A NOVEL PILOT PATTERN FOR MINIMIZING …ijesr.org/admin/upload_journal/journal_M.Archana...

ISSN 2277-2685 IJESR/July 2015/ Vol-5/Issue-7/887-895

M. Archana et. al./ International Journal of Engineering & Science Research

*Corresponding Author www.ijesr.org 887

A NOVEL PILOT PATTERN FOR MINIMIZING SYNCHRONIZATION ERRORS

IN CARRIER AGGREGATION SYSTEMS

M. Archana*1, Ch.Venkat Ramaiah

2

1Dept of Electronics and communications, SRM University, Chennai, India.

2Asst. Prof, Dept. of ECE, R.M.D. Engineering College, Kavaraipettai, Tamil Nadu, India.

ABSTRACT

Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier modulation scheme, which is used as

the air interface technique for LTE (Long Term Evolution) standard. Furthermore Carrier Aggregation (CA) is

defined by 3GPP (3rd Generation Partnership Project) to support wide-bandwidth transmission. However OFDM

is sensitive to synchronization errors due to multipath fading, Doppler shift and oscillator instability. In this

paper, a block type pilot pattern error suppression algorithm is proposed to reduce the synchronization errors in

carrier aggregation systems. A block type pilot pattern used here is weighted CAZAC (Constant Amplitude Zero

Auto-Correlation) sequence which is well exploited in LTE Advanced standard. Simulation results show that, it

can significantly improve the system performance and has a smooth performance when synchronization errors

vary.

Keywords: Carrier Aggregation (CA), Carrier Frequency Offset (CFO), Orthogonal frequency Division

Multiplexing (OFDM), Sampling Time Offset (STO),weighted Constant Amplitude Zero Auto-Correlation

(CAZAC).

1. INTRODUCTION

A multicarrier communication system with orthogonal subcarriers is called Orthogonal Frequency Division

Multiplex (OFDM) system. The word “orthogonal” indicates that there is a precise mathematical relationship

between the frequencies of the carrier in the system. The basic principle of OFDM is to split a high data rate

sequence into a number of low rate sequences that are transmitted simultaneously over a number of subcarrier.

In order to support more number of users than OFDM, carrier aggregation technique is used. Carrier

Aggregation (CA) is a technique that aggregates multiple component bands into an overall wider bandwidth, is

proposed to support wide bandwidth transmission in LTE- Advanced standard.CA also provides flexibility in

spectrum assignment. It is of three types, Inter-band non-continuous, intra-band continuous and intra-band non

continuous.

However, OFDM system is sensitive to synchronization errors, such as Carrier Frequency Offset (CFO) and

Sampling Time Offset (STO). Inaccurate synchronization will break the orthogonality among subcarriers and

introduce ICI (Inter Carrier Interference) into received signal, which will lead to serious performance

degradation. Furthermore, in non-continuous CA scenario, signals transmitted on different component bands

always experience different synchronization errors, which make synchronization much more difficult. There

have been many previous works on synchronization problem. In [4], a timing synchronization algorithm was

discussed but CFO compensation method wasn’t considered. CFO compensation, methods are proposed in [5-

14]. These frequency synchronization methods can be classified as two groups. The first is called feedback

method, which will increase the transmission overhead and possibly cause outdated estimation in time-varying

scenario. An alternative is to achieve synchronization via signal processing at the receiver without the help of a

control channel. In [8-10], SIC (Successive Interference Cancellation) as well as PIC (Parallel Interference

Cancellation) methods were raised. In these methods, the received signals are classified as reliable group and

unreliable group. The reliable signals are directly detected while the unreliable signals are detected after the

cancellation of the MAI (Multi Access Interference) effects due to the reliable signals. Inverse interference

matrix method was discussed in [11-13]. Unfortunately, the methods in [8-11] need perfect multiple CFO’s


Copyright © 2015 Published by IJESR. All rights reserved 888

estimation, which is impossible in practical system. And our previous algorithms proposed in [12-13] didn’t

consider timing offset. Furthermore, all the methods mentioned above are applied non continuous –CA system,

and to our best knowledge, there are few papers about the synchronization problems in CA system. In this

project work, mainly focus on the synchronization problems in non-continuous CA system and proposed a block

type pilot based synchronization errors suppression algorithm. First use the correlation of the pilot block to

estimate the STO. Interference estimation is followed after STO estimation. In this stage, pilot block are

exploited to estimate the ICI components directly. Then suppress the components by inverse matrix method.

Since block type pilot is a common pilot pattern in wireless communications, this method can be easily extended

to the other systems.

2. SYSTEM MODEL

Fig.1 shows the simplified block diagram of the orthogonal frequency division multiplexing (OFDM) based

non-continuous carrier aggregation (non-continuous CA) system. Consider the case that is without loss of

generality, we suppose K component bands are exploited in this communication system. The carrier frequency

of the kth component band is denoted as fck and the subcarrier amount of the kth component band is Nk.

Here we assume the vector dk=[dk

0,dk

1,..............dkNk-1]

T (k=1, 2, 3 …, K) denotes the transmitted data on the k

th

component band. Then the output of the Nk-IFFT is

∑−

=

=1

0

/2/1)(

k

k

N

i

Ninji

kk

k edNnsπ (1)

(a) Transmitter

(b) Receiver

Fig. 1: Block diagram of OFDM based non-continuous CA system (a) Transmitter (b) Receiver

The base band signal will be modulated to the transmission band with the carrier frequency fck which is

generated by the transmitter frequency synchronizer at the up-converter. Here we consider each signal



experiences a frequency selective fading channel with the time domain impulse response hk(n) (k=1, 2, 3 …K).

Then the received signal is the superposition of signals from all active component bands and can be written as

∑=

+⊗=K

k

kktfjk tvthetstrkc

1

2)()()).(()(

π (2)

Where ⊗ denotes convolution and Vk (t) is the AWGN on the k

th component band. To extract the transmitted

signal rk(t) on the k

th component band, the generated signal will pass through a low-pass filter and cancel the

signals from the other component bands. Through Nk-FFT processing, the output of the lth subcarrier on the kth

component band is

Ylk=dl

kHl

k (3)

Where Hlk

denotes the frequency domain channel response of the kth

component band.

3. INFLUENCE OF SYNCHRONIZATION ERRORS

To start the influence of inaccurate synchronization, multiple CFOs and STOs will be introduced into the

received signals. CFO and STO of the kth

component band are denoted as ∆fek

and ∆tek

respectively. After

transmitted through fading channels, the CFOs and channel fading corrupted time domain signal can be written

as

ke

ke

kc

tnt

kK

k

ktffjk tvthetstr∆+

=

∆+ +⊗=∑ )()()).(()(1

)(2π (4)

For non-continuous CA, different component bands are usually separated by sufficient bandwidth, so the

interference between them can be negligible. The STO corrupted received signal can be written as

kes

ke

tnTt

kktfjkk tvthetsnr∆+=

∆ +⊗= |)()(]).([)(2π

)(/1/)(2/2

1

0

k

kNenijNnijk

i

N

i

k

ik nveeHdN kkkkkk

k

δδεδππ ++= ++−

−∑

∑−

=

++ ++=1

0

/)(2)(/1

k

kkkk

N

i

k

kNninjk

i

k

i

k

ik nvepHdN δδεεπ

(5)

Where Ts=T/Nk is the sampling period, T denotes the symbol period, εk and δk denote the normalized CFO and

STO respectively as, after FFT,

∑∑−

=

−

=

−++ +=1

0

1

0

/2/)(2/1

k k

kkkkk

N

n

N

i

k

l

NnljNnnijk

i

k

i

k

ik

k

l VeepHdNYπδεεπ

∑ ∑−

=

−

=

+− +=1

0

1

0

/2])[(2/1

k k

kkkk

N

n

N

i

k

l

Njlinjk

i

k

i

k

ik VeepHdNδπεεπ

∑−

≠=

− ++=1

0

0

kN

lii

k

l

k

li

k

i

k

i

k

i

kk

l

k

l

k

l VQpHdQpHd

(6)

Where �� = ��∑ �� denotes the interference factor and Vkl denotes AWGN. Obviously, we

can obtain the matrix expressed frequency domain signal vector according to the above equation

k

k

v

K

M

K

M

k

v RPQ VY += (7)

Where �� = [�� , � �……… . �#�� ]T (Rl

k=dl

kHl

k) denotes the received signal vector distorted by channel

fading, PMk=diag(p0

k,p1

k,......pNk-1

k) denotes the phase rotation matrix caused by STO and



=

−−−−

−

kk

N

k

N

k

Nkk

k

M

QQQ

QQQ

Q

kk

k

0)2()1(

110

........

.

.

.

.

........

.

.

(8)

denotes the ICI matrix caused by CFO. The diagonal components in k QkM are the CPE (Common Phase Error)

components and the other components denote the ICI from the corresponding subcarriers.

4. SYNCHRONIZATION ERRORS SUPPRESSION ALGORITHM

In this section, we will discuss the proposed block type pilot based synchronization errors suppression

algorithm. The pilot pattern used here is the weighted CAZAC sequence, which is one of the strongest

candidates as pilot pattern in OFDM system and has been exploited by LTE-Advanced standard. Let L to be any

positive integer larger than one and M to be any number, which is relatively prime with L. Then an example of

weighted CAZAC sequence is given as

−==

−==

+

++

LisevenLnec

LisoddLnenc

nnLMj

nM

nnnLMj

M

,1................1,0,

,1.,.........1,0,)(

)2/(/2

)(

)2/)1((/2

2π

π (9)

and the circular auto-correlation of weighted CAZAC sequence is

∑−

=

≠

==+

1

0

mod0,0

0,)()(

L

n

LMM

Lncnc

τ

ττ (10)

(a) STO Estimation Algorithm

In this part, we discuss the proposed frequency domain STO estimation algorithm to obtain the STO between

the practical sampling time and optimum sampling time. To implement this algorithm, special weighted

CAZAC sequence should be exploited, in which pilots spaced by half subcarriers amount should satisfy the

following requirement

k

Nl

k

l kPP 2/+±= (11)

Where Plk denotes the pilot transmitted on the lth subcarrier of the kth component band. In OFDM system, since

the amount of subcarrier is always an even number, we can obtain the expression of %&�as

1..............1,0,

)2/(/2 2

−== +k

llNMjk

l NleP kπ

(12)

Together with equation (11), we can obtain

int2/1)2/2/2/1(

])2/()2/[(/)2/(/ 22

])2/()2/[(/2)2/(/2 22

=++

+++=+

±= ++++

lNM

NlNlNMllNM

ee

k

kkkK

NlNlNMjllNMj kkkk ππ

(13)

Obviously, to satisfy the above requirement, we just need to set M as an odd and Nk as a multiple of 4 (in this

scenario, M is naturally relatively prime with Nk). In OFDM system, to achieve high speed FFT/IFFT,

subcarrier amount is usually set as exponential times of 2. Thus the above requirement can be achieved by just

set M as an odd number. Therefore

−=

=

+

+

iflisevenPP

iflisoddPP

k

Nl

k

l

k

Nl

k

l

k

k

;

,

2/

2/ (14)

As illustrated in previous section, ICI has the appearance of Gaussian noise. Furthermore, according to the

interference self-cancellation algorithm, the pilot pattern used here could alleviate the interference. Therefore,

we could treat the interferences as noise and don’t consider it. In this situation, the lth

received pilots can be

expressed as



kk

l

k

l

k

l

k QpHPlP 0)( = (15)

We suppose the frequency domain channel response has already been estimated. Then the correlation can be

calculated as follows

' = ∑ (��)*�+,�-&.�� /0*�� %�(1�� &�� )

=∑ |%&�|3|��|3[4&.��/3�� &�� . (4&�)∗] =89:� ∑ |%�(1)|3|��|3�� &�� (16)

Therefore, the STO of the kth component band can be obtained from the phase of η as

;� = <=>?<@-AB(C)DE(C)/9 (17)

Since the variation range of arc tan(x) is (-π/2, π/2), the estimation range is (-0.5, 0.5), which fully covered the

available value of STO.

(b) Interference Suppression Algorithm

After STO estimation, we perform interference estimation and then suppress the inaccurate synchronization.

Instead of estimating CFOs, which is fairly difficult to get accurate results, we directly estimate the interference

components under the aid of the received pilot block. After that, the ICI matrix can be reconstructed by very

simple mapping method. Then we can suppress interferences by inverse matrix method and finally improve

system performance.

From the expression of QLk

we can obtain that

�� = 1G� H �� I��

= ��∑ 839(��.J�)/��

= QkNk-1 (18)

Therefore, the ICI matrix Q Mk can be rewritten as

�K� = L�� ⋯ ��⋮ ⋱ ⋮��3� ⋯ �� P (19) We can see that the matrix QM

k is a circulant matrix with only Nk different components. Therefore, equation (7)

can be written as

RS� = L�� ⋯ ��⋮ ⋱ ⋮��3� ⋯ �� PTK� L ��⋮�� P + V�

=L ��4��4�� ⋯ �� 4��⋮ ⋱ ⋮�� 4�� 4�� ⋯ ��3� 4��3� P L ��⋮�� P + V�

=�K� WS� + V� (20)

Where WS� = [�� …�� ]Xand RMk=L ��4��4�� ⋯ �� 4��⋮ ⋱ ⋮�� 4�� 4�� ⋯ ��3� 4��3� P (21)

M. Archana

Copyright © 2015 Published by IJESR. All rights reserved

We can treat equation (20) as an equations set and the

estimate these Nk values by solving this equations set, the

of CFO can be suppressed by multiply the inverse

been estimated and the pilots are known to the receive,

rank matrix. Thus, (RMk)

-1 can be easily constructed

easily estimated by using the following equation is as shown below:

WY�Z = WS� + (�[� )��V�After that, the interference matrix QM

k’

CFO are highly correlated during the pilot block and the foll

errors can be easily suppressed by LS (Least Square) method as

R\]^^_`\\`a� = �V� + (T[� )0Since the interferences from other component bands are ignorable, the synchronization errors of each band

could be independent and have few influences on the suppression performan

process to suppress the inaccurate synchronization on each band.

Fig. 2: Block diagram of synchronization errors suppression algorithm

Fig. 2 shows the block diagram of the suppression algorithm. The whole processing

follows:

(1)When a symbol arrives at the receiver, detect whether this symbol is a pilot symbol:

Yes, go to step (2); No, go to step (5);

(2) Do channel estimation;

(3) Do STO estimation under the aid of channel estimation;

(4) Do Q matrix estimation under the aids of channel estimation results and STO estimation results.

(5) Do inverse-matrix based suppression algorithm.

5. SIMULATION RESULTS

Fig. 3 shows the MSE (Mean Square Error) performance of the proposed STO estimation algorithm

are evaluated in AWGN and ITU Ped

reduced to 10-4

level which is a fairly small value. So this achieves smooth performance when synchronization

errors vary.

M. Archana et. al./ International Journal of Engineering & Science Resear

Copyright © 2015 Published by IJESR. All rights reserved

We can treat equation (20) as an equations set and the Nk components in QMk

as the unknown values. If we can

values by solving this equations set, the QMk

matrix can be reconstructed and the interferences

of CFO can be suppressed by multiply the inverse QMk

matrix with the received signal. Since STO has already

been estimated and the pilots are known to the receive, RMkcan be easily calculated. Obviously,

can be easily constructed Qvk

and QMk

which contains the Nk components in, can be

easily estimated by using the following equation is as shown below:

(22)

k’ can be reconstructed from the estimatedWY�Z .We assume the STO and

CFO are highly correlated during the pilot block and the following data blocks. Therefore, the synchronization

errors can be easily suppressed by LS (Least Square) method as

0[(WK�Z )0WK� ]Z ��(WY�Z )0V�Since the interferences from other component bands are ignorable, the synchronization errors of each band

could be independent and have few influences on the suppression performance. Therefore, we can use parallel

process to suppress the inaccurate synchronization on each band.

Block diagram of synchronization errors suppression algorithm

2 shows the block diagram of the suppression algorithm. The whole processing can be described as

(1)When a symbol arrives at the receiver, detect whether this symbol is a pilot symbol:

(3) Do STO estimation under the aid of channel estimation;

rix estimation under the aids of channel estimation results and STO estimation results.

matrix based suppression algorithm.

Fig. 3 shows the MSE (Mean Square Error) performance of the proposed STO estimation algorithm

are evaluated in AWGN and ITU Ped-B scenarios. This shows that MSE of the proposed algorithm can be


International Journal of Engineering & Science Research


as the unknown values. If we can

matrix can be reconstructed and the interferences

matrix with the received signal. Since STO has already

can be easily calculated. Obviously, RMk is a full

components in, can be

.We assume the STO and

owing data blocks. Therefore, the synchronization

(23)

Since the interferences from other component bands are ignorable, the synchronization errors of each band

ce. Therefore, we can use parallel

can be described as

rix estimation under the aids of channel estimation results and STO estimation results.

Fig. 3 shows the MSE (Mean Square Error) performance of the proposed STO estimation algorithm. The results

B scenarios. This shows that MSE of the proposed algorithm can be




Table 1: Simulation Parameters

Baud Rate 9600 symbols/second

Modulation Type 16 QAM

Component Carriers 4

FFT size 256

Cyclic prefix length 16

Number of Sub Carriers 512

Channels AWGN, Pedestrian-B(Ped-B)

Fig. 3: MSE performance of the proposed STO estimation algorithm

Now selecting the algorithm proposed in [11] as comparison and choosed 5, 10, 50 as the bandwidth of the

banded-matrix. Fig. 4 shows the BER performance in AWGN channel and Fig. 5 shows the BER performance

in Ped-B channel. Comparing the simulation results, we can see that due to the co-effects of CFO and STO, the

BER performance will be degrade seriously. If we used banded matrix suppression method, when the

bandwidth is small a serious error flow phenomenon occurs in high SNR region. Thus the Bit Error Rate (BER)

is reducing for the proposed system; hence it improves the performance of the system.

Fig. 4: BER performance in AWGN channel

0 2 4 6 8 10 12 14 16 18 2010

-5

10-4

10-3

10-2

10-1

100

SNR in dB

MS

E

MSE performance of proposed STO estimation algorithm

MSE of Proposed STO Estimation in Ped B Scenario

MSE of Proposed STO Estimation in AWGN Scenario

0 2 4 6 8 10 12 14 16 18 2010

-5

10-4

10-3

10-2

10-1

100

SNR in dB

BIT

ER

RO

R R

AT

E

BER performance in AWGN channel

No Sync Error

No Suppression

Proposed System

Banded Matrix Suppression Bandwidth=50





Now comparing the simulation results in Ped-B scenario in fig. 5,at SNR= 6dB in Pedestrian-B channel, Bit

Error Rate=15x10-3

for Banded Matrix suppression method when sub band bandwidth is 50KHz whereas

proposed system gives Bit Error Rate of 5x10-3. Thus the Bit Error Rate (BER) is reducing for the proposed

system; hence it improves the performance of the system

Fig. 5: BER Performance in Ped-B Channel

6. CONCLUSION

In this project work, focus on the synchronization problems in non-continuous CA OFDM systems and propose

a novel block type based synchronization errors suppression algorithm. Unlike other STO estimation

algorithms, the proposed method exploits a special kind of weighted CAZAC sequence which abides the rule of

interference self cancellation and can improve the estimation accuracy. Furthermore, the proposed algorithm

could directly estimate the interference components without CFO estimation. Thus, system complexity can be

reduced. Most of all, the synchronization errors of each band could be independent, which is more close to

practical situation. According to the simulation results the proposed algorithm could significantly improve

system performance and achieve smooth performance when synchronization errors vary. Since the weighted

CAZAC sequence pilot pattern is defined in LTE- Advanced standard this algorithm can be treat as a candidate

method in LTE-Advanced systems and other block type pilot based systems.

REFERENCES

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Systems Beyond IMT-2000, 2003.

[2] ITU-R Rec.M.2134. Requirements related to Technical performance for IMT-Advanced Radio Interface(s),

2008.

[3] Yuan G, Zhang X, Wang W, Yang Y. Carrier aggregationfor LTE- advanced mobile communication

systems. IEEE Communications Magazine 2010; 48(2): 88– 93.

[4] Chen N, Tanaka M, Heaton R. OFDM timing synchronization under multi-path channels. IEEE 57th

Vehicular Technology Conference, April 2003.

[5] Morelli M. Timing and frequency synchronization for the uplink of an OFDMA system. IEEE Transactions

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[6] Zhang X, Ryu H, Kim J. Suppression of synchronization errors in OFDM basedcarrier aggregation system.

Proc. IEEE APCC., Nov. 2010; 106-111.

[7] Adakane DV, Vasudevan K. An Efficient Pilot Pattern Design for ChannelEstimation in OFDM Systems.

IEEE International Conference on Signal Proceesing, Computing and control, 2013.

0 5 10 15 20 25 30 3510

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10-4

10-3

10-2

10-1

100

SNR in dB

BIT

ER

RO

R R

ATE

BER performance in Ped-B channel

No Sync Error

No Suppression

Proposed System






[8] Jiang Y, Zhu X, Lim E, Huang Y. Joint Semi-Blind Channel Equalization and ICI Mitigation for Carrier

Aggregation Based CoMP OFDMA Systems with Multiple CFOs. IEEE ICC 2013 Signal Processing for

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[9] Wang H, Zhu L, Shi Y, Xing T, Wang Y. A Novel Synchronization Algorithm for OFDM Systems with

Weighted CAZAC Sequence. Journal of Computational Information Systems, 2012; 2275–2283.

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OFDMA Systems. IEEE Transactions on Wireless Communications 2007; 6(10): 3546-3551.

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[12] Zhang X, Ryu H-G. Joint Estimation and Suppression of PhaseNoise and CFO in MIMO SC-FDMA with

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using CAZAC sequences performance and sequence optimization. EURASIP Journ.Wireless Com- mun. and

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