Effect Of Turbo Codes On OFDM Systems Over AWGN Channel To Improve BER

Transcription

1 International Journal of Power Control Signal and Computation(IJPCSC) Vol. 4. No.2. pp April - June 2012 ISSN: X Effect Of Turbo Codes On OFDM Systems Over AWGN Channel To Improve BER G.Brindha,Assistant professor Lord Jegannath college of Engineering and Technology.Anna University Chennai, TamilNadu,India 1 brindha.13@gmail.com Abstract - OFDM(Orthogonal Frequency Division Multiplexing) has gathered increased interest due to its high spectrum efficiency and robustness against multipath interference, which makes the efficient use of spectrum by allowing overlap. OFDM is a popular modulation method for the transmission of high data rates over the wireless channels. There is a growing demand for the transmission of information quickly without the formation of errors. In this paper, the use of Turbo codes increases the reliability of OFDM system to achieve high data rates. Forward Error Correcting (FEC)codes are a new class of codes that can achieve increased error performance in OFDM systems. The Bit Error Rate(BER) performance is substantially improved by increasing the number of iterations in the case of Turbo Codes.The simulation is made with MATLAB source code under Additive white Gaussian Noise(AWGN) channel. Keywords: Orthogonal Frequency Division Multiplexing, Turbo codes, FEC, Bit Error Rate. 1. INTRODUCTION The telecommunications industry is in the midst of a veritable explosion in wireless technologies. Once exclusively military, satellite and cellular technologies are now commercially driven by ever more demanding consumers, who are ready for seamless communication from their home to their car, to their office, or even for outdoor activities. With this increased demand comes a growing need to transmit information wirelessly, quickly, and accurately. To address this need, communications engineer have combined technologies suitable for high rate transmission with forward error correction techniques. The latter are particularly important as wireless communications channels are far more hostile as opposed to wire alternatives, and the need for mobility proves especially challenging for reliable communications. Orthogonal Frequency Division Multiplexing (OFDM) is a Multi-Carrier Modulation technique in which a single high rate data-stream is divided into multiple low rate datastreams and is modulated using sub-carriers which are orthogonal to each other. Some of the main advantages of OFDM are its multi-path delay spread tolerance and efficient spectral usage by allowing overlapping in the frequency domain. OFDM is implemented in practice using the discrete Fourier transform (DFT).Recall from signals and systems theory that the sinusoids of the DFT form an orthogonal basis set, and a signal in the vector space of the DFT can be represented as a linear combination of the orthogonal sinusoids. One view of the DFT is that the transform essentially correlates its input signal with each of the sinusoidal basis functions. If the input signal has some energy at a certain frequency, there will be a peak in the correlation of the input signal and the basis sinusoid that is at that corresponding frequency. This transform is used at the OFDM transmitter to map an input signal onto a set of orthogonal sub carriers, i.e., the orthogonal basis functions of the DFT. Similarly, the transform is used again at the OFDM receiver to process the received sub carriers. The signals from the sub carriers are then combined to form an estimate of the source signal from the transmitter. The orthogonal and uncorrelated nature of the sub carriers is exploited in OFDM with powerful results. Since the basis functions of the DFT are uncorrelated, the correlation performed in the DFT for a given sub carrier only sees energy for that corresponding sub carrier. The energy from other sub carriers does not contribute because it is uncorrelated. This separation of signal energy is the reason that the OFDM sub carriers spectrums can overlap without causing interference. OFDM signals are typically generated digitally due to the difficulty in creating large banks of phase locks oscillators and receivers in the analog domain. Figure.1 shows the block diagram of a typical OFDM transceiver. The transmitter section converts digital data to be transmitted, into a mapping of subcarrier amplitude and phase. It then transforms this spectral representation of the data into the time domain using an Inverse Discrete Fourier Transform (IDFT). The Inverse Fast Fourier Transform (IFFT) performs the same operations as an IDFT, except that it is much more computationally efficiency, and so is used in all practical systems. In order to transmit the OFDM signal the calculated time domain signal is then mixed up to the required frequency. Figure.1.OFDM Transceiver The receiver performs the reverse operation of the transmitter, mixing the RF signal to base band for processing, then using a Fast Fourier Transform (FFT) to analyze the signal in the frequency domain. For a given system bandwidth the symbol rate for an OFDM signal is

2 171 much lower than a single carrier transmission scheme. This convolutional code. The binary input data sequence is low symbol rate makes OFDM naturally resistant to effects represented by d k the input sequence is passed into the of Inter-Symbol Interference (ISI) caused by multipath input of a convolutional encoder [8], ENC1 and a coded bit propagation. Multipath propagation is caused by the radio stream, generated. The data sequence is then interleaved. transmission signal reflecting off objects in the propagation That is, the bits are loaded into a matrix and read out in a environment, such as walls, buildings, mountains, etc. way so as to spread the positions of the input bits. The bits are often read out in a pseudo random manner. The interleaved data sequence is passed to a second convolutional encoder ENC2, and a second coded bit stream, is generated. Figure.2.Addition of Guard period to an OFDM signal These multiple signals arrive at the receiver at different times due to the transmission distances being different. This spreads the symbol boundaries causing energy leakage between them. The effect of ISI on an OFDM signal can be further improved by the addition of a guard period to the start of each symbol. This guard period is a cyclic copy that extends the length of the symbol waveform. Each sub carrier, in the data section of the symbol, (i.e. the OFDM symbol with no guard period added, which is equal to the length of the IFFT size used to generate the signal) has an integer number of cycles. Because of this, placing copies of the symbol end-to-end results in a continuous signal, with no discontinuities at the joins. 2. TURBO CODES Turbo codes were first presented at the International Conference on Communications in Until then, it was widely believed that to achieve near Shannon s bound performance, one would need to implement a decoder with infinite complexity. Parallel concatenated codes, as they are also known, can be implemented by using either block codes (PCBC) or convolutional codes (PCCC). PCCC resulted from the combination of three ideas that were known to all in the coding community. The transforming of commonly used non-systematic convolutional codes into systematic convolutional codes. The utilization of soft input soft output decoding. Instead of using hard decisions, the decoder uses the probabilities of the received data to generate soft output which also contain information about the degree of certainty of the output bits. This is achieved by using an interleaver. Encoders and decoders working on permuted versions of the same information. The combination of turbo codes with the OFDM transmission is so called Turbo Coded OFDM (TC- OFDM) can yield significant improvements in terms of lower energy needed to transmit data, a very improvement issue is in personnel communication devices. A. Turbo Encoder The basic mechanism of turbo codes is the use of two convolutional codes in parallel with the interleaver between them.the encoder is a parallel concatenated Figure.3..Turbo Encoder We can regard the turbo code as a large block code. The performance depends on the weight distribution - not only the minimum distance but the number of words with low weight. Therefore, we want input patterns giving low weight words from the first encoder to be interleaved to patterns giving words with high weight for the second encoder. Two component codes are used to code the same input bits, but an interleaver is placed between the encoders. The outputs from the two component codes are then punctured and multiplexed. Note that the systematic bits are rarely punctured, since this degrades the performance of the code more dramatically, than puncturing the parity bits. The choice of the interleaver is a crucial part in the turbo code design. The task of the interleaver is to scramble bits in a pseudo-random, predetermined fashion. This serves two purposes. Firstly, if the input to the second encoder is interleaved, its output is usually quite different from the output of the first encoder. This means that even if one of the output code words has low weight, the other usually does not, and there is a smaller chance of producing an output with very low weight. Higher weight, as we saw above, is beneficial for the performance of the decoder. Secondly, since the code is a parallel concatenation of two codes, the divide-andconquer strategy can be employed for decoding. If the input to the second decoder is scrambled, also its output will be different, or uncorrelated from the output of the first encoder. This means that the corresponding two decoders will gain more from information exchange. ENC1 and ENC2 are Recursive Systematic Convolutional (RSC) codes that is, convolutional codes which use feedback (they are recursive ) and in which the uncoded data bits appear in the transmitted code bit sequence. B. Turbo Decoder Two component decoders are linked by interleavers in a structure similar to that of the encoder. As seen in the Figure.ure 3, each decoder takes three inputs i.e. the systematically encoded channel output bits, the parity bits transmitted from the associated component encoder, and the information from the other component decoder about the likely values of the bits concerned. The

3 172 decoder operates iteratively, and in the first iteration the fades in the frequency response of the channel cause some first component decoder produces a soft output as its groups of subcarriers to be less reliable than other groups estimate of the data bits. The soft output from the first and hence cause bit errors to occur in bursts rather than, encoder is then used as additional information for the independently. The burst errors can extensively degrade second decoder, which uses this information along with the the performance of coding. To solve this channel outputs to calculate its estimate of the data bits. problem, several ways are considered. The easiest method The second iteration can begin, and the first decoder is to use stronger codes, in fact an interleaving technique decodes the channel outputs again, but now with additional along with coding can guarantee the independence among information about the value of the input bits provided by errors by affecting randomly scattered errors. We use turbo the output of the second decoder in the first iteration. This code to improve the performance. For analysis of the cycle is repeated, and with every iteration,the Bit Error OFDM system, first we examine the uncoded situation and Rate (BER) of the decoded bits tends to fall. However the then we will analyze the effect of coding under turbo coded improvement in performance obtained with increasing OFDM condition. numbers of iterations decreases as the number of iterations increases. Both decoders provide estimates of the same set A. Algorithm For Simulation of data bits, albeit in a different order. If all intermediate values in the decoding process are soft values, the decoders can gain greatly from exchanging information, after appropriate reordering of values. Information exchange can be iterated a number of times to enhance performance. Such decoders, although more difficult to implement, are essential in the design of turbo codes.the component decoders have to exploit both the inputs from the channel and a-priori information. They must also provide what are known as soft outputs for the decoded bits. C. Puncturing Different code rates are achieved by puncturing the parity bit sequences Puncturing the data bit sequence leads to a severe degradation in turbo code performance.for maximum flexibility,puncturing is applied at signal space level.the modulator s output is a vector of points in signal space representing the information and parity bits for a frame,in the correct temporal order for transmission through the channel.the puncturing module then includes some of these points for transmission and skips those that should be punctured.at the receiving end,the probabilities for bits associated with punctured modulation symbols are set to be equiprobable. 3. ANALYSIS OF TURBO CODED OFDM SYSTEM An OFDM system was modeled using MATLAB to allow various parameters of the system to be varied and tested. The aim of doing the simulations was to measure the performance of OFDM under AWGN channel. The combination of turbo codes with the OFDM transmission is so called Turbo Coded OFDM (TC-OFDM) can yield significant improvements in terms of lower energy needed to transmit data, a very improvement issue in personal communication devices [12, 13]. Unfortunately, the majority of existing papers treating the TCOFDM assumes that the channel estimation using only the pilot symbols is sufficient. It is shown, however, that there is a large potential gain in using the iterative property of turbo decoders where soft bit estimates are used together with the known pilot symbols. The performance of such an iterative estimation scheme proves to be of particular interest when the channel is strongly frequency- and timeselective. Similar to every other communications scheme, coding can be employed to improve the performance of overall system. Several coding schemes, such as block codes, convolutional codes and turbo codes have been investigated within OFDM systems. Moreover, the deep Figure.4.Simulation model of OFDM system with Turbo codes The above Figure.ure shows the simulation model of the OFDM system with Turbo codes. The algorithm for the simulation is with many steps. The steps are as follows. 1. First the information bits are generated randomly. 2. The information bits are encoded using turbo encoder with generator matrix. 3. Use different modulation schemes to convert the binary bits 0 and 1into complex signals in the system. 4. Then serial to parallel conversion is performed. 5. Zero padding is then performed. 6. Use IFFT to perform the generation of OFDM signals. 7. To transmit signals serially perform parallel to serial conversion. 8. Then introduce noise to simulate channel errors. Here we assume AWGN channel to transmit the signals..perform reverse operations to decode the received signal at the receiver side. 9. By comparing the decoded bits with the original bits we can compute the erroneous bits. 10. The calculate the bit Error Rate and plot the graph. There are different simulation parameters used in this OFDM system.the simulation parameters are shown in the table 1. Table1.Simulation Parameters Error correcting code Turbo codes Channel AWGN Number of sub carriers 48 Number of pilot carriers 4 Coding rate 1/2 Symbol interval 4µsec Iterations 4 Channel spacing 20MHz

4 SIMULATION RESULTS The turbo codes give better performance at low First the development of an OFDM system model SNR. The BER performance of TCOFDM system is then try to improve the performance by applying forward compared with the respective uncoded system under the error correcting codes to the uncoded system. From the fading AWGN channel. In this Figure.ure 4 it is shown study of the system, it can be concluded that we are able to that, for the required BER 10-3 AWGN channel gives improve the performance of uncoded OFDM by better performance as compared with marcov channel. convolutional coding scheme. Figure.6 shows the BER for AWGN gives a gain of approximately 22 db over marcov turbo codes with different iterations. channel. We observe a little gain at lower SNR between 0 Further improvement on the performance has to <10dB, and more gain at higher SNR < 40dB. been achieved by applying turbo coding to uncoded OFDM system. Turbo codes with low order decoding iterations have been evaluated. The SNR performance for BER vs SNR 10 0 OFDM with Impulse BER 10 2 and 10 4, that are suitable for speed and data OFDM with AWGN applications, are 173nalysed. As a result, the TCOFDM system with least number of decoding iterations, 5 to 10 iterations are shown to be sufficient to provide good BER Bit performance. error rate 0 BER 10 0 BER Vs SNR plot for turbo codes for different iterations Original Two iterations Three iterations Five iterations Ten iterations Figure.7.OFDM system with AWGN and Impulse channel 10 0 Turbo code (puntured) with Figure.5.BER Vs SNR for Turbo codes for different iterations Simulation also shows the performance of marcov noise. Simulation results are shown in Figure.7 shows the influence of asynchronous impulsive noise on turbo coded OFDM system[1]. As shown in Figure.ure, the influence of the impulse noise is distributed over the whole carriers by applying DFT in the receiver. Therefore, data symbol on each sub-carrier is degraded under the case where large impulse noise is added or many impulses are added to the OFDM symbol whereas small impulse noise affect less data symbols on sub-carrier, hence less affective. If there are so many symbol errors in the symbols, then whole OFDM symbol will be lost [1]-[12]. Prob of bit error Figure.8.Turbo code with 6 iterations for punctured sequence in db Turbo code (unpuntured) with Uncoded AWGN +HIR AWGN+LIR AWGN Prob of bit error Probabi lity of error in db Figure.6.Probability of error for Uncoded, AWGN,AWGN with HIR and AWGN with LIR Published: goplex Publishing Figure.9.Turbo code with 6 iterations for unpunctured sequence 5. CONCLUSION To conclude, this major project gives the detail knowledge of a current key issue in the field of

5 174 communications named Orthogonal Frequency Division 10. Turbo-coded OFDM in IEE Trans. Of Multiplexing (OFDM). We focused our attention on turbo codes and their implementation. We elaborated on the performance theory of the codes Then we tied concepts of OFDM and turbo coding with a target-based, modulation scheme. The SNR performance for BER and, that are suitable for speed and data applications, are analyzed. As a result, the TCOFDM system with least number of decoding iterations, 3 to 5 iterations are shown International Conference on Universal Personal Communications, Hanjong Kim, Performance improvement of Block Turbo Coded OFDM System Using channel state information the 23rd international conference on circuits/systems, computers and communications (ITC-CSCC 2008). 12. J. Terry, and J. Deiskala, OFDM Wireless LANs: to be sufficient to provide good BER performance. A Theoretical and Practical Guide, Sams The concept of OFDM and turbo coding with a Publishing, Indiana, target-based, modulation scheme by introducing the noises, which occurs in power line communication networks is done by analyzing the performance of power line networks. The simulation of the entire work is done on MATLAB7. First developing an OFDM system model then try to improve the performance by applying forward error correcting codes to our uncoded system. From the study of the system, it can be concluded that improving the 13. Haixa Zhang, Feng Zhao, Dongfeng Yuan, Mingyan Jiang, Performance of turbo code an WOFDM system on rayleigh fading channels, Proceedings, IEEE, vol. 2, pp , Sept George White, Optimised Turbo Codes for Wireless Channels, Phd Thesis Communications performance of uncoded OFDM by convolution coding Research Group Department of Electronics scheme University of York, UK, 10th Oct FUTURE SCOPE The above system can be implemented in VLSI platform with the formulation of Turbo codes. And the system can be investigated with multipath channel effects. Further improvement on the performance has been achieved by applying turbo coding to MIMO-OFDM system. REFERENCES 1. Dhiraj G. Agrawal1, Roma K. Paliwal2, Priti Subramanium3, Effect of Turbo Coding on OFDM Transmission to Improve BER, International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 2, Issue B.BALAJI NAIK, PERFORMANCE OF TURBO CODED OFDM IN WIRELESS APPLICATION, National Institute of Technology Rourkela 3. 1M. K. GUPTA, 2VISHWAS SHARMA, TO IMPROVE BIT ERROR RATE OF TURBO CODED OFDM TRANSMISSION OVER NOISY CHANNEL, Journal of Theoretical and Applied Information Technology. 4. Ramjee Prasad, OFDM for Wireless Communications systems, Artech House Publishers, L. Hanzo, M. Munster, B.J. Choi, T. Keller, 6. OFDM & MC-CDMA for Broadband Multiuser Communications, WLANs and Broadcasting John Wiley Publishers, John G. Proakis, Masoud Salehi, communication system using MATLAB Thomson Asia Pvt. Ltd., Singapore, W. J. Blackert, E. K. Hall, and S. G. Wilson, Turbo Code Termination and Interleaver Conditions, IEE Electronics Letters, vol. 31, no. 24, pp , Nov T. A. Summers and S. G. Wilson, SNR Mismatch and Online Estimation in Turbo Decoding, IEEE Trans. On Communications, vol. 46, no. 4, pp , April A. G. Burr, G. P. White, Performance of G.BRINDHA is presently Assistant professor of Electronics and communication Engineering in Lord Jegannath College of Engineering And Technology, under Anna University. She has been engaged in teaching in Advanced Digital Signal Processing & Antennas and Wave Propagation. After her graduation from Anna University, Chennai in 2009 with honors, she has obtained her M.E (Applied Electronics) from the same university in 2011 with first class. She is an enthusiast in the areas of Wireless Communication, Mobile Communication, CDMA systems and Digital Signal Processing.