Adaptive Coding Technique To Improve Ber

Published Date: 02 Nov 2017

Abstract— Orthogonal Frequency Division Multiplexing (OFDM) has been successfully applied to a wide variety of digital communication applications over the past several years. OFDM is a suitable candidate for high data rate transmission with forward error correction (FEC) methods over wireless channels. OFDM is a suitable candidate for high data rate transmission with forward error correction (FEC) methods over wireless channels. In this project, the system throughput of a working OFDM system has been enhanced by adding turbo coding. The use of turbo coding and power allocation in OFDM is useful to the desired performance at higher data rates. Simulation is done over additive white Gaussian noise (AWGN) and impulsive noise (which is produced in broadband transmission) channels.

Keywords— Turbo codes, bit error rate, OFDM, AWGN, Bit Error rate, orthogonal frequency division multiplexing, Signal to Noise Ratio.

I. Introduction

With the rapid growth of digital communication in recent years, the need for high speed data transmission is increased. Moreover, future wireless systems are expected to support a wide range of services which includes video, data and voice. One way to transmit this data rate information is to employ well-known conventional single carrier systems. Wireless technologies are the veritable explosions in telecommunication industries. 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 (FEC) techniques. Orthogonal Frequency Division Multiplexing (OFDM) is the standard being used throughout the world to achieve the high data rates necessary for data intensive applications that must now become routine. A particularly attractive feature of OFDM systems is that they are capable of operating without a classic equalizer, when communicating over depressive transmission media, such as wireless channels, while conveniently accommodation the time- and frequency-domain channel quality fluctuations of the wireless channel.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) has grown to a popular communication technique for high speed communication in the last decade. Being an important member of the multicarrier modulation (MC) techniques, Orthogonal Frequency Division Multiplexing (OFDM), is also called Discrete Multitone Modulation DMT) [2]. It is based upon the principle of frequency division multiplexing (FDM) where each frequency channel is modulated with simpler modulation scheme. It splits a high rate data stream into a number of lower rate streams that are transmitter simultaneously over a number of orthogonal subcarriers. Although OFDM principles have been developed over several decades, its implementation for high data rate communications has only recently become popular by the reduced cost and availability of suitable signal processing components which make it a competitive technology for commercial applications also.

For a fading channel, adaptive modulation results in an improvement of 12 - 16 dB in the Signal to Noise Ratio (SNR) required to maintain a given BER, as compared with fixed modulation. Adaptive user allocation exploits the difference in frequency selective fading between users, to optimise user subcarrier allocation. In a multipath environment the fading experienced on each subcarrier varies from user to user, thus by utilising user/subcarrier combinations that suffer the least fading, the overall performance is maximised. Adaptive user allocation results in an additional average signal power improvement of 3 - 5 dB.

1.1 Evolution of OFDM

The evolution of OFDM [2] can be divided into three parts. These consist of Frequency Division Multiplexing (FDM), Multicarrier Communication (MC) and Orthogonal Frequency Division Multiplexing.

1.1.1 Frequency Division Multiplexing (FDM)

Frequency Division Multiplexing (FDM) has been used for a long time to carry more than one signal over a telephone line. FDM is the concept of using different frequency channels to carry the information of different users. Each channel is identified by the central frequency of transmission. To ensure that the signal of one channel does not overlap with thesignal from an adjacent one, some gap or guard band is left between different channels. This guard band leads to inefficiencies which were exaggerated in the early days since the lack of digital filtering made it difficult to filter closely packed adjacent channels.

1.1.2 Multicarrier Communication (MC)

Multicarrier (MC) is actually the concept of splitting a signal into a number of signals, modulating each of these new signals over its own frequency channels; multiplexing these different frequency channels together in an FDM manner; feeding the received signal via a receiving antenna into a de-multiplexer that feeds the different frequency channels to different receivers and combining the data output of the receivers to form the received signal.

1.1.3 Orthogonal Frequency Division Multiplexing

OFDM is derived from the concept of MC where the different carriers are orthogonal to each other. Orthogonal in this respect means that the signals are totally independent. It is achieved by ensuring that the carriers are placed exactly at the nulls in the modulation spectra of each other.

1.2 Orthogonal Frequency Division Multiplexing Technology

In OFDM system, the bit stream that is to be transmitted is split into several parallel bit streams. The available frequency spectrum is divided into sub-channels and each low rate bit stream is transmitted over one sub channel by modulating a subcarrier using a standard modulation scheme, for example; PSK, QAM. The subcarrier frequencies are chosen so that the modulated data streams are orthogonal to each other, ensuring that cross talk between the sub-channels is eliminated. Channel equalization is simplified by using many slowly modulated narrowband signals instead of one fastly modulated wideband signal. The primary advantage of OFDM is its ability to cope up with severe channel conditions, for example multipath and narrowband interference without complex equalization filters. The performance of OFDM system depends on several factors, such as the modulation schemes used, the amount of multipath, and the level of noise in the signal.

The performance of a single carrier transmission will degrade rapidly in the presence of multipath. Before equalizers are developed parallel transmission scheme was preferred for achieving high data rate despite its bandwidth inefficiency and high cost due to several modulators and demodulators.

Orthogonal Frequency Division Multiplexing (OFDM) has become a popular modulation method in high speed wireless communications. By partitioning a wideband fading channel into flat narrowband channels, OFDM is able to mitigate the detrimental effects of multipath fading using a simple one-tap equalizer. There is a growing need to quickly transmit information wirelessly and accurately. OFDM is a suitable candidate for high data rate transmission with forward error correction (FEC) methods over wireless channels. In this project the system throughput of a working OFDM system has been enhanced by adding turbo coding. The use of turbo coding and power allocation in OFDM is useful to the desired performance at higher data rates. Simulation is done over additive white Gaussian noise (AWGN) and impulsive noise (which is produced in broadband transmission) channels. The wideband system has 48 data sub-channels; each is individually modulated according to channel state information acquired during the previous burst. This project is to increase the system throughput while maintaining system performance under a desired bit error rate (BER). To improve the performance of the uncoded OFDM signal by convolution coding.

For the most part, Orthogonal Frequency Division Multiplexing (OFDM) is the standard being used throughout the world to achieve the high data rates necessary for data intensive applications that must now become routine. This project enhances the throughput of an existing OFDM system by implementing adaptive modulation and turbo coding. The new system guarantees to reach a target performance BER of 10-2 over a slow time-varying fading channel. The system automatically switches from lower to higher modulation schemes on individual subcarriers, depending on the state of the quasi-stationary channel. In conjunction with the adaptive design, forward error correction is performed by using turbo codes. The combination of parallel concatenation and recursive decoding allows these codes to achieve near Shannon’s limit performance in the turbo cliff region [6].

This project consists of simulation in MATLAB programming and the BER (Bit Error Rate)

II. Analysis Of Turbo CodeD OFDM

The majority of existing papers treating the TCOFDM assumes that the channel estimation using only by the pilot symbols is sufficient (or even that the channel is perfectly known). 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 time- selective [1].

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 fades in the frequency response of the channel cause some groups of subcarriers to be less reliable than other groups and hence cause bit errors to occur in bursts rather than, independently . The burst errors can extensively degrade the performance of coding. To solve this problem several ways are considered. The easiest method is to use stronger codes, in fact an interleaving technique along with coding can guarantee the independence among errors by affecting randomly scattered errors. We use turbo code to improve the performance. For analysis of the OFDM system, First we examine uncoded situation and then we will analyze the effect of coding under turbo coded OFDM condition [6].

As mentioned before, bursty errors deteriorate the performance of the any communications system. The burst errors can happen either by impulsive noise or by deep frequency fades. Powerline channels suffer from both of these deficiencies. "Figure 2.1" shows the performance of uncoded OFDM system with AWGN and impulsive noise (which is modeled as marcov noise). In this "figure 2.1" it is shown that, for the required BER 10-3 AWGN channel gives better performance as compared with marcov channel. AWGN gives a gain of approximately 22 db over marcov channel. We observe a little gain at lower SNR between 0 to <10dB, and more gain at higher SNR < 40dB[1].

To improve the performance of this system FEC code can be used. Convolutional code is a good example of FEC code. It is shown in "Figure 2.2"that Convolutional coding in OFDM can give performance improvement of some 5 db on AWGN channel over the uncoded OFDM system at required BER. Here the convolutional codes are based on the rate ½, constraint length 3 and (7, 5) generators matrix convolutional code.

III. SIMULATION MODEL

For simulation purposes, we based our work on the simulation tool provided online in [9]. It’s a complete OFDM WLAN physical layer simulation in MATLAB. The program simulates a 64 subcarrier OFDM system. The system supports up to 2 transmit and 2 receive antennas, a convolutional code generator with rates 1/2, 2/3, and 3/4. The code is punctured to IEEE specifications. As an option, one can chose to interleave the transmit bits for added protection. The system supports 4 modulation schemes, binary phase shift keying, quadrature phase shift keying, sixteen quadrature amplitude modulation, and sixty four quadrature amplitude modulation. Frequency jitter can also be added to a system that supports two channel models, namely additive white Gaussian noise, AWGN and flat Rayleigh fading. One can input the desired length of the delay spread. The cyclic prefix is 16 samples long. You can also request a specific average signal to noise ratio. Transmit power amplifier effects and phase noise distortion can be added to the transmit signal. The simulator also comes with a series of synchronization algorithms including packet detection, fine time synchronization, frequency synchronization, pilot phase tracking, channel estimation, all of that if you wish to simulate IEEE 802.11 standards. There is also a switch to add a receiver timing offset. We drastically modified the simulator to study the aspects relevant to the scope of our project.

During the simulations, in order compare the results, the same random messages were generated. For their radiant function is in MATLAB.

3.1 Simulation Parameters

During the simulations, in order compare the results, the same random messages were generated. For that radiant function is in MATLAB.

Table :- Simulation parameters

Parameters

Values

Digital Modulation

BPSK, QPSK, PSK.

Turbo code rates

1/2 , 2/3, 3/4, 5/6, 7/8.

Decoding algorithm

Log MAP.

Code Generator

{111, 10 1}

IFFT

64.

Channel

AWGN.

3.2 Uncoded OFDM

3.3 Turbo code-2/3 rate.

3.4 Turbo code-7/8 rate.

IV. APPLICATIONS

Orthogonal frequency division multiplexing (OFDM) is a multi carrier modulation technique well suited to overcome adverse effects in hostile transmission environment. This technique provides a reliable reception of signals affected by multipath propagation and selective fading and has been used in broadcast media such as European terrestrial digital video broadcasting (DVB-T) and digital audio broadcasting (DAB) and in the IEEE 802.11a(local area network, LAN) and the IEEE 802. 16 a (metropolitan area network, MAN) standards.

OFDM is also being pursued for dedicated short-range communications (DSRC) for roadside to vehicle communications.

With the advent of next generation (4G) broadband wireless communications, the combination of multiple-input multiple-output (MIMO) wireless technology with orthogonal frequency division multiplexing (OFDM) has been recognized as one of the most promising techniques to achieve high data rate and provide more reliable reception compared with the traditional single antenna system.

IV. FUTURE WORK

The combination of turbo codes and adaptive OFDM can be powerful. However, a complete coded, adaptive system would include a few more wrinkles. First the system we implemented can be enhanced by improving the MAP implementation from max-log-map to log-map. Such changes would only require minimal changes to the MAP decoder modules. We believe that greater control over the BER fluctuations in the adaptive mode can be achieved by adding a 3 bit modulation scheme between QPSK and 16QAM. Even more control can be achieved by adding a module to vary the turbo code’s rate and puncturing patterns such that multiple data rates can be achieved using the same modulation scheme (i.e. 16QAM). Not shown in our work is the lack of utility of turbo-codes when the target BER is lower than the "error floor" of the code. In the future, it would be highly beneficial to implement a convolutional or trellis encoder that could be used when the turbo code use is no longer the better alternative. Spectrally, we could use the energy saved from carriers in the no-transmission zone to boost performance of carriers near a switching threshold for instance. Also, improving the channel estimation technique by integrating it with the turbo decoding process could yield some greater gains. Finally, to support greater user speeds, one could implement a channel predictor.

CONCLUSION

We focused our attention on turbo codes and their implementation. We tied concepts of OFDM and turbo coding with a target-based, modulation scheme. First I developed an OFDM system model then try to improve the performance by applying forward error correcting codes to our uncode system. Improvement on the performance has 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 that are suitable for speed and data applications, are analyzed.