924_ftp Robust Frame Synchronization for the DVB-S2 System With Large Frequency Offsets

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INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS

Int. J. Commun. Syst. Network 2009; 27:3552Published online 14 November 2008 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/sat.924

Robust frame synchronization for the DVB-S2 system withlarge frequency offsetsz

Pansoo Kim1,,y, Raffaella Pedone2, Marco Villanti2, Alessandro Vanelli-Coralli2,Giovanni Emanuele Corazza2, Dae-Ig Chang1 and Deock-Gil Oh1

1Global Convergence Technology Research Department, ETRI, 161 Gajeong-dong, Yuseong-gu, Deajeon 305-350,Republic of Korea

2DEIS/ARCES, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy

SUMMARY

This paper tackles the problem of frame synchronization design for the Digital Video Broadcastingstandard via Satellite (DVB-S2) system. In particular, the design of a novel frame detection schemeexhibiting robustness against large frequency errors is proposed. Post-detection integration (PDI) isexploited to provide coarse frame alignment with limited complexity with respect to classical approximatemaximum likelihood (ML) solutions. The novel scheme, denoted as differential generalized post-detectionintegration, derives from a pragmatic adaptation of the well-known generalized PDI scheme (IEEE Trans.Commun.2007;55(11):21592171), which eliminates its inherent source of weakness in the presence of largefrequency offsets, i.e. its non-differential component given by non-coherent PDI. The proposed approachmanages to reduce the remarkable performance gap with respect to approximate ML techniques available

in the literature (IEEE Trans. Commun.2002;50(7):10621065), providing the designer with an interestingperformance/complexity trade-off. Performance is numerically evaluated in terms of receiver operatingcharacteristics and mean acquisition time (MAT), handling the acquisition procedure according to a single-dwell approach. Both threshold crossing and MAX criterion are contrasted to identify the best designsolution. Copyrightr 2008 John Wiley & Sons, Ltd.

Received 3 June 2008; Revised 31 July 2008; Accepted 9 August 2008

KEY WORDS: DVB-S2; frame synchronization; differential detection; post-detection integration; frequencyoffset; threshold crossing

*Correspondence to: Pansoo Kim, Global Convergence Technology Research Department, ETRI, 161 Gajeong-dong,Yuseong-gu, Deajeon 305-350, Republic of Korea.yEmail: [email protected] paper was presented in part at the IEEE WCNC07.

Contract/grant sponsor: ETRIMavigex Collaboration ProjectContract/grant sponsor: IT R&D program of KCC/IITA; contract/grant number: 2008-S008-02

Copyright r 2008 John Wiley & Sons, Ltd.


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1. INTRODUCTION

The increasing demand for new multimedia services has recently fostered the development of an

upgraded version of the Digital Video Broadcasting standard via Satellite (DVB-S). The new

standard, identified as DVB-S2 [1,2], besides supporting the backward-compatible mode,includes novel features to increase the system capacity, such as adaptive coding and modulation

(ACM) [3] and is able to achieve satisfactory performance at very low signal-to-noise ratio

(SNR) thanks to the introduction of robust low-density parity check (LDPC) codes [4].

The numerous effort spent in optimizing capacity and bit error rate performance requires

one to design efficient synchronization and parameter estimation algorithms, which are

able to effectively operate at low SNR. This task is made even more difficult by the fact

that low-cost mass market set-top boxes usually employ low-quality tuner chip, which can

cause large frequency offsets at the high-frequency bands considered in DVB-S2 (Ku/Ka

bands).

This paper focuses in particular on frame synchronization for the DVB-S2 system, which is

the necessary requisite for correct demodulation and decoding at the receiver. As in common

practice, DVB-S2 frame synchronization is based on a data-aided approach that inserts a knownpreamble in the frame, namely the unique word identified as start of frame (SOF) in [1]. Frame

synchronization is usually divided into two phases: the first is frame acquisition, which provides

the coarse frame epoch estimate; the second is tracking, which serves as a verification of the

hypothesis provided by acquisition. Frame acquisition is certainly the most critical step because

it is characterized by the largest uncertainty region (the frame epoch domain) that normally

spans over the entire frame duration at terminal start-up. Owing to its inherent criticality, frame

acquisition is specifically addressed in this paper. To handle the large uncertainty domain,

acquisition is achieved by quantizing the uncertainty region into a finite number of cells or

hypotheses and solving a detection problem that has to discriminate between the synchronous

cell/cells, indicated as H1, and all misaligned cellsH0. With continuous transmissions, as in the

DVB-S2 case, symbol timing synchronization can be satisfactorily performed before frame

acquisition, exploiting, for example, the Gardner algorithm [5]. The absence of unknownfractional timing delays (smaller than the symbol period) allows performing frame acquisition

with a single hypothesis/cell per symbol in the discretization process, without introducing

oversampling at the receiver to reduce the associated complexity.

An example of robust frame detection in the presence of frequency offset based on the

approximate maximum likelihood (ML) criterion has been proposed by Choi and Lee in [6],

where very satisfactory performance is achieved at the cost of large complexity, through

averaging out the frequency uncertainty and introducing ad hoc design modifications. Less

complex solutions are provided by the post-detection integration (PDI) approach [79]. The

base concept of PDI detection is to perform coherent correlation over a SOF segment followed

by a second accumulation stage that acts after a non-linear processing able to mitigate the

effects of the frequency rotation. Different PDI (DPDI) techniques can be found in the

literature, such as non-coherent PDI (NCPDI), DPDI, and generalized PDI (GPDI). Inparticular, GPDI is the linear combination of NCPDI, DPDI, and several other differential

terms, resulting in this way the most robust PDI solution. In scenarios with moderate frequency

errors, besides the complexity reduction, PDI schemes can provide significant performance gains

with respect to the approximate ML solutions, exploiting the positive effects of coherent

correlation performed over SOF. In fact, by opportunely dimensioning the correlation length,

P. KIM ET AL.36

Copyright r 2008 John Wiley & Sons, Ltd. Int. J. Commun. Syst. Network2009; 27:3552

DOI: 10.1002/sat


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an SNR improvement can be achieved notwithstanding the angular rotation introduced by the

frequency error.

Unfortunately, considering the very large frequency offset of actual DVB-S2 scenarios (in the

order of 20% of the symbol rate) referred in DVB-S2 implementation guideline [2], no coherent

accumulation is typically feasible here, and PDI solutions are in general penalized. In particular,NCPDI is not able to provide useful contribution to the GPDI approach, thus degrading the

achievable detection performance. To overcome this problem, this paper proposes a novel PDI-

based detection scheme able to enhance the robustness against frequency uncertainty with

respect to GPDI, limiting at the same time complexity. The novel scheme, identified as

differential generalized post-detection integration (D-GPDI), is derived through a structured

approach that alters the structure of GPDI by eliminating NCPDI and leaving only the

differential terms. D-GPDI manages to outperform GPDI when no coherent accumulation is

feasible, thus approaching the approximate ML solutions, and providing the designer with an

interesting performance/complexity trade-off.

2. SYSTEM MODEL

The system model is carried out by considering the DVB-S2 frame structure, where the SOF of

lengthLSOF precedes data symbols. The baseband equivalent of the received signal rtis given

by rt st nt, where nt is the complex additive white Gaussian noise with two-sided

power spectral density N0/2 and st is the baseband equivalent transmitted signal in the form

st X1

k1

ffiffiffiffiffiEs

p akgtkTse

j2pDftf 1

wheregtis the squared root raised cosine (SRRC) pulse waveform normalized in energy, Es is

the average symbol energy,Tsis the symbol period, Dfis the unknown frequency offset,f is the

unknown phase, and ak can be written as

ak cjkjLF

; jkjLF 2 0; LSOF 1

dk; jkjLF 2 LSOF; LF 1

81)

Decision

variable

Decision

variable

MF

Sequence

Generator

Figure 1. Non-coherent PDI (NCPDI) andn-Span differential PDI (n-Span DPDI) block diagrams. DPDIis n-Span DPDI with n5 1.

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GPDI, we can write as follows:

LNCPDI XL1k0

jxkj2 5

LnSpan DPDI XL1kn

xkxkn

6

L

GPDI

L

NCPDI

2XL1

n1 L

nSpan DPDI

7

where xk Pk1M1

mkM rmcm is the received sample after coherent correlation over M.

Unfortunately, the very large normalized frequency offset (DfTs up to 20%) that affects the

received signal in actual DVB-S2 scenarios requires the adoption of a coherent integration

length M5 1 to optimize PDI performance. This constraint partially limits the inherent

performance advantages of PDI detectors with respect to the solutions available in the literature

proposed by Choi and Lee [6], based on the approximate ML criterion, which will be detailed

for completeness in Section 3.2. However, PDI schemes remain valid alternatives to these

solutions, especially GPDI, which solves frequency uncertainty through a generalized LRT

decision criterion instead of averaging it out as done by Choi and Lee. Notwithstanding the

performance loss, even with M5 1, PDI approaches are still interesting because of the reduced

computational complexity, which is quantitatively measured in Section 3.1.The performance gap between the conventional PDI solutions and the approximate ML

approach can be reduced by observing that the NCPDI component in the GPDI scheme results

to be particularly penalized by the design constraint M5 1 required by the very critical

frequency offset. In fact, as clearly shown in Figure 1, NCPDI performs a block energy

detection of the received signal, by accumulating over L blocks ofM samples. Notably, when

DGPDI

Figure 2. Generalized PDI (GPDI) and differential GPDI (D-GPDI) block diagrams.

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M5 1, NCPDI has an inherent weakness in distinguishing between the H1 and H0 hypotheses

because its output presents the same statistics in both cases, making it not applicable in practice.

This fact is one-to-one with a performance degradation introduced in the GPDI approach, since

the NCPDI output partially masks the gain provided by all n-Span DPDI terms, which instead

are still able to collect useful information with M5

1.This observation is the rationale behind the introduction of the novel detector identified as

D-GPDI. D-GPDI implements in fact the same detection structure as GPDI eliminating the

NCPDI branch, and thus is able to outperform GPDI for M5 1. Formally, D-GPDI is defined

by the decision variable

LDGPDI 2XL1n1

LnSpan DPDI 8

which is essentially GPDI without the NCPDI contribution and where L LSOF in the case

under evaluation. Note that the multiplicative coefficient in Equation (8) is left for coherence

with GPDI, but has evidently no impact on performance. The corresponding block diagram is

schematized in Figure 2 identified by the dotted square and overlapped with GPDI in order to

highlight the nature of the performed structured design.

3.1. Complexity evaluation of D-GPDI

As anticipated, one of the main advantages of the use of the PDI-based structured approach for

frame acquisition is the limited complexity required to achieve satisfactory performance, which

enables practical implementation in commercial receivers. To provide quantitative measure of

this issue, the required computational complexity of the proposed structured approach is

computed here in terms of the number of multiplications needed to perform a single test in the

uncertainty region scan process.

Considering at first the conventional PDI schemes, the number of required multiplications,

Nim, i5NCPDI, DPDI, n-Span DPDI, and GPDI, and assuming that M5 1 and L LSOF

result in

NNCPDIm 6LSOF 9

NDPDIm 24LSOF 1 10

NnSpan DPDIm 22LSOF 2LSOF n 1 11

NGPDIm 2LSOFLSOF 3 1 12

The complexity related to the novel D-GPDI solution can be computed as the GPDI

complexity depurated by the processingad hocrequired by NCPDI, i.e. 2LSOFmultiplications to

perform PDI of the coherent samples squared modules, yielding

NDGPDIm 2LSOFLSOF2 1 13

Notably, both GPDI and D-GPDI complexities are related to L2SOF with a coefficient 2,

while the linear dependence on LSOF has a weighting factor 6 for GPDI and 4 for D-GPDI.

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3.2. Reference detector for performance comparison

To complete the picture, this subsection presents more in detail an alternative solution for SOF

detection, based on the ML criterion, to provide a performance benchmark for the proposed D-

GPDI scheme.

As anticipated in the Introduction, the application of the ML criterion, along with severalapproximations to analytically manage the design problem, yields effective and very robust

solutions for SOF detection in the presence of frequency uncertainty [6,10,11]. Here, we consider

as a reference the scheme proposed by Choi and Lee and identified as L0 in [6], denoted in the

following as Choi and Lee detectorL0 (CLD-L0). The corresponding decision variable is

defined as [6]

LCLDL0 XLSOF1i1

XLSOF1ki

rkckrkicki

2

XLSOF1ki

jrkj2jrkij

2

8

924_ftp Robust Frame Synchronization for the DVB-S2 System With Large Frequency Offsets

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