Giải pháp cải thiện tỷ lệ lỗi bit (BER) trong hệ truyền dẫn số OFDM, ứng dụng trong truyền hình số DVB t thế hệ mới tt tiếng anh

MINISTRY OF EDUCATION AND TRAINING MINISTRY OF NATIONAL DEFENCE ACADEMY OF MILITARY SCIENCE AND TECHNOLOG Y TRAN HUU TOAN SOLUTION ON IMPROVEMENT BIT ERROR RATE (BER) IN OFDM DIGITAL TRANSMISSION SYSTEM, APPLYING FOR THE DIGITAL VIDEO BROADCASTING – NEW GENERATION TERRESTRIAL Major: Electronic Engineering Code: 52 02 03 SUMMARY OF PhD THESIS IN TECHNIQUE Hanoi, 2019 This thesis has been completed at: ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY Scientific supervisors: Dr Nguyen Van Lien Assoc Prof Dr Bach Nhat Hong Reviewer 1: Assoc Prof Dr Trinh Anh Vu University of Engineering and Technology, Vietnam National University, Hanoi Reviewer 2: Assoc Prof Dr Nguyen XuanQuyen Hanoi University of Science and Technology Reviewer 3: Assoc Prof Dr Bui Ngoc My Academy of Military Science and Technology The thesis was defended at the Doctoral Evaluating Council at Academy level, held at Academy of Military Science and Technology at …., date… 2019 The thesis can be found at: - The library of Academy of Military Science and Technology - Vietnam National Library INTRODUCTION The urgency of thesis In recent years, the Orthogonal Frequency Division Multiplexing (OFDM) has been used very effectively in many fields One of those is theDigial Video Broadcasting - Terrestrial (DVB-T) DVB-T has more disadvantages than other modes of transmission such as: - Channels are degraded due to multi-path reflections - Man - made noise - Frequency distribution is quite thick in the spectrum for interference television between analogues and digital Therefore, it has been suggested that broadcasting DVB-T is not practical However, the introduction of digital terrestrial television standards such as DVB-T of Europe, ATSC of Americal and ISDB-T of Japan has overcome most of the disadvantages mentioned above So far the DVB-T system has been evolved to the second generation (DVBT2); The DVB-T2 standard is the most modern DVB-T2 mainly broadcasts high quality digital (high definition HDTV) Therefore, the two main targets of the transmission system are concerned: channel capacity and quality of BER To achieve the two above targets, many technical solutions have been adopted in DVB-T2 [33] However, DVB-T2 does not consider comprehensive and comprehensive types of interference impact on the system For example, co-channel interference by radio stations with frequency near the digital broadcasting frequency (CCI) The InterChannel Interference remains when the carrier mode is extended to 32K, overlapping interference of the sub-wavelengths of the subcarrier over the original band At the same time, decoder quality should also be considered All of these factors are limited to improving the BER quality of the system To further improve the bit error rate for the DVB-T2 standard, the additional solution to minimize the impact of these types of above noise, while also taking into account the quality of the decoder Therefore, the topic of " Solution on improvement Bit Error Rate (BER) in OFDM digital transmission system, applying forthe Digial Video Broadcasting – New Generation Terrestrial” is a topic of high scientific and practical Research purposes - Study the effect of different types of noise reducing the bit error rate of the system - Find out solution to improve bit error rate (BER) in OFDM digital transmission system from which to apply in the Digial Video Broadcasting – New Generation Terrestrial - Improve the quality of the decoder Research subject and research scope Research subject: - ICI, CCI interference and solution on reduce ICI, CCI in DVB-T2 system - Study soft decoding for LDPC in DVB-T2 system Research scope: - M-QAM transceiver system with parameters in accordance with DVB-T2 standard Interference between subcarriers and active noise in the M-QAM system Research content - Research application of spatial filter to minimize the effect of positive interference (Co-Channel Interference (CCI)) - Research and application of Kalman filter to prevent Inter-Channel Interference (ICI) - Study soft decoding for error correction code, thereby improving the bit error rate Research Methods Analytical methods and computer simulations are used in the thesis Scientific and practical significance Scientific significance: - To proposetheapplication of spatial filter to minimize the effect of CoChannel Interference, ie, anti-interference outside the television; thereby improving the bit error rate - To propose the application of spatial filter to minimize the effect of the Inter-Channel Interference, thereby improving the bit error rate of the system - To propose the application Soft-Input Soft-Output decoding to improve decoding quality for LDPC code, thereby improving the bit error rate of the system Practical significance: - The researchs in the thesis contribute the solution on improvement the quality of the current Digial Video Broadcasting – Terrestrial The structure of the thesis The thesis consists of chapters: Chapter 1: Overview of European terrestrial digital television (DVB-T and DVB-T2) Chapter 2:Research space filter, proposedpractical application diagram Chapter 3: Research Extended Kalman Filter and proposed a detailed algorithm to minimize the effect of frequency shifting Chapter 4: This chapter researchs the MAP algorithm for LDPC code and evaluates the possibility of improving the bit error rate when using it CHAPTER 1: OVERVIEW OF THE DIGIAL VIDEO BROADCASTING – TERRESTRIAL DVB-T 1.1 Digital video broadcasting standards The first generation of DVB-T has three standards: - ATSC of American - DVB-T of Europe - ISDB-T of Japan Similarity of the three above standards is used MPEG-2 standard for video signal The main difference is the modulation method 1.2 Digital video broadcasting with Europe standard DVB DVB organization is separated many committees, including the following subcommitees: - DVB-S: Develop digital broadcasting technology via satellite - DVB-C: Develop digital broadcasting technology via cable - DVB-T: Develop digital broadcasting technology via terrestrial In Vietnam, the digital television standard is accepted and allowed for widescale deployment of DVB-T It can be said that the development of all three modes of transmission is very practical, because the three transmission environment will complement for each other 1.3 DVB-T Overview According to [34], the general block diagram of the DVB-T system shown in Figure 1.1 Figure 1.1 DVB-T system scheme DVB-T uses Basic technology solutions: - Modulating and multiplexing technique DVB-T uses OFDM modulation and multiplexing technique - Error correction coding solution (channel coding) DVB-T uses channel coding as the combined coding: Internal encoder is convulutional codes and External encoder is RS codes (Reed-Solomon codes) Using two error correction codings as a link code is better error correction According to [34] the link code has the probability of bit error decreases exponentially while the complexity increases linearly 1.4 The Digial Video Broadcasting –second generation Terrestrial (DVB-T2) DVB-T2 is the second generation of Digial Video Broadcasting –Terrestrial DVB-T2 allows for increase channel data capacity (  30% ) than DVB-T and and increased reliability in the ground wave propagation environment DVB-T2 is primarily intended for high definition digital television (HDTV) 1.4.1 Basic technical solutions: 1.4.1.1 Physical Layer Pipes (PLPs) 1.4.1.2 Extended carrier modes 1.4.1.3 Channel coding (Forward Error Correction encoding FEC) 1.4.1.4 Pilot Insertion 1.4.1.5 256-QAM modulation 1.4.1.6 Rotated Constellation 1.4.1.7 PAPR Reduction 1.4.1.8 Mapping bits onto constellations 1.4.1.9 Cell Interleaver, Time Interleaver 1.4.1.10 DVB-T2 frame structure The above are the technical solutions used in DVB-T2 The breakthrough solution is: while DVB-T uses inner and outer correction code are Convulutional Codes and RS, DVB-T2 uses LDPC and BCH These codes enable better BER performance and transfer better data over the same channel 1.4.2 Comment on DVB-T2 First of all it should be affirmed that: DVB-T2 is the most modern terrestrial digital television standard The system has inherited the DVB-T technology solutions and added new technology solutions to improve transmission capacity and dramatically improve the bit error rate Today, demand for audiovisual is increasing TV programs are required not only for content but also for quality requirements On the other hand, the trend of HD technology is increasingly being developed, HD production equipments is replacing the existing SD production system All of these issues require further refinement of DVB-T2 standards on a number of issues such as: the MPEG-4 Compressed Application Research, continue to improve the bit error probability 1.5 Summary of published works So far, there has been a number of studies to overcome and improve the quality of DVB-T2 such as [15], [16], [19], [44], [60], [61] [79] However, it can be seen that: - There is no technical solution to reduce noise in front of the TV receiver - There is no effective technical solution to minimize the impact of ICI interference on high frequency offset and Doppler frequency - The DVB-T2 decoding problem is still in hard mode So, with the aim of thesis “The solutions to improve Bit Error Rate (BER) in OFDM digital transmission system, apply for the Digial Video Broadcasting – New Generation Terrestrial”, the researching content of this thesis will be studied in three aspects as follows: Firstly, research application of spatial filter to improve SNR at the TV receiver input, this means improving the bit error probability of the system The results of this study are presented in chapter Secondly, research application of Kalman filter to minimize the effect of Inter-Channel Interference in case the frequency offset changes random.The results of this study are presented in chapter Thirdly, research application of soft encoder to substitute for hard encoder to to improve decoder’s quality, this means improving the bit error probability of the system The results of this study are presented in chapter CHAPTER 2: RESEARCH APPLICATION OF SPATIAL FILTER TO MINIMIZE INTERFERENCE OCCURRING IN TELEVISION RECEIVER 2.1 General problems about space filter and applications 2.1.1 Space and time signal 2.1.2 Spatial Filter Perform spatial signal processing with a spatial filter circuit which has impulse response h( x ,t ) is a multi-dimensional linear system Then system’s input is spatial signal S ( x ,t ) and system’s output is f ( x ,t ) Filtering process is to separate the desired signal components with a certain frequency and propagation direction 2.1.3 Basic beam structure 2.1.4 Applicability of spatial filter To minimize Co-Channel Interference (CCI), according to the theory of spatial filter circuits, M elements can create M-1 negative directions and so can only eliminate the M-1 of CCI noise source The efficiency of interference filtering of spatial filter circuits is usually evaluated by SINR ratio When the larger the M, the larger the SINR However, at that time, a great deal of calculations were required (especially multiplication) Therefore, to improve SINR of the spatial filter, attention is paid to the number of calculations (relative to the speed of the signal processing) and this is huge limitation on the possibility of applying the model to reality To reduce the signal level in the direction of the positive interference source, thesis proposed using self-compensation method which is a type of spatial filter that is made entirely of hardware and possible application in practice 2.3 Quadrature positive self-suppressor 2.3.1 Principle diagram The principle diagram of Quadrature positive self-suppressor is shown in Figure 2.6 Figure 2.6 Quadrature positive self-suppressor - The equalizer performs inverse the noise vector components and is the amplitude control element of one of the two orthogonal component interference vectors - The multiplier is the phase difference signal splitter of the two orthogonal components - The electronic locks on duty: In the absence of interference, the system will interrupt, inthe opposite, the system will be connected by the control voltage The multiplicator’s input has two signals: The first one is the interference signal in the secondary antenna are analyzed into two orthogonal components (due to the 00 and 900 phase reversal) The second is the distorted signal between the interference vector from the secondary antenna and the interference vector from the lateral wing of the main  antenna from the adder’s output S The task of the multiplier is to separate the false signal for two orthogonal channels The output signal of the multiplier:     (2.29) U  K TS U n 00 ,900 U S  cos n  sin n  2   Sensitivity of the multiplier (considered as phase separation), with the variation of  n p , is characterized by sensitivity of phase separation  :    K TS U  cos n  sin n  (2.30)  2   The gain factor of the control element is K đc Then the amount of leftover error of the system will be: U S (2.31) U Sdu   K đc The noise control factor is: U K CA  S   K đc (2.32) U Sdu Thus, the noise control factor of self-compensator depends on K đc and sensitivety of the multiplier  , with K đc  K cb K sbc (where K cb is gain factor of the equilibrium modulator, K sbc is gain factor after adder) 2.3.2 Operational principle The operational principle of quadrature positive self-suppressor is shown in Figure 2.7 Figure 2.7 The operational principle of achannelof quadrature positive self-suppressor Where: S0 is signal vector obtained from the lateral wing of the main antenna S n is signal vector obtained from secondary antenna In the secondary channel, the vector S n is separated by two orthogonal vectors S Y and S X n , then two vectors are reversed in the equilibrium modulator, n get two vectors  SYn and  S X n  The distorted signal S is applied to the two multiplicator of the two branches, generates distorted signals for two branches, passes through electronic locks and integral circuits lead to amplitude control of  SY and  S X When the n n amplitude of two these components changes, this leads to phase of  S n changes  When phase of  S n is opposite to phase of S then S  , the self-suppressor stops working Thus, the self-suppressor has created concave slot in the direction of thepositiveinterference source, so the positive interference source will not entrance the receiver This will improve BER quality of the system To be able to create four concave slots towards four positive source interferences, by using four auxiliary antennas placed perpendicular to each other – one auxiliary antenna avoids creating a concave slot in a right angle 2.4 Simulation and results As known, bit error probability is a function of signal to noise ratio (SNR) at S the receiver input Pb  f   In digital information, SNR is evaluated (or N 11 effectively eliminated noise, thereby improving the quality of the system When the more modulation level increases (the noise increases), the more effective the self-suppressor, such as: with 4-QAM modulator, K CA  10 the gain is 3dB; while with 256-QAM modulator, K CA  10 , the gain is up to 7dB This means that we can apply the quadrature positive self-suppressor to the Digial Video Broadcasting – Second Generation Terrestrial (DVB-T2) using the modulator up to 256-QAM CHAPTER 3: RESEARCH APPLICATION OFKALMAN FILTER TO PREVENT INTER-CHANNEL INTERFERENCE (ICI) 3.1 The effect of OFFSET and DOPPLER Digial Video Broadcasting –Terrestrial system uses OFDM transmission system According to [52], [67], one of the fundamental disadvantages of OFDM is very sensitive to the frequency shifting between transmitter and receiver signals Frequency offsetmay due to differences between the internal oscillator frequencies at transmitter and receiver sides or Doppler frequency shift ing in the channel Frequency offset is the causes of lossing of orthogonality between the subcarriers creating ICI interference 3.2 ICI interference cancelling solutions Synthetize references materials on preventing ICI interference can be presented in the classification scheme of the ICI interference preventing methods shown in Figure 3.2 Figure 3.2 Classification of ICI interference preventing methods 3.2.1 Self – Cancellation Scheme method (SC) Self – Cancellation scheme method proposed by Yuping Zhao and SvenGustas Haggman in 2001 [82] The main idea of this method is to modulate the input data on a group of subcarriers with predetermined coefficients for the ICI interference generated in the group will self-cancel each other Thus, the total of ICI interference components number is reduced by half, because only the even 12 subcarriers are added Therefore, if using the Self - Cancellation scheme, BER performance will improve, but bandwidth efficiency will be reduced by half 3.2.2 Maximum likelihood method (ML) This method was proposed by Moose [68] The main idea of this method is to estimate the frequency offset  by the closest algorithm and then eliminate the ICI noise at the receiver To implement this method, it is necessary to create an OFDM version before the transmission and then compare each subcarrier between the next symbols According to [63], it was found that when N and  are small, the Self – Cancellation method and the closest method have good BER performance However, both methods reduce bandwidth efficiency, because each subcarrier has a backup When N and  are high, the two methods are no longer effective In this case, according to [63] should apply the Extended Kalman Filter method, because the EKF which allows us to estimate accurately is a powerful recursive estimation method Therefore, ICI interference will be eliminated, thereby improving the system's BER quality 3.3 Kalman Filter 3.4 Extended Kalman Filter (EKF) [49] A Kalman filter has expectant and linearized covariance matrix called the Extended Kalman Filter The Kalman filtering method consists of two processes: status estimation process and calibration process (measured value updated process) of the Extended Kalman Filter 3.4.1 Status estimation The updated equations over time ofExtendedKalmanfilterare as follows: xˆ k  f (xˆ k 1 , u k 1 ,0) (3.54) Pk  A k Pk 1ATk  Wk Qk 1WkT (3.55) Like the discrete Kalman filter, state vector and covariance matrix are calculated from the previous step k  A k and Wk is the Jacobian matrix of the kth step, Q k is the covariance matrix of the process 3.4.2 Measured value update process Measured value updated equations of Extended Kalman filter are as follows: 1 (3.56) K k  Pk HTk H k Pk HTk  Vk R k VkT  xˆ k  xˆ k  K k z k  h( xˆk ,0) (3.57) Pk  (I  K k H k )Pk (3.58) Measured value updated equations of Extended Kalman filter will correct the state vector and the error covariance matrix based on the actual measured value z k H k and Vk is the Jacobian matrix of the observation process at the kth step, R k is the interference covariance matrix of the measurement process Synthetize updated process over time and measured value update process, it is possible to 13 build up the general operation diagram of the extended Kalman filter shown in Figure 3.5 Figure 3.5 Operation of the Extended Kalman filter 3.5 Application of Kalman filter to prevent inter-channel interference (ICI) To cancel ICI interference, basic problem is to accurately estimate the frequency offset  Using the above analysis, it is possible to use the extended Kalman filter as the most feasible method for exactly estimating  without reducing the system bandwidth In order to be able to apply the Kalman filter, we need to represent the mathematical equations for the updated process over time and the measured value update process To construct operation algorithm of the extended Kalman filter and to estimate  (n) , we assume the following: - Frequency offset  (n) is considered constant in an OFDM frame - Status does not change according to the steps A  - There is no control entrance u  - The measurement process interference is the state matrix H  - The fluctuation of the process is very small, choose Q  So we have the mathematical equations for the updated process over time and the measured value update process are as follows: The equations update over time: ˆ n  ˆ n1 (3.67) Pn  Pn1 (3.68) The equations update the measurement process: K n  Pn ( Pn  R )1 (3.69)   ˆ n  ˆ n  K n ( zn  ˆ n ) (3.70) Pn  1  K n Pn (3.71) The algorithm schema of the extended Kalman filter to estimate  (n) is described in Figure 3.6 14 Figure 3.6 The algorithm schema of the extended Kalman filter to estimate ˆ n Simulation and results: To compare ICI interference cancellation performance of the above three interference preventing methods, the author simulates by using Matlab software The simulation diagram shown in Figure 3.9 Figure 3.9 The simulation diagram evaluates ICI interference cancellation performance 15 The program is run with the following parameters: - Frequency offset   0.05 ,   0.15 and   0.3 - M-QAM modulator with M= 4, 16, 64 and 256 Simulation results: With the 4-QAM modulator, the results of BER performance simulations of the three methods are shown in Figure 3.10, Figure 3.11 and Figure 3.12: Figure 3.10 BER performance of interference cancellation diagrams of 4-QAM modulator with   0.05 Figure 3.11 BER performance of interference cancellation diagrams of 4-QAM modulator with   0.15 Figure 3.12 BER performance of interference cancellation diagrams of 4-QAM modulator with   0.3 With the 16-QAM modulator, the results of BER performance simulations of the three methods are shown in Figure 3.13, Figure 3.14 and Figure 3.15: 16 Figure 3.13 BER performance of interference cancellation diagrams of 16-QAM modulator with   0.05 Figure 3.14 BER performance of interference cancellation diagrams of 16-QAM modulator with   0.15 Figure 3.15 BER performance of interference cancellation diagrams of 16-QAM modulator with   0.3 With the 64-QAM modulator, the results of BER performance simulations of the three methods are shown in Figure 3.16, Figure 3.17 and Figure 3.18: Figure 3.16 BER performance of interference cancellation diagrams of 64-QAM modulator with   0.05 Figure 3.17 BER performance of interference cancellation diagrams of 64-QAM modulator with   0.15 17 Figure 3.18 BER performance of interference cancellation diagrams of 64-QAM modulator with   0.3 With the 256-QAM modulator, the results of BER performance simulations of the three methods are shown in Figure 3.19, Figure 3.20 and Figure 3.21: Figure 3.19 BER performance of interference cancellation Figure 3.20 BERperformance of interference cancellation diagrams of 256-QAM modulator with   0.05 diagrams of 256-QAM modulator with   0.15 Figure 3.21.BER performance of interference cancellation diagrams of 256-QAM modulator with   0.3 18 Based on the simulated results, author tabulate to calculate the gain with diargrams of the 4-QAM, 16-QAM and 256-QAM at BER = 10-3 Table 3.1 Compare the gain of 4-QAM interference cancellation scheme Method Do not use ICI interference cancellation Self – Cancellation Maximum likelihood Extended KalmanFilter   0.05 Gain 23dB   0.15 Gain 26 dB   0.3 Gain 42 dB 22.5dB 0.5 dB 24.5 dB 1.5 dB 28.5 dB 13.5 dB 20dB dB 20 dB dB 23 dB 20 dB 22.5dB 0.5 dB 22.5 dB 3.5 dB 22.5 dB 19.5 dB Table 3.2 Compare the gain of 16-QAM interference cancellation scheme Method Do not use ICI interference cancellation Self – Cancellation Maximum likelihood Extended Kalman Filter   0.05 Gain 31 dB   0.15 Gain   0.3 Gain 40 dB The ICI is great Cancel ICI interference but BER is large 22.5 dB 8.5 dB 32 dB dB 19 dB 12 dB 21 dB 19 dB 22.5 dB 22.5 dB 20 dB 11 dB 20 dB 20 dB 20 dB 20 dB Table 3.4 Compare the gain of 256-QAM interference cancellation scheme Method Gain   0.05 Do not use ICI 40 dB interference cancellation Self – 20 dB 20 dB Cancellation Maximum 18 dB 22 dB likelihood Extended 17.5 dB 22.5 dB Kalman Filter Look at the results we see:   0.15 Gain   0.3 Gain The ICI is great Cancel ICI interference but BER is large 18.5 22.5 dB 17.5 17.5 dB 19 - With the higher order modulator (the larger the binary symbol size), the greater the frequency offset  , the gain increases significantly when using the ML method and the EKF method The EKF has the best cancellation performance when the higher the modulation level, the greater the frequency offset; as in the 256-QAM modulator and the frequency offset   0.3 , EKF method gives a gain of about dB at BER = 10-3 compared to the ML method - The Self – Cancellation method is only effective when the frequencyoffset  is low But when the greater the frequencyoffset  , the SC method is almost impossible to cancel ICI interference General comment: - The Self – Cancellation method does not completely cancel ICI interference from vicinity subcarriers and the effect of ICI interference that is not completely cancelled will increase as the frequency shifting and symbol size increase The Self – Cancellation method does not require too complexity hardware and software, however it reduces bandwidth efficiency when there are two redundancy per subcarrier - The closest method has the same disadvantage as the Self – Cancellation method However, the BER performance is better because the frequency shifting estimate is more accurate The technical implementation of this method is less complicated than the Self – Cancellation method - Extended Kalman Filter method does not reduce bandwidth efficiency when frequency offset can be estimated from the beginning of the data series in each OFDM frame This method is more complicated than two above methods Three above methods reduce ICI interference The choice of which method depends on the specific application With the new generation of digial video broadcasting –terrestrial, high-carrier mode, high-level modulation and mobile reception (high frequency offset), it is proposed to use the Extended Kalman Filter (EKF) to eliminate the ICI interference in the systemwill give the best BER performance CHAPTER 4: APPLICATION OF SOFT DECODING TO IMPROVE BIT ERROR PROBABILITY FOR LINK CODE 4.1 LDPC code and LDPC link code 4.2 SISO soft decoding algorithms Classification of Soft-Input Soft-Output algorithms (SISO) are shown in Figure 4.2 20 Figure 4.2 Classification of SISO algorithms Comparing the above SISO decoding algorithms, according to [75] comments on complexity: MAP algorithm has the highest complexity Log-MAP decoding algorithm is three times as complex as SOVA algorithm but much lower than MAP algorithm, The Max-Log-MAP algorithm is twice as complex as the SOVA algorithm About quality: The quality of MAP algorithm is the best, the quality of Log-MAP algorithm is not as good asthat of MAP algorithm, the quality of Max-Log-MAP is lower than that of Log-MAP algorithm Finally, the quality of SOVA algorithm is the worst 4.3 Application of MAP decoding algorithm for LDPC code The MAP algorithm involves decoding algorithms of the maximum likelihood values (ML) to minimize bit error probability and this is the optimal method for estimating the state and output of Markov processes in white noise conditions The MAP decoding algorithm that the author constructs here is a soft decision decoder using channel information The decoding algorithm consists of two steps: Horizontal step: update rmn ( x ) The rmn ( x ) quantities that concern with the m check bits will be updated and passed as the message to the bit nodes that have been checked by the m check nodes This operation is performed with all test nodes Vertical step: update qmn ( x ) The quantities qmn ( x ) that concern with the n bits are updated and passed as a message to the check nodes, including the n bit node Consider the case of binary LDPC code: 4.3.1 Horizontal step: update rmn ( x ) For qml  qml ( )  qml ( ) and rml  rml ( )  rml ( ) According to [72], we have: rmn  qmn (4.6) nN m ,n 21 For each other element O (m, n) of H, calculate the product qmn , along the m row, subtract the value of n column It is therefore called horizontal step Use condition rmn ( )  rmn ( )  , there is a link between rmn , rmn ( ) and rmn ( ) calculated by the following formula:  rmn  rmn , rmn ( )  (4.7) rmn ( )  2 4.3.2 Vertical step : update qmn ( x ) Theorem 4.1 [72]: For a cn bit which relate to the check states, if the independent check bits are: qn ( x )  P( cn  x | rn )  P( z m  | cn  x ,r ) (4.8) mM n Where  is normalized constant We have: rmn ( x )  P( zm  | cn  x ,r ) Using (4.9) and theorem 4.1 we can write: qn ( x )  P( cn  x | r )  rmn ( x ) (4.9) (4.10) mM n Abbreviate : qmn  P( cn  x | zm  0,m  M n ,m ,r ) According to theorem 4.1, it is possible to write: qmn ( x )  P( cn  x | r )  rmn ( x ) (4.11) (4.12) mM nm From the product of (4.12) calculated under the column of matrix H (along the check bits), the update qmn ( x ) is called the vertical step of the decoding algorithm 4.3.3 Initialization and completion of decoding The iterative decoding algorithm is initialized by setting qmn x   Pn x  , with qn ( x ) determined by the formula: qn ( x)   n Pn ( x)  rmn ( x) mM n (4.13) Where  n was chosen to qn 0  qn 1  Posterior probability q n ( x ) is used to make decision for x , x  0,1 Perform temporary decision: If qn 1  0.5 , establish cn 1  If qn ( )  0.5 , establish cn ( )  Decoding ends when Hcˆ  , that means all check bits are satisfied simultaneously Conversely, if the number of iterations is less than the maximum number of iterations, repeat from the horizontal step; if the number of iterations is greater than or equal to the maximum number of iterations, the error is reported This is a break-down that goes beyond code error correction capability with the number of that loops 22 From the above analyzes of the loop decoding procedure for the LDPC code, we have the decoding algorithm flowchart shown in Figure 4.4 Figure 4.4 Algorithm flowchart of loop decoding for binary LDPC code 4.4 Effeciency of improve bit error probability when using soft decoding for LDPC code compared to hard decoding 4.4.1 Simulation block diagram The simulation block diagram is shown in Figure 4.5 Figure 4.5 Simulation block diagram 23 4.4.2 Simulation results Matlab Simulink simulation software was used to evaluate soft loop decoding using MAP algorithm with hard decision decoding with LDPC code (16200, 8100), ie code rate , with effect of Gaussian noise The simulation’s result is shown in Figure 4.7 Figure 4.7.Evaluate soft loop decoding and hard decision decoding with LDPC code (16200, 8100) From the simulation’s result in Figure 4.7, it can be seen that: with bit error rate of 10-4, the gain of soft decoding compared to the hard decoding for LDPC code (16200, 8100) is 1.2dB That means that when applying the MAP decoding algorithm to the OFDM system, the decoding result is better than hard decoding, thereby significantly improving the quality of the system Especially, with Digial Video Broadcasting – Second Generation Terrestrial (DVB-T2) system that is currently using hard decoding algorithm (bit flipping algorithm) to decode the LDPC code, it is necessary to consider replacing the soft decoding algorithm for this system However, in order to apply the MAP decoding algorithm to the present Digial Video Broadcasting –Terrestrial system, it is necessary to replace the microprocessing chips and the memory capacity to ensure that the system is not delayed during processing in real time Because, with hard decoding, the signal passes through the demodulator will make a decision right away about the information bits before send them to the decoder Whereas, with soft decoding, the demodulator does not self-determine the obtained bits information obtained, but will send its information to the decoder in a suitable structure so that the decoder makes the final decision more accurately, ie, the system’s quality will be better But then the system will be delayed because it takes some time to process and make the final decision on the obtained bits information That means that when 24 applying the MAP decoding algorithm to DVB-T2 system, it will have to pay the penalty for the complexity and delay, leading to system delay during processing in real time However, with the rapid development of microprocessing chip technology and memory capacity today, the complexity of soft decoding is no longer a problem Therefore, application of MAP decoding algorithm to DVB-T2 systems to improve the system’s quality is feasible CONCLUSION A The updatedresults of the thesis 1) With the application of space filters to the DVB-T2 system, it is possible to improve signal/noise ratio at the receiver input, thus improving the bit error rate of the system However, the application of this filter system in practice will be greatly restricted because of the large number of calculations required (especially multiplications) Propose application of self-suppressor to the DVB-T2 system will allow to cancel the positive noise outside the television receiver, while the implementation is not too complex A technical solution that DVB-T2 has not mentioned yet 2) Based on the research application of Extended Kalman filter to cancel the Inter-Carrier Interference (ICI) caused by frequency offset which loss of orthogonality between subcarriers, the author proposed to use this filter on current DVB-T2 system to improve the system's BER quality This filter scheme is especially effective when the frequency offset is high 3) The thesisproposed MAP decoding algorithm for LDPC code replacing hard decoding algorithm in DVB-T2 standard for lower bit error probability Thus, using the MAP decoding algorithm will improve the BER quality of the current DVB-T2 standard At the same time, with the proposed parity check matrix that designed with simple structure H in MAP decoding algorithm not only provides good decoding efficiency but also allows the application of this algorithm when the modulation level increases B Further research direction 1) Survey the overall system using three research results listed in A to DVBT2 standard Evaluate quantitatively the effectiveness of improving the BER quality of the whole system 2) Apply the research results step by stepinto reality asour country tests Digial Video Broadcasting - Second Generation Terrestrial (DVB-T2), contributing to improve the efficiency of terrestrial television system 25 SCIENTIFIC PUBLICATION [1] Tran Huu Toan, Bach Nhat Hong, “Adaptive modulation method accoding to quantity and location of subcarriers in OFDM transmission system”, Journal of Military Science and Technology, no.40, pp.70-76, December 2015 [2] Tran Huu Toan, Bach Nhat Hong, “Applying MAP decoding algorithm for LDPC code”, Journal of Science and Technology (Hanoi University of Industry), no.32, pp.17-20, February 2016 [3] Tran Huu Toan, Bach Nhat Hong, “A parity check matrix construction method for LDPC code as required by the previous code rate and matrix size”, Journal of Science and Technology (Hanoi University of Industry), no.34, pp.9-11, June 2016 [4] Tran Huu Toan, “Propose solutions to improve the quality of Digital Video Broadcasting – Second Generation Terrestrial”, Journal of Science and Technology (Hanoi University of Industry), no.38, pp.50-54, February 2017 [5] Tran Huu Toan, Bach Nhat Hong, “Applying Extended Kalman Filter to prevent interchannel interference in OFFDM system”, Journal of Military Science and Technology, no.52, pp.97-103, December 2017 [6] Tran Huu Toan, “Research application of spatial filter to minimize interference occurring in television receiver”, Journal of Military Science and Technology, no.57, pp.52-58, October 2018 ... updatedresults of the thesis 1) With the application of space filters to the DVB- T2 system, it is possible to improve signal/noise ratio at the receiver input, thus improving the bit error rate... Practical significance: - The researchs in the thesis contribute the solution on improvement the quality of the current Digial Video Broadcasting – Terrestrial The structure of the thesis The thesis... BCH These codes enable better BER performance and transfer better data over the same channel 1.4.2 Comment on DVB- T2 First of all it should be affirmed that: DVB- T2 is the most modern terrestrial