Accurate Estimation of Doppler Shift in Mobile Communications With High Vehicle Speed

8/13/2019 Accurate Estimation of Doppler Shift in Mobile Communications With High Vehicle Speed

1/11

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS Int. J. Commun. Syst. (2013)Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/dac.2510

SPECIAL ISSUE ARTICLE

Accurate estimation of Doppler shift in mobile communications

with high vehicle speed

J. Y. Hua1,2,*,† , D. H. Yuan1, G. Li1 and L. M. Meng1

1College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310032, China2 National Mobile Communication Research Laboratory, Southeast University, Nanjing 210096, China

SUMMARY

Representing the channel varying rate and the mobile speed of a mobile terminal directly, Doppler shift isan important parameter in vehicular mobile communications and therefore is widely used in mobile target

detection and adaptive applications. Hence, this paper puts forward an accurate Doppler shift estimator inmobile communications with high vehicle speeds, which can also be treated as a vehicular speed estimatordue to the well-known relation between the Doppler shift and the mobile speed. Specifically, the proposedestimator is based on the channel level crossing rate, and an iterative process is presented to achieve signal-to-noise ratio (SNR) insensitive estimates in accordance with the level crossing rate estimation error analysis.Moreover, we prove the convergency of the iterative Doppler shift estimator in theory. Computer simulationsconducted under a wide range of noise corruption clearly show that the proposed estimator substantially out-performs several existing estimators in terms of accuracy and achieves a good SNR-insensitive performancein a wide range of velocities and SNRs. Copyright © 2013 John Wiley & Sons, Ltd.

Received 7 May 2012; Revised 29 October 2012; Accepted 5 January 2013

KEY WORDS: Doppler shift; iterative process; level crossing rate; high vehicle speed; mobile communi-cations.

1. INTRODUCTION

Recently, with the development of mobile communications, signal reception in vehicular platform

has become a heated topic and attracts much attention [1–4], which together with the intelligent

transportation system (ITS), converges two important fields of electronics remote sensing and

mobile communication, and has been proposed to provide the transportation safety guarantee and

vehicular data communication [4]. Generally, vehicular communication includes vehicle-to-vehicle

and vehicle-to-infrastructure (V2I) communications, and this paper focuses on the signal processing

in V2I systems, such as the cellular mobile communication system.

Generally, the speed estimation can be done by the inductive loop detector (ILD) [5] or the

Doppler shift (f d) [6]. However, as for V2I systems, the ILD requires additional cost of specialloop sensor systems, and therefore, the Doppler shift-based estimator is preferred, where the latter

exploits the fact that the fading rate of a channel depends on its maximum Doppler shift, and the

Doppler shift is related to the velocity of the mobile terminal (MT) [7–9]. This important rela-

tionship always comes into existence, irrespective of the background noise intensity. Moreover, the

rapid fading or fast mobile speed is a central issue in mobile communications [3, 7, 10, 11], which

usually causes negative influence [12–14], and as a branch of mobile communications, the vehicular

communication also suffers from the fast fading channel caused by high vehicle speeds [1,3, 4].

*Correspondence to: Jingyu Hua, College of Information Engineering, Zhejiang University of Technology, Hangzhou,310032, China.

†E-mail: [email protected]

Copyright © 2013 John Wiley & Sons, Ltd.


2/11

J. Y. HUA ET AL.

According to the requirements of 3G and 4G systems, the vehicle speed may span from 0 to

500 km/h. Hence, an effective estimation of the Doppler shift or the MT velocity is of great impor-

tance in vehicular mobile communications and will produce a good power control performance [15],

a sound handoff performance [16], an effective dynamic channel parameter estimation [17–20],

an outstanding adaptive transmission performance [21, 22], a good position estimation [23], and a

good target tracking ability [2]. These requirements confirm the necessity of an accurate Doppler

shift estimation in modern vehicular communication systems. To estimate the Doppler shift andthen the mobile speed, scientists had studied some methods in [16, 17, 24–28] and their reference

therein. Unfortunately, all of the aforementioned Doppler shift estimators, except for that in [27],

did not explicitly give consideration to the noise effect in Doppler shift estimation; thus, they expe-

rienced significant performance degradations as a result of the influence of additive white Gaussian

noise (AWGN). In addition, the AWGN had been taken into account for the carrier frequency off-

set estimation [29]; however, the carrier frequency offset is very different from the Doppler shift

in the mechanism as well as the influence [30] and therefore is beyond the scope of this paper.

Accordingly, we only focus on the Doppler shift estimation in our study.

In [27], Hua et al. proposed an iterative autocorrelation function (ACF) method to reduce the

effects of AWGN, but its intrinsic series approximation errors make it only effective for small

Doppler shift ranges. On the other hand, the ITS may require both high estimation accuracies and

large estimation ranges of Doppler shift; for example, the overspeed alarm is not operable in the

infrastructure center without accurate Doppler shift estimations. In fact, the crossing rate-based

Doppler shift estimator produces no series approximation errors and is robust to the propagation

environment [31]. Here, the level crossing rate (LCR) of a random process (RP) is defined as

the cross number per second when the RP envelope level down-crosses a certain threshold level.

Accordingly, this paper proposes a Doppler shift estimator capable of reducing noise influence

effectively without reducing the estimation range, while conventional methods failed to resolve the

two problems at the same time. Specifically, we first give an analysis of the signal-to-noise ratio

(SNR)-insensitive conditions for the LCR-based method and then implement these conditions with

a simple iterative process. To our knowledge, it is the first time that the iterative LCR method is

proposed to confront the two problems mentioned earlier. Moreover, we prove the convergency of

the proposed estimator by analytical derivation. We also verify our algorithm with Monte Carlo sim-

ulation, where accurate and SNR-insensitive estimates are obtained in a wide range of velocities and

SNRs, viz., within the error tolerance limit (5%). Because the concerned speed in this paper rangesfrom 30 km/h 480 km/h, it must be in the vehicular environment, and the conventional studydid not concern such a high-speed case as 480 km/h. In addition, the proposed estimator substan-

tially outperforms the traditional Doppler-based estimator [6] and maintains at least the comparable

performance as the ILD method, which makes the proposed Doppler shift estimator of significant

novelty and suitable for real-world applications.

The remainder of this paper is organized as follows. Section 2 presents the signal model under

consideration together with the conventional LCR-based Doppler shift estimator. And then, the esti-

mation bias, the SNR-insensitive condition, and the iterative process are presented in Section 3.

Finally, numerical results are analyzed in Section 4, and conclusions are summarized in Section 5.

2. SIGNAL MODEL

In high-speed vehicular environments, the transmitted signal from the base station is corrupted by

the fading channel. Let us suppose that a band-limited pilot signal is transmitted over fading chan-

nels, and the distinguishable multi-path fading channels are wide-sense stationary and mutually

uncorrelated scattering processes. After synchronously matching the pilot signal, the expression of

channel estimates can be written according to [32]

Ocl

.n/D cl.n/C ´.n/ (1)

where Ocl.n/, cl.n/, and ´.n/ represent the channel estimates, the actual channels, and AWGN (withvariance 2´ ), respectively. n is the discrete time index, and l denotes the path index. Here, the actual

Copyright © 2013 John Wiley & Sons, Ltd. Int. J. Commun. Syst. (2013)

DOI: 10.1002/dac


3/11

ACCURATE ESTIMATION OF DOPPLER SHIFT

channels (cl.n/) are modeled as zero-mean complex Gaussian RPs with variance 2l . For notational

brevity and without loss of generality, the strongest path is assumed to be the first path, denoted

as Oc.n/.Generally, LCR of the envelope of a RP can be defined as the cross number per second when the

envelope level down-crosses the threshold level (Ath). Moreover, precise expressions of LCR overRayleigh channels have been derived in [33]:

N ˛LCR Ds

b2

b0˛e˛

2

(2)

bn D .2/n 2l

Z f df d

f np f nd f n

df C .2/n 2´Z 1=2T s1=2T s

f ndf (3)

where ˛ , f d, and T s denote the ratio of the threshold level to the envelope root mean square level,the actual Doppler shift, and the pilot symbol interval, respectively. Generally, we choose ˛ D 1.Then, the simplified LCR expression and the LCR-based Doppler shift estimator (assuming zero

AWGN) can be derived as [17, 33]

N ˛LCR Dp

2˛e˛2

f d (4a)

Of d0 D ep

2ON 1LCR (4b)

In Equation (4b), ˛ is fixed as 1. Note that different alphas will introduce different N ˛LCRs andtherefore different expressions of Equation (4b), but the final Doppler shift estimation will not be

affected by the choice of ˛ because ˛ must be known to the scientists in deriving the estimators.

Furthermore, we store K channel estimates of the l th path to estimate LCR ON 1LCR

, so long as

the time length (K T s) is larger than the fading period. Although Equation (4b) does not exploit

any series approximation like the ACF method (i.e., its estimation range is much larger than that of the latter), it is derived under the assumption of infinite SNR. Unfortunately, AWGN is inevitable

in real-world scenarios and may cause some estimation biases. This kind of bias will be described

clearly in the next section.

3. ITERATIVE DOPPLER SHIFT ESTIMATOR

In [28], Hua and You proposed to use a decimator to refine the Doppler shift estimation, and in [27],

Hua et al. suggested to use adaptive autocorrelation lags to obtain the Doppler shift estimation.

These literature presented a rough discussion about the iterative Doppler shift estimator; however,

their estimation ranges are small.

In our investigation, we will further study the iterative estimation technique in LCR calculation.

First we will show a fairly high estimation bias in Equation (4b), followed by an effective iterativemethod to remove this bias. The algorithm substantially utilizes the useful information hidden in

the estimation bias of Doppler shift and therefore outperforms conventional algorithms. Moreover,

the LCR calculation does not require series approximation, which effectively extends the Doppler

shift estimation range compared with the correlation-based method. These two advantages make up

of the novelties in our study.

3.1. Bias analysis of the estimator defined in Equation (4b)

According to Equations (2) and (4b), ON LCR is functional on AWGN, so is Of d0. Hence, the estimatorof Equation (4b) is unbiased only in noise-free scenarios. To account for this influence, first, we


DOI: 10.1002/dac


4/11

J. Y. HUA ET AL.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

5

10

15

20

250dB5dB10dB15dB

η

Normalized Doppler shift (fdT

s)

Figure 1. Estimation bias in noisy case.

assume that K is large enough

i.e., ON 1LCR N

1LCR

; then, we define the ratio of Doppler shift

estimation in noisy scenarios to that in noise-free scenarios as [33]

Dep 2

ON 1LCR.noisy/ep 2

ON 1LCR.noise-free/s

b2

b0

bs2

bs0(5)

where bs2 and bs0 are calculated according to Equation (3) with 2´ D 0, while b2 and b0 are

computed with 2´ ¤ 0. After simple calculation and substitution, we have

Ds

1C .1=2f dT s/2 6

6. sC 1/ (6)

where s D 2l = 2´ denotes the symbol signal to noise ratio. By Equation (6), we can investigatethe estimation bias caused by AWGN in theory. Accordingly, Figure 1 is presented through the

numerical computation of Equation (6).

From Figure 1, we clearly find that the estimation bias is a function of SNR and f dT s. It isobvious that a higher SNR and a larger f dT s lead to a smaller bias. These results cast light on theimprovement for the original LCR-based Doppler shift estimator.

3.2. SNR-insensitive conditions

From Equation (6) and Figure 1, when the actual Doppler shift (f d) is given, different sampleintervals (different T s’s) lead to different doppler shift estimation biases, when T sf d rises to a par-ticular value, the curves tend to be superposed, which means that the AWGN influence tend to be

same for all the curves at this time. This is hidden and useful information because it supplies us withadditional information of f d so long as more than one sampling rates are taken into consideration;that is, if we can choose an appropriate sample interval, the estimation error will be trivial.

Generally, must be larger than 1 as shown in Figure 1, and if the LCR-based Doppler shiftestimator approach the performance at noise-free scenarios, it must have D 1 and lead to a certainsample interval. To find this sample interval, we assume D 1 and substitute it into Equation (6);then, we have the following equation:

1 D Ds

1C Œ1=.2f dT s/2 6

6. sC 1/ (7)


DOI: 10.1002/dac


5/11


Solving Equation (7), we have the SNR-insensitive condition:

p 24D 1

f dT s(8)

Because the sampling rate usually is invariant, a decimator analogous to [28] can be employed to

produce different sample intervals; then, Equation (8) should be rewritten as

p 24D 1

Mf dT s(9)

where M denotes the decimating factor. In fact, the method in [28] can be considered as a specifiedexample of our study, and this paper further finds which decimating factor is optimal and presents

an effective iterative process to realize the optimal decimating factor.

3.3. Iterative process for refined estimation

To realize the optimal decimating factor, an iterative process is proposed and produces great per-

formance improvements compared with the conventional estimators. Note both this process and the

SNR insensitive condition are the main novelties of this paper.

After denoting Of d.i / and M.i/ as the Doppler shift estimation and the computed decimatingfactor at the i th iteration, we can illustrate the proposed iterative process as follows:

(1) At initialization, set a frequency difference threshold th and let {M.1/ D 1, Of d.0/ D 0, anditeration counter I c D 1}.

(2) Estimate Doppler shift for current iteration and get Of d.I c/ by Equation (4).(3) Compute M.I c/D

j 1=

p 24

Of d.I c/T s

k, where bc denotes the floor function.

(4) If M .I c/ D M.I c 1/ or j Of d.I c 1/ Of d.I c/j < th, exit the iterative process; otherwise,I c D I cC 1, go to step 2).

When the iterative process is finished, the final Doppler shift estimation is expressed as Of d.

3.4. Convergency analysis

After defining n as the bias ratio at the nth iteration, we can express the following recurve equation:

n Ds

1C 2n1 11C s

, n > 1 (10)

with the initial condition 1 Dq

1C .1=2f dT s/266. sC1/ > 1.

Solving Equation (10), the bias ratio at the N Ith iteration can be rewritten as

2n 12n1 1 D

1

1C s )N I

Ds 1C 21 1

.1C s/N I1 (11)

Looking at Equation (11), we can draw some beneficial conclusions in the following:

N I > 1 for any iteration number N I > 1. For a given f dT s, N I monotically decreases with N I increasing. If N I!1, N I ! 1. For a given N I , the iteration number varies with the actual f dT s. For a given N I , different SNRs result in different iteration numbers. With the knowledge of SNR, f dT s, and th, the iteration number can be calculated by

Equation (11) because N I Dth=f d C 1.


DOI: 10.1002/dac


6/11

J. Y. HUA ET AL.

So far, the convergency of the proposed iterative process is approved, and the improvement of its

performance is controlled by N I as shown in Equation (11). Hence, N I is an important parameter tomake a trade-off between complexity and performance, which is related to the th. In our research,we found that th D 10 Hz is an appropriate choice.

3.5. Further modification

Generally, the floor function exploited in the iterative process will cause some deviations from the

ideal estimation. Hence, an important task is to ensure the precision of Of d; here, we propose a simplepolynomial fitting to overcome this problem. Given Of d’s, we can derive the following process: For a certain SNR range (5–15 dB), set up the polynomial error expression by using Monte

Carlo simulations (1000 times in our study)

Doppler D p3 Of 3d C p2 Of 2d C p1 Of d C p0 (12)

where p3 p0 is invariant for different SNRs; thus, a small bias will be remained after fitting.However, we find in extensive simulations that this kind of performance loss will not cause

large bias.

Calculate the improved estimation byNf d D .1CDoppler/ Of d (13)

Using the aforementioned process, we can further improve the estimation for Doppler shift in

most scenarios. Note that both Equations (12) and (13) are determined by offline simulations.

4. SIMULATIONS AND ANALYSIS

This section presents some simulation results in high-speed vehicular environments in terms of mean

square error (MSE) and accuracy. Then, we compare the performance of the proposed method with

those of the logarithmic envelope (LE) method [16], the ACF method [24], the iterative ACF method

[27], the LCR method [17], and the phase method [28].The detailed simulation parameters are shown in Table I, where the total simulation duration is

1000 slots and the international telecommunications union (ITU) R. M.1225 Veh. B channel model

with six independent paths is used at the carrier of 2.11 GHz. Meanwhile, each slot consists of

1056 bits (bit rate 1.2288 Mbits/s). In each slot, five pilot symbols, each of 32 bits, are time multi-

plexed with four data blocks and used for moving average channel estimation. Note that the M.1225

Veh. B channel model presented in ITU standards is a widely used vehicular communication channel

for 3G/B3G systems; thus, our simulations reflect the case in vehicular networks and therefore is

useful for V2I ITS systems.

Figure 2 illustrates the accuracy of some existing methods as well as the proposed method with

polynomial fitting, where higher SNRs lead to smaller biases. This is consistent with the discussion

presented in Section 3. From Figure 2, we can clearly present that the LE method in [16] yields an

obvious bias and a distinguishable error floor in all scenarios. Furthermore, we demonstrate that the

proposed method yields similar performance as the iterative ACF method and outperforms the other

four methods. Additionally, the biases of the iterative-based method are small even if the SNR is

Table I. Simulation parameters.

Slot length 1056 bits Channel model ITU M.1225 Veh. BBit rate 1.2288 Mbit/s Simulation length 1000 slotsPilot length 32 bits Path number 6Carrier 2.11 GHz Data block length 224 bitsth 10 Hz Modulation QPSK


DOI: 10.1002/dac


7/11


50 100 150 200 250 300 350 400 450 5000

100

200

300

400

500

600

700

Actual Doppler shift (Hz)

E s t i m a t e d

D o p p l e r s h i f t ( H z )

Ideal estimationLE methodLCR methodACF methodPhase methodIterative ACF methodProposed method with fitting

(a) SNR=0dB

50 100 150 200 250 300 350 400 450 5000

100

200

300

400

500

600

700


E s t i m a t e d

D o p p l e r s h i f t ( H z )


(b) SNR=5dB

50 100 150 200 250 300 350 400 450 5000

100

200

300

400

500

600

700


E s t i m a t e d D o p p l e r s h i f t ( H z )


(c) SNR=10dB

50 100 150 200 250 300 350 400 450 5000

100

200

300

400

500

600


E s t i m a t e d D o p p l e r s h i f t ( H z )


(d) SNR=15dB

Figure 2. Doppler shift estimation performance comparison II: the logarithmic envelope (LE) method [16],the autocorrelation function (ACF) method [24], the level crossing rate (LCR) method [17], the phase method

[28], the iterative ACF method [27], and the proposed method.

0 dB, whereas the biases of the non-iterative methods are large in low SNR and at low speed. On theother hand, although the proposed method does not outperforms the iterative method in Figure 2, in

real-world scenarios, the former is superior to the latter because of the larger estimation range of the

normalized Doppler shift.

To show the large estimation range of the proposed method, high-speed simulations are presented

in Figure 3, where the highest speed approaches 480 km/h (Doppler shift 938 Hz), and have not been

taken into consideration in conventional investigations. From Figure 3, we explicitly see that the pro-

posed method produce precise estimates so long as SNR is larger than 5 dB, which is the working

SNR range for common communication systems. When SNR is 0 dB, the proposed method pro-

duces obvious but acceptable bias for high speeds. Moreover, Ki [5] presented that the ITS system

requires the estimation error less than 5%, and Jakus and Coe [6] produced the error about 3.3% at

90 km/h and 12.5% at 60 km/h. Obviously, if SNR > 5 dB, the proposed method performs better

than that in [6] and substantially fulfils the requirements in [5]. In fact, at this time, the proposed

method produced the error about 2.73% at 90 km/h and 1.7% 60 km/h}, and the maximum error is

about 4.5%. Additionally, although not shown in Figure 3 for the sake of legible figure, we must

point out that compared with the proposed method, other five methods, including the iterative ACF

method, will produce much larger biases at high speeds. Hence, only the proposed method is reliable

and suitable for Doppler shift estimation in high-speed scenarios.

Figure 4 compares the MSE of the tested methods averaged along speed dimension. Because the

conventional LCR method, the LE method, the ACF method, and the phase method cannot eliminate


DOI: 10.1002/dac


8/11

J. Y. HUA ET AL.

0 100 200 300 400 500 600 700 800 900 10000

200

400

600

800

1000

1200


E s t i m a t e d D o p p

l e r s h i f t ( H z )

Ideal estimationSNR 0dBSNR 5dBSNR 10dBSNR 15dB

Figure 3. The proposed estimator in high-speed scenarios. SNR, signal-to-noise ratio.

0 5 10 1510

−4

10

−3

10−2

10−1

100

101

SNR (dB)

M S E

LE methodACF methodIterative ACF methodPhase methodLCR methodProposed method without fittingProposed method with fitting

Figure 4. The mean square error (MSE) performance comparison of Doppler shift estimators. LE,logarithmic envelope; ACF, autocorrelation function; LCR, level crossing rate; SNR, signal-to-noise ratio.

the effect of additive noise, they experience severe performance degradation. On the other hand, the

proposed method maintains low MSE and obtains at least one order of magnitude gain when SNR

falls into the range of 0–15 dB. Moreover, because the iterative estimation is larger and lesser than

the actual Doppler shift at an SNR of 0 dB and other higher SNRs, the fitting operation causes a

little MSE increase at an SNR of 0 dB. Fortunately, at working SNR ranges, the proposed estimator

with polynomial fitting can evidently observe performance improvements compared with the one

without fitting. In addition, compared with the iterative ACF method, the proposed estimator with

polynomial fitting produces similar and smoother MSEs at working SNR ranges while having larger

Doppler shift estimation ranges. Accordingly, we can conclude that the iterative technique plus poly-

nomial fitting removes the AWGN influence effectively and yields a great performance improvement

in a wide range of SNRs and velocities, which must be beneficial for the ITS application requiring

accurate MT speed estimations.


DOI: 10.1002/dac


9/11


5. CONCLUSION

After investigating the noise corruptions, we propose a novel estimation algorithm for Doppler

shift in vehicular communications by applying the iterative technique and polynomial fitting to

the conventional LCR method, where we study the SNR-insensitive condition and realize it through

an iterative process. With the help of the iteration hypostasis, we also analyze the convergency.

Both the simulation and the analysis show a good and consistent performance in a wide rangeof velocities and SNRs, which will benefit many communication applications, such as those in

3G and multi-input multi-output orthogonal frequency division multiplex (MIMO-OFDM) systems

[17–19].

ACKNOWLEDGEMENTS

This paper is sponsored by the key project of Chinese ministry of education (grant no. 210087), Zhejiangprovincial NSF (grant no. LY12E07005), and the open research fund of National Mobile CommunicationsResearch Laboratory, Southeast University (grant no. 2010D06).

REFERENCES

1. Al-Masud A, Mondal M, Ahmed KM. Vehicular communication system for vehicle safety using RFID. Proceedings

of the IEEE MICC’09, Kuala Lumpur, Malaysia, 2009; 697–702.

2. Bera R, Mondal D, Sil S, Dhar S, Sur S, Bhaskar D, Sarkar SK, Kandar D. Vehicular communication and safety in

realization of intelligent transport system. Proceedings of the CODEC’09, Kolkata, India, 2009; 1–4.

3. Tan I, Tang W, Laberteaux K, Bahai A. Measurement and analysis of wireless channel impairments in DSRC

vehicular communications. Proceedings of the IEEE ICC’08, Beijing, China, 2008; 4882–4888.

4. Kiokes G, Amditis A, Uzunoglu NK. Simulation-based performance analysis and improvement of orthogonal

frequency division multiplexing—802.11p system for vehicular communications. IET Intelligent Transport Systems

2009; 3(4):429–436.

5. Ki YK. Speed-measurement model utilising embedded triple-loop sensors. IET Intelligent Transport Systems 2011;

5(1):31–37.

6. Jakus K, Coe DS. Speed measurement through analysis of the Doppler effect in vehicular noise. IEEE Transactions

on Vehicular Technology 1975; VT-24(3):33–38.

7. Wang JZ, Zhu HL, Gomes NJ. Distributed antenna systems for mobile communications in high speed trains. IEEE

Journal on Selected Areas in Communications 2012; 30(4):675–683.

8. Mousa A, Mahmoud H. Reducing ICI effect in OFDM system using low-complexity Kalman filter based on

comb-type pilots arrangement. International Journal of Communication Systems 2011; 24(1):53–61.

9. Hafez H, Fahmy YA, Khairy MM. LTE and WiMAX: performance and complexity comparison for possible channel

estimation techniques. International Journal of Communication Systems Online first, 2011. DOI: 10.1002/dac.1370.

10. Zhou YQ, Wang J, Sawahashi M. Downlink transmission of broadband OFCDM systems—part II: effect of Doppler

shift. IEEE Transactions on Communications 2006; 54(6):1097–1108.

11. Zhang ZY, Cheng P, Zhou XW, Qiu PL. System synchronization and channel estimation analysis for IEEE 802.16e

OFDMA downlink system. International Journal of Communication Systems 2009; 22(4):375–398.

12. Karami E. Performance analysis of decision directed maximum likelihood MIMO channel tracking algorithm.

International Journal of Communication Systems Online first, 2012. DOI: 10.1002/dac.2329.

13. Neelakantan PC, Babu AV. Computation of minimum transmit power for network connectivity in vehicular ad hoc

networks formed by vehicles with random communication range. International Journal of Communication Systems

Online first, 2012. DOI: 10.1002/dac.2390.

14. Ozen A. A novel variable step size adjustment method based on channel output autocorrelation for the LMS training

algorithm. International Journal of Communication Systems 2011; 24(7):938–949.

15. Monk AM, Miltein LB. Open-loop power control error in a land mobile satellite system. IEEE Journal on Selected

Areas in Communications 1995; 13(2):205–212.

16. Sampath A, Holtzman J. Estimation of maximum Doppler frequency for handoff decisions. Proceedings of the IEEE

VTC’93, Secaucus, New Jersey, 1993; 859–862.

17. Ma Z, Yan Y, Zhao C, You X. An improved channel estimation algorithm based on estimating level crossing rate for

CDMA receiver. Chinese Journal of Electronics 2003; 12(2):235–238.

18. Aboutorab N, Hardjawana W, Vucetic B. A new iterative Doppler-assisted channel estimation joint with parallel

ICI cancellation for high-mobility MIMO-OFDM systems. IEEE Transactions on Vehicular Technology 2012;

61(4):1577–1589.

19. Gao J, Zhu X, Wu Y. Kalman smoothing-based adaptive frequency-domain channel estimation for uplink

multiple-input multiple-output orthogonal frequency division multiple access systems. IET Communications 2011;

5(2):199–208.


DOI: 10.1002/dac


10/11

J. Y. HUA ET AL.

20. Hua JY, Meng LM, Xu ZJ, Li G. An adaptive signal-to-noise ratio estimator in mobile communication channels.

Digital Signal Processing 2010; 30(3):692–698.

21. Heo J, Wang Y, Chang K. A novel two-step channel-prediction technique for supporting adaptive transmission in

OFDM/FDD system. IEEE Transactions on Vehicular Technology 2008; 57(1):188–193.

22. He J, Xu C, Li L. Adaptive subcarrier bandwidth and power in OFDM-based cognitive radio systems for high

mobility applications. International Journal of Communication Systems Online first, 2012. DOI: 10.1002/dac.2424.

23. Hammes U, Zoubir AM. Robust Mobile Terminal tracking in NLOS environments based on data association. IEEE

Transactions on Signal Processing 2010; 58(1):5872–5882.24. Xiao C, Mann K, Olivier J. Mobile speed estimation for TDMA based hierarchical cellular systems. IEEE

Transactions on Vehicular Technology 2001; 50(4):981–991.

25. Dogandzic A, Zhang B. Estimating Jakes’ Doppler power spectrum parameters using the Whittle approximation.

IEEE Transactions on Signal Processing 2005; 53(3):987–1005.

26. Hua J, Sheng B, You X. The phase probability distribution of general Clarke model and its application in Doppler

shift estimation. IEEE Antennas and Wireless Propagation Letters 2005; 4(4):373–377.

27. Hua J, Meng L, Li G, Wang D, You X. An accurate scheme for channel parameter estimation in mobile propagations.

IEICE Transactions on Electronics 2009; E92C(1):116–120.

28. Hua J, You X. A schemefor theDopplershiftestimation despite thepower control in mobile communication Systems.

Proceedings of the IEEE VTC’04 Spring, Milan, Italy, 2004; 284–288.

29. van de Beek JJ, Sandell M, Borjesson PO. ML estimation of time and frequency offset in OFDM systems. IEEE

Transactions on Signal Processing 1997; 45(7):1800–1805.

30. Hua JY, Xu ZJ, Meng LM, Li G. A spectrum-efficient integer frequency offset estimator of mobile OFDM system

in double selective channels. International Journal of Communication Systems 2010; 23(8):1041–1056.

31. Tepedelenlioglu C, Giannakis GB. On velocity estimation and correlation properties of narrow-band mobilecommunication channels. IEEE Transactions on Vehicular Technology 2001; 50(4):1039–1052.

32. Gao X, Jiang B, You X, Pan Z, Xue YS, Schulz E. Efficient channel estimation for MIMO single-carrier block

transmission with dual cyclic timeslot structure. IEEE Transactions on Communications 2007; 55(11):2210–2223.

33. Stuber GL. Principle of Mobil Communications. Kluwer Academic: Boston, MA, 2001.

AUTHORS’ BIOGRAPHIES

J. Y. Hua was born in Zhejiang province, China in 1978. He received his PhD degree of Radio Engineering from Southeast university in 2006. Now he is an associate professor of Zhejiang university of technology. His research interests lie in the area of channel parameterestimation and multicarrier signal processing in mobile communication.

D. H. Yuan was born in Jiangsu province, China in 1987. She received his B.Sc degree of EEfrom Huzhou normal university in 2010. Now she is pursuing her MSc degree in Zhejianguniversity of technology. Her research interests lie in the area of parameter estimation andwireless channel modeling.


DOI: 10.1002/dac


11/11


G. Li was born in Zhejiang province, China in 1983. He received his PhD degree of EE fromNanjing University of science and technology in 2011. Now he is an assistant professor of Zhejiang university of technology. His research interests lie in the area of signal propagationand optical communication.

L. M. Meng was born in Zhejiang province, China in 1963. She received his PhD degreeof EE from Zhejiang university in 2003. Now she is a full professor of Zhejiang universityof technology. Her research interests lie in the area of WLAN and detection technique inmobile communication.


DOI: 10.1002/dac

Accurate Estimation of Doppler Shift in Mobile Communications With High Vehicle Speed

Documents

Transcript of Accurate Estimation of Doppler Shift in Mobile Communications With High Vehicle Speed