlation. Figure 4 shows the plot of the energy for different lags and the sample index corresponding to the maximum value of the correlation energy gives the symbol boundary. We advance the symbol boundary by maximum cyclic shift delay (4 samples during L-LTF) after decoding the signal field. The symbol boundary estimated here is found to be exact.
(7) 1
The window length estimated in (7), is used to compute the energy of correlation given in (5) as
E (k ) = ∑ P(k + m)
(8)
m=1
Then, the fine symbol boundary estimate d opt is given by
d opt = max[E (k )]
(9)
k
magnitue of normalized correlation
WE
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
By using the symbol boundary estimate given in (9) we perform the rest of receiver operations to decode signal fields. If the parameter in the signal field, the number of space time streams is greater than one, we advance the symbol boundary estimated in (9) by appropriate cyclic shift value.
0
5
10
15
20 25 sample index
30
35
40
45
Figure 2. Magnitude of normalized correlation for different lags
Where d 2 is the cyclic shift applied on the second transmitter chain. The R.H.S of (12) has two terms. In the first term, signal s(n) is used and in the second term, cyclic shifted s(n) is used. Because of this, in the correlation computation, we tend to get the spread at two different locations separated by the amount of cyclic shift. This phenomenon is generally interpreted as pseudo multipath in MIMO wireless systems. The coarse symbol boundary is estimated by cross correlating the received samples during short training field (STF), with the local sequence.
1 0.9
smoothed normalized correlation
0.8 0.7 0.6 0.5 0.4 0.3 0.2
L −1
0
*
c(d ) = ∑ r1 (n + d ) s s (n)
0.1
(13)
n =0
0
5
10
15
20 25 sample index
30
35
40
Figure 3. Smoothed normalized correlation for different lags 1 0.9
The index of the maximum amplitude norm of the correlation vector corresponds to the coarse symbol boundary. The correlation vector is obtained from the correlations on different receive chains. Because of the pseudo multipath and variation in the channel delay spread, there is uncertainty in the estimated symbol boundary based on correlation peak.
0.8
d c = arg max c(d )
0.7
dε ( 0, L −1)
(14)
energy
0.6 0.5
where, d c is the coarse symbol boundary and ss (n) is local sequence (reference short training symbol in time domain). The coarse symbol boundary position varies depending on the instantaneous channel magnitudes ( h11 and h12 ). To overcome the pseudo multipath problem and inter symbol interference, fine symbol timing synchronization is proposed.
0.4 0.3 0.2 0.1 0
0
2
4
6
8 10 sample index
12
14
16
18
Figure 4. Normalized energy of the correlation for different lags
In the proposed fine symbol time estimation, the received samples are cross correlated with local sequence (during long training symbol), the cross correlation values for different time lags are given by,
4. THEORY In this section we prove that the window length required for the energy computation in time synchronization should be the sum of cyclic prefix length and the maximum cyclic shift applied at the transmitter. We will also show the need for the adaptive window length in the time synchronization. Without loss of generality consider a case of two transmit antennas and one receiver antenna system (represented as 2x1). This is a typical case when space time block coding is employed in the system. For this case, (3) can be simplified as
N −1
p (k ) = ∑ r1 (n + k ) s * (n)
(15)
n=0
where s (n) is time domain long training sequence. Substituting (12) in (15), we get N −1 L −1 p (k ) = ∑ ∑ h11 (l ) s ( n + k − l ) + n = 0 l = 0
(16)
* ∑ h12 (l ) s (( n + k − l − d 2 ) mod N ) + w1 ( n + k ) s (n) l =0 L −1
L −1
L −1
l =0
l =0
r1 (n) = ∑ h11 (l ) x1 (n − l ) + ∑ h12 (l ) x2 (n − l ) + w1 (n)
(11)
Expanding and rearranging terms in (16), Using (1) and (2), and assuming that the cyclic shift on the first transmit chain is zero, (11) can be written as
L −1
N −1 * ∑ h12 (l ) ∑ s ((n + k − l − d 2 ) mod N )s (n) + l =0 n =0 L −1
r1 ( n) = ∑ h11 (l ) s ( n − l ) + l =0
L −1 N −1 p(k ) = ∑ h11 (l ) ∑ s (n + k − l ) s * (n) + l =0 n =0
(12)
L −1
N −1
l =0
n =0
∑ h12 (l ) s (( n − l − d 2 ) mod N ) + w1 (n)
* ∑ w1 (n + k ) s (n)
(17)
The auto correlation of transmitted signal can be written as N −1
2
* ∑ s (n + k − l ) s (n) = σ S δ (k − l )
(18)
model. For multipath channels, we considered TGac channel models [12]. The channel models also introduce the pathloss and fading to the transmitted output signal. The relation between the transmit power, SNR and the pathloss is given by
n =0
Substitute Equation (18) in Equation (17) and after simplification 2
(19)
N −1
* ∑ w1 ( n + k ) s ( n)
n =0 2
where, σ s is energy of the reference long training symbol. The magnitude of the third term is very small when compared with the sum of the first two terms. The energy is calculated on the correlation output using effective window size (w), for different lags is given by, 2
E (r ) = ∑ p(k + r )
(20)
k =0
Substitute Equation (19) in Equation (20), after simplification we get 2
4 w
4 w
k =0
4 w
Signal to Noise Ratio at the receiver and N _ FIG is the noise figure. In the simulation setup, we vary the SNR from 0 dB to 10 dB with block fading using the TGac channel models B, D and E. The channel models are selected to cover low, medium and high delay spread scenarios. For each SNR, We run simulations for 1000 realizations of channel and noise. In each realization, we generate a different transmit frame and pass through AFE, the channel and AWGN noise and other impairments. At the receiver, we perform energy detection, AGC gain estimation, coarse time synchronization, long training field detection and estimate the symbol boundary using the proposed adaptive fine time synchronization. We have used channel bandwidth of 20 MHz in the simulations. We performed simulation with the proposed method and also with conventional method (without using adaptive window length). We compute the percentage of correct detections. If the estimated symbol boundary is in the inter symbol interference (ISI) free region, then it is taken as the correct detection. The results with percentage of correct detections for these two methods are shown in Figures 5 – 7.
2
E (r ) = σ s ∑ h11 (k + r ) + σ s ∑ h12 ((k + r − d 2 ) mod N ) + k =0
(23)
Where, TX _ POWER is the transmit power, SNR is the required
2
p ( k ) = σ s h11 ( k ) + σ s h12 (( k − d 2 ) mod N ) +
w
path _ loss _ dB = TX _ POWER − SNR − N _ FIG
(21) 100
*
2σ S ∑ Re(h11 (k + r )h12 ((k + r − d 2 ) mod N ))) k =0
95
4 w
2
4 w
E (r ) = σ S ∑ h11 (k ) + σ S ∑ h12 ((k − d 2 ) mod N ) k =0
2
(22)
k =0
90 % of correct detections
In (21), as the two channel taps are uncorrelated, the third term is very small compared to the first and the second terms, so it can be neglected.
85 80 propsed method, 2x1 propsed method, 2x2 propsed method, 2x1 propsed method, 2x2 conventional method, conventional method, conventional method, conventional method,
75 70
As h11 ( k ) and h12 ( k ) can have spread equal to cyclic prefix length, from equation (22), we can see that to capture the total sub channel multipath delay spread, the effective window size w should be sum cyclic prefix length and maximum delay spread ( d 2 here). Since the maximum cyclic shift value is unknown at the receiver and the width of the channel delay spread is a variable, we need an adaptive window length estimator given in (7) in the fine time synchronization.
5. SIMULATION RESULTS To prove the usefulness of the proposed method in real systems, we have performed simulations in the multipath fading channels. We considered VHT mixed format packet consisting of preamble, signal fields and data as shown in Figure 1. For generating the short symbols, only one-fourth of the used subcarriers are loaded and for generating the long symbols, all used subcarriers are loaded [1]. The preamble is followed by signal field and sequences of data field. We modeled AFE section with analog filters, DAC/ADC, TGac channel [11,12] and RF impairments. The transmitted signal is passed through analog front end (AFE)
65 60
0
1
2
3
4
5 6 SNR (in dB)
7
without fading without fading with fading with fading 2x1 without fading 2x2 without fading 2x1 with fading 2x2 with fading 8
9
10
Figure 5. Perecentage of correct detections in Tgn B channel
there is a need for the robust and adaptive time synchronization to estimate the symbol boundary accurately. In this paper, we proposed an adaptive time synchronization method exploiting the pseudo multipath. We have shown theoretically the need for adaptive window length in the time synchronization. We provided simulation results to show the usefulness of the proposed method in real systems. The proposed method works well for all BW options 20 MHz, 40 MHz, 80 MHz and 160 MHz and any number of transmitting and receiving antennas.
model 100 95
% of correct detections
90 85 80 propsed method, 2x1 propsed method, 2x2 propsed method, 2x1 propsed method 2x2, conventional method, conventional method, conventional method, conventional method,
75 70 65 60
0
1
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3
4
5 6 SNR (in dB)
7
without fading without fading with fading with fading 2x1 without fading 2x2 without fading 2x1 with fading 2x2 with fading 8
9
7. REFERENCES [1] IEEE 802.11ac, 2011. Specification frame work for ac: IEEE 802.11-09/0992r21, January 2011.
10
Figure 6. Perecentage of correct detections in Tgn D channel model 100 95
% of correct detections
90
80 propsed method, 2x1 propsed method, 2x2 propsed method, 2x1 propsed method 2x2, conventional method, conventional method, conventional method, conventional method,
70 65 60
0
1
2
3
4
5 6 SNR (in dB)
7
without fading without fading with fading with fading 2x1 without fading 2x2 without fading 2x1 with fading 2x2 with fading 8
9
[3] IEEE 802.11n-2009 part 11 wireless LAN medium access control (MAC) and physical layer (PHY) specifications. [4] Wang, K., Faulkner, M., Singh, J., and Tolochko, I. 2003. Timing Synchronization for 802.11a WLANs under Multipath Channels, ATNAC 2003 (Australian Telecommunications, Networks and Applications Conference). [5] Misuk Cho, Yunho Jung, and Jaeseok Kim, 2009. Symbol Timing Synchronization for IEEE 802.11n WLAN Systems, First Asian Himalayas Internati onal conference. Internet, 2009. AH-ICI 2009, pp 1-6, Nov. 2009.
85
75
[2] IEEE 802.11-2007 part 11 wireless LAN medium access control (MAC) and physical layer (PHY) specifications.
[6] Proakis, John G. 2007. Digital communication, 5th edition, Mc Graw Hill. [7] Rappaport, T.S. 1996. Wireless Communications, Principles and Practice, New Jersey, Prentice-Hall. 10
Figure 7. Perecentage of correct detections in Tgn E channel model
From the Figs 5 to 7, we can infer that the percentage of correct detections reaches 100 % at SNR of 4 dB in channels B and D for proposed method. In high delay spread channel E, the probability of correct detections crosses 96% at SNR of 4 dB for proposed method. We can also notice that the curves for the conventional method tend to saturate in B and D channels. It is very clear from these figures that the proposed method is useful in real systems.
6. CONCLUSION The wireless LAN standard IEEE 802.11ac, for very high throughput physical layer and MAC layer, recommends that different cyclic shifts to be applied on signals transmitted from multiple transmitting antennas. This results in pseudo multipath at the receiver. The new standard supports four different frame formats. The delay spread of the channel also varies from one realization to another. The proper estimation of symbol boundary is very crucial for successful decoding of a packet. At the receiver,
[8] Minn, H., Bhargava, V. K., and Lataief, K. B. 2003. A Robust Timing and Frequency Synchronization and Channel Estimation for OFDM, IEEE Trans. Wireless Commun., vol. 2, pp. 822–839, Jul. 2003 Method. [9] Stuber, G.L., Barry, J.R., McLaughlin, S.W., Ye Li, Ingram, M.A., and Pratt, T.G. 2004. Broadband MIMO-OFDM wireless communications, IEEE proceedings, vol. 92, pp.271-294, Feb 2004. [10] Jooyeol Yang, Kyungwhoon Cheun, and Jeongchang Kim, 2006. Symbol Timing Synchronization Algorithm for Wireless LAN Systems in Multipath Channels, Asia-Pacific conference, Commun., pp.1-5, Aug 2006. [11] TGn Channel Models, 2004. IEEE Std. 802.11 - 03/940r4, May, 2004. [12] TGac Channel Model Addendum Supporting Material, 2009. IEEE Std. 802.11-09/0569r0, May 2009.
