doppler spread estimation in frequency selective rayleigh channels for ofdm systems athanasios...
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Doppler Spread Estimation in
Frequency Selective Rayleigh Channels for
OFDM Systems
Athanasios Doukas, Grigorios Kalivas
University of PatrasDepartment of Electrical & Computer Engineering
Applied Electronics Laboratory
Doppler Spread Estimation219/07/2006
Conclusions
Simulation Results
Doppler Spread Estimator
Fading Channel and OFDM System
Introduction
Contents
Doppler Spread Estimation319/07/2006
Introduction
Orthogonal Frequency Division Multiplexing (OFDM) has been widely applied in the last years for various wireless communication systems such as Digital Video Broadcasting (DVB) and wireless local area networks (WLANs) ensuing great success.
These systems however, should be capable of working efficiently in wide range of operating conditions, such as large range of mobile unit (MU) speeds, different carrier frequencies in licensed and un-licensed bands, various delay spreads, and wide dynamic signal to-noise ratio (SNR) ranges.
This way assessing the channel quality and its rate of change is of great importance in adapting the system parameters to continuously changing channel conditions.
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Necessity of Doppler Spread Estimation
1
It provides information about the fading rate of the channel. Knowledge of Doppler spread can improve detection and aid into transmission optimization in both physical layer and higher levels.
2
Doppler information can help in selection of appropriate transmission characteristics to combat ICI including proper channel estimators to enhance reception.
3
Specifically designed channel estimators can be applied and the rate of appliance can be chosen to improve throughput and an increase in the estimation rate can help to lower BER.
Doppler Spread Estimation519/07/2006
Switching rate of diversity branches is used for the velocityestimation but its sensitivity to the fading channel is shown.
Another type of estimators are the covariance-based estimators, which estimate the Doppler frequency from the auto- covariance of powers ofthe received signal envelope or from sums of the I/Q components.
Other methods, highly associated to LCR, are the Zero Crossing Rate (ZCR), which uses the in-phase or quadrature phase (I/Q) signal part, and some other higher order crossings of the signal envelope.
Previous Work
4 1
Doppler Estimation
Level Crossing Rate (LCR) of the received signal envelope is proportional to the Doppler frequency and thus used in Doppler estimation. However, the fading nature of the wireless channel decreases the estimator’s accuracy in low Doppler values.
2
3
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Doppler Estimator Novelty
Wide Sense Stationary (WSS)Channelwith a few samples
Novel PointsOf Estimator
Low Mobility Of Systems that divide the state of the system in two operational modesa) Movingb) Still
Doppler Spread Estimation719/07/2006
Conclusions
Simulation Results
Doppler Spread Estimator
Fading Channel and OFDM System
Introduction
Contents
Doppler Spread Estimation819/07/2006
Wireless Fading Channel (1/4)
)()t(),t(h
)t(r)t(tt)t(r t2*
Channel Impulse Response
where γℓ(t)’s are wide-sense stationary (WSS) narrowband complex Gaussianprocesses, which are independent for different paths.
Correlation Function rτ(Δt)
Doppler Spread Estimation919/07/2006
Wireless Fading Channel (2/4)
ƒ2jƒ2j e)t(de),t(h)ƒ,t(H
The frequency response of the time-varyingradio channel at time t is
Doppler Spread Estimation1019/07/2006
Wireless Fading Channel (3/4)
ƒrtr
etr
e)t(r
)ƒ,t(ƒƒ,ttƒ,tr
ƒt2
ƒ2j2t
ƒ2j
*H
The correlation function of H(t,ƒ) can be separated into the multiplication of a time domain correlation rt(Δt) and a frequency domain correlation rƒ(Δƒ).
rt(Δt) is dependent on the vehicle speed or, equivalently, the Doppler frequency, while rƒ(Δƒ) depends on the multipath delay spread.
With the separation property, we are able to propose our Doppler estimator described in the next section.
Doppler Spread Estimation1119/07/2006
]k[r]i[r]k,i[r ƒt
ƒtt iTr]i[r
ƒkr]k[r ƒƒ
]n[rnJ]n[r Jd0t
Wireless Fading Channel (4/4)
For an OFDM system with block length Tƒ and tone spacing (subchannel spacing) Δƒ, the correlation function for different blocks and tones can be written as
where
Jake’s Model
Time Correlation
Frequency Correlation
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OFDM Physical Layer
Doppler Spread Estimation1319/07/2006
1N
0k
N/nk2jk,i
k,in,i
1N,,0neX
}X{IFFTx
1N,,0keyN
1
}y{FFTY1N
0n
N/nk2jn,i
n,ik,i
Transmitted/Received Signal
TransmittedSignal
ReceivedSignal
Doppler Spread Estimation1419/07/2006
Conclusions
Simulation Results
Fading Channel and OFDM System
Introduction
Contents
Doppler Spread Estimator
Doppler Spread Estimation1519/07/2006
1N
0i
*k,ik,i
rt
r
N
1][r
]n[rnJ]n[r Jd0t
Equations Used
Time Correlation
Time Correlation using
Received Data
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Doppler Estimation Procedure
Nr=2, ρ=0
Nr=2, ρ=1
Combination of previous results
DopplerDopplerEstimationEstimation
1
2
3
Doppler Spread Estimation1719/07/2006
10J0J]0[r 0d0t
1
0i
*k,ik,it
012
0n
*k,0ik,it 2
1]0[r
02
1]0[r
1
Estimation Procedure Step 1/3
Doppler Spread Estimation1819/07/2006
dƒ0d0t ƒT2J*1J]1[r
*k,1k,0t
112
0i
*k,1ik,it ]1[r
12
1]1[r
2
Estimation Procedure Step 2/3
Doppler Spread Estimation1919/07/2006
1
0i
*k,ik,i
*k,1k,01
0f
d
2
1J
T2
1ƒ
3
Estimation Procedure Step 3/3
Doppler EstimationFormula
Doppler Spread Estimation2019/07/2006
Conclusions
Fading Channel and OFDM System
Introduction
Contents
Doppler Spread Estimator
Simulation Results
Doppler Spread Estimation2119/07/2006
0 5 10 15 20 25 30 35 40 45 500
1
2
3
4
5
6
7
8x 10
-3
Subcarrier Index
Est
imat
ion
Doppler estimation with SNR=5dB in BRAN Channel A for BPSK modulated data
Doppler estimation 0 Hz Doppler estimation 10 HzDoppler estimation 20 HzDoppler estimation 30 HzDoppler estimation 40 Hz
Simulation Results (1/4)
Doppler Spread Estimation2219/07/2006
0 5 10 15 20 25 30 35 40 45 500
1
2
3
4
5
6
7
8x 10
-3
Subcarrier Index
Est
imat
ion
Doppler estimation with SNR=5dB in BRAN Channel A for BPSK modulated data
Doppler estimation 0 Hz Doppler estimation 10 HzDoppler estimation 20 HzDoppler estimation 30 HzDoppler estimation 40 Hz
Simulation Results (2/4)
0 5 10 15 20 25 30 35 40 45 500
1
2
3
4
5
6
7
8x 10
-3
Subcarrier Index
Es
tim
ati
on
Doppler estimation with SNR=10 dB in BRAN Channel A for BPSK modulated data
Doppler estimation 0 Hz Doppler estimation 10 HzDoppler estimation 20 HzDoppler estimation 30 HzDoppler estimation 40 Hz
Doppler Spread Estimation2319/07/2006
0 5 10 15 20 25 30 35 40 45 505
5.5
6
6.5
7
7.5x 10
-3
Subcarrier Index
Estim
atio
n
Doppler estimation with SNR=10dB in BRAN Channel A for QPSK modulated data
Doppler estimation 0 Hz Doppler estimation 10 HzDoppler estimation 20 HzDoppler estimation 30 HzDoppler estimation 40 Hz
Simulation Results (3/4)
0 5 10 15 20 25 30 35 40 45 505
5.5
6
6.5
7
7.5x 10
-3
Subcarrier Index
Es
tim
ati
on
Doppler estimation with SNR=10dB in BRAN Channel A for QPSK modulated data
Doppler estimation 0 Hz Doppler estimation 10 HzDoppler estimation 20 HzDoppler estimation 30 HzDoppler estimation 40 Hz
Doppler Spread Estimation2419/07/2006
0 5 10 15 20 25 30 35 40 45 500
1
2
3
4
5
6
7
8x 10
-3
Subcarrier Index
Est
imat
ion
Doppler estimation with SNR=10dB in BRAN Channel B for BPSK modulated data
Doppler estimation 0 Hz Doppler estimation 10 HzDoppler estimation 20 HzDoppler estimation 30 HzDoppler estimation 40 Hz
Simulation Results (4/4)
0 5 10 15 20 25 30 35 40 45 500
1
2
3
4
5
6
7
8x 10
-3
Subcarrier Index
Es
tim
ati
on
Doppler estimation with SNR=10dB in BRAN Channel B for BPSK modulated data
Doppler estimation 0 Hz Doppler estimation 10 HzDoppler estimation 20 HzDoppler estimation 30 HzDoppler estimation 40 Hz
Doppler Spread Estimation2519/07/2006
Simulation Results
Fading Channel and OFDM System
Introduction
Contents
Doppler Spread Estimator
Conclusions
Doppler Spread Estimation2619/07/2006
We have presented a Doppler estimator for low mobility OFDM systems in Frequency Selective Rayleigh Fading Channels, using only two OFDM symbolsfor the time correlation in wireless OFDM systems.
The estimator instead of trying to estimate the accurate value of the Dopplerfrequency divides the mobility into a still and a moving mode, which for the case ofWLANs is the most important. We have examined its performance in wirelesschannels with different power delay profiles, including sparse channels.
The estimator, in most of the cases, manages to clearly distinguish the two modesof mobility, still or moving.
Conclusions