Simulation of Error Correction Codes on Wireless Communication Systems
Chapter One
OBJECTIVES OF THE STUDY
The main objectives chosen for this thesis are highlighted as follows:
- Understand the concept of error correction in wireless communication
- Implementation of error correction codes in a noisy channel using convolution codes to compensate transmission impairment in order to increase quality of
- Creation of the experimental simulation model of a communication system in MATLAB®2008/Simulink to oversee the process of error correction
- Investigate the performance of the chosen codes (convolution codes) with different code rates.
CHAPTER TWO
LITERATURE REVIEW AND THEORETICAL BACKGROUND
LITERATURE REVIEW
Emerging wireless communication systems transmit high data rates to provide wide range of services including high quality voice, data, images and other multimedia applications. Unfortunately, wireless communication signals propagate through harsh communication channels encountering various obstacles resulting in data errors at the receiver end. Error correction codes are a means of including redundancy in a stream of information bits to allow the detection and correction of symbol errors during transmission. The approach to error correction coding taken by modern digital communication systems started in the late 1940’s with the ground breaking work of Shannon, Hamming and Golay (Valenti, 1999). The birth of error correction coding showed that Shannon’s channel capacity could be achieved when transmitting information through a noisy channel (Jemibewon, 2000).
Several researches have been carried out on error correction codes for different types of systems. In Huang (1997), the evaluation of soft output decoding for turbo codes was carried out in which the performance of turbo codes were evaluated through computer simulation. Coding theory related to this research was also studied, including convolution encoding and Viterbi decoding.
Jemibewon (2000), studied the smart implementation of turbo decoding for improved power efficiency in which the technical feasibility of adapting the resolution of analog-to-digital converter (ADC) in a decoder and the number of decoding iterations in order to obtain the best trade-off between BER performance and power dissipation in a communication system with the aid of computer simulations
Thomas et al. (2003) presented an article on integrated circuits for channel coding in third generation (3G) cellular mobile wireless systems. The article considered decoder design problems and using case studies described 3G decoder solutions using application-specific integrated circuits (ASIC).
Source and Channel Coding Techniques necessary for reliable transmission of Digital Mammograms over a wireless AWGN channel was investigated by Hosseinzadeh et al. (2004). Lossless source compression techniques were investigated. Error correction and detection mechanisms were also discussed focusing on schemes that achieve the lowest bit error rates to ensure reliable data transfer over a noisy wireless channel
Vats and Kacewicz (2005) studied error correcting codes for speech channels of cellular phones with emphasis laid on the most popular cell phone standard, Global System for Mobile communication (GSM). Simulations using MATLAB®2008 were carried out to show the power of convolution codes and interleaving done in GSM for speech signals. Codes used in the competing code division multiple access (CDMA) standard were also evaluated.
Cheng (2005), in his research investigated error protection using convolution encoder and Viterbi decoder system against insertion, deletion and substitution errors (IDS). Two independent IDS error correcting designs working on the convolution encoder and the Viterbi decoder were presented.
Balakrishnan et al. (2007) studied the performance analysis of various error control codes in terms of their BER performance and power consumption on different platforms. Error control codes with different constraints were implemented and simulated using VHSIC (Very high speed integrated circuits) hardware description language (VHDL). Implementation on field-programmable gate array (FPGA) and ASIC design were also carried out and the energy consumption was measured. The error control performance of these codes was then evaluated in terms of Bit Error Rate (BER) by transmitting randomly generated data through a Gaussian channel.
Cheong (2007) also carried out a study on block error correction codes and convolution codes. The paper gave an overview on error control coding employed in forward error correction, particularly block codes and convolution codes.
Simulation of error correction codes on wireless communication systems investigates the performance of error correcting codes for data signals used in wireless systems; cellular phones, internet, geolocation/GPS, emergency services, radio and television broadcasting e.t.c. This research work employed the use of convolution error correction codes in order to deal effectively with noise. With the use of MATLAB®2008 for simulation and analysis, the error correction performance of the convolution code (with different code rates) was evaluated in terms of Bit Error Rate (BER) by transmitting randomly generated data through a Gaussian channel. Bit-error-rate (BER) performance and power dissipation are two important measures of performance used to characterize communication systems.
SHANNON LIMIT
In 1948, Claude Shannon proved that any communication channel could be characterized by maximum theoretical capacity, C. If the source information rate, R, is less than C (R<C), then there exist channel- encoding method such that information can be reliably transmitted. This theorem set a theoretical limit on possible information rate for achieving reliable (error-free) transmission through the channel by appropriate coding. On the other hand, if R>C, reliable transmission is not possible regardless of the amount of signal processing performed at the transmitter and receiver. Thus if the required transmission rate R (measured in bits per second) is less than C, it is possible to design a communication system for that channel and with the help of error-control coding, a very small probability of output error can be achieved for that channel. The capacity C in bits per second (b/s) depends on only two parameters, the channel bandwidth and the signal-to-noise ratio. In practice, it has proved to be remarkably difficult to find classes of constructive codes that can be decoded by feasible decoding algorithms at rates, which come at all close to the Shannon limit (Arasteh, 2006).
CHAPTER THREE
DESIGN METHODOLOGY
INTRODUCTION
In this research, a strategy to present convolution codes used for error correction in wireless communication systems was introduced, without using mathematical structures. Methods used in the study are explained in the following sections:
IMPLEMENTATION OF CODES
The convolution error correction code is implemented by simulations in MATLAB®2008. Figure 3.1 illustrates the procedure for encoding and decoding in a communication system, where u is the information word, v is the codeword, v’ is the received word and u’ is the decoded word.
CHAPTER FOUR
RESULTS AND ANALYSIS
SIMULATION RESULTS
Computer simulations were used in this research to evaluate the performance of the error correction scheme presented in the last three chapters.
Simulation results of the error correction using convolution codes are based on Bit Error Rate (BER) performance over a range of signal-to-noise ratios (Eb/No). The error rates of the received data were computed by comparing it to a delayed version of the transmitted data. This comparison (as in Table 4.1) was done for the uncoded system and two different codes:
- Rate 1/2code
- Rate 1/3code
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
INTRODUCTION
Error correction coding is a key element of any digital wireless communication system, minimizing the effects of noise and interference on the transmitted signal at the physical layer. Error prone communications systems are unreliable, unreliable services are often discarded. There is therefore the need to improve the reliability of such services. Since it is practically impossible to build all potential designs to test and compare performances, it is best to obtain mathematical models that describe the behavior of different parts of the considered systems, run computer simulations to obtain estimates of the results. Computer simulations use mathematical models to imitate the functionality of a real process. They are also used to compare the performance of alternative designs before implementation.
SUMMARY
The research carried out in this work provides a simulating tool that outlines efficiently error correction in wireless communication systems with the use of convolution codes. Convolution code is widely used in digital wireless communication system for detecting and correcting errors in received signal message bits. Simulations for two rates of convolution codes have been presented and the bit error rate for each type was determined.
LIMITATIONS
Due to low frequency of errors as Eb/No increases the amount of time it will take to gather enough data to accurately measure the BER is monumental. Hence wise estimation and extrapolation techniques need to be used to make a precise estimation of BER in a short duration. Other concerns include computational complexity, encoding and decoding delays. Longer decoding depths do not provide significant BER improvement. Accurate characterization by Monte Carlo simulation at very low BER e.g. less than 10−10, is not computationally feasible.
CONCLUSION
With the detailed description of the convolution code encoder and decoder presented in Chapters 2 and 3 respectively, the performance of convolution codes was investigated through extensive computer simulation. To validate the convolution codes simulation, comparisons were made between the coded and uncoded bit error rate (BER) results. Based on the results of the graphs, it is evident that there was
-2
a 70% and 74% improvement on coding gains between reference points of 10
-4
and 10
for the two code
rates used. This improvement can be attributed to the introduction of the convolution codes. In general, it is observed that the introduction of the convolution coding scheme has helped to decrease the Bit Error Rate significantly which in turn will result in the transmission of signals of specified quality with a smaller transmit power. In other words, it leads to higher power efficiency, but on the other hand, the bit rate is half (or third) that of the uncoded scheme thus lowering bandwidth efficiency. It can thus be concluded that a coded system offers better channel efficiency than the uncoded system.
RECOMMENDATIONS FOR FURTHER WORK
The following are suggested for further work:
- Implementation of other versions of error correction codes in order to meet different design requirements can improve this work. This will involve investigation of BER performance for other coding schemes e.g. Block codes; repetition codes, parity codes n-redundancy codes e.t.c, using other code rates and using real time data such as images, voice and so on to validate the accuracy and authenticity of the developed correction
- Hardware Implementation of the coding scheme used in this research using real data is also another area that needs further investigation. This can be achieved by implementing the “real-life” part of the thesis; building a real-life communication system to verify the functionality and the results obtained through the simulated
- Additional MATLAB®2008 functions can be created to process various types of applications (images, speech, texts, and video e.t.c) in place of randomly generated signals, however some applications may require larger processing
REFERENCES
- Arasteh D. (2006). Teaching Convolution Coding Using MATLAB®2008 in Communication Systems. Proceedings of the ASEE Gulf South-west Annual Conference Southern University, Baton Rouge, LA USA.
- Asif S, Abdullah S.M and Anisul Islam, A.M. (2009). Comparison of BER between Uncoded Signal and Coded Signal (Using Convolution Code) over Slow Rayleigh Fading Channel. Journal of Theoretical and Applied Information Technology 2005-2009. Through: http://www.jatit.org
- Babale, S.A. (2010). Modeling and Analysis of System Capacity against Radio Frequency Impairment. Unpublished M.Sc Thesis. Department of Electrical and computer Engineering, Ahmadu Bello University, Zaria, December, 2011.
- Balakrishnan G, Yang M, Jiang Y and Kim Y. (2007). Performance analysis of error control codes for wireless sensor networks. Paper presented at the Information Technology (ITNG 2007) 4th International Conference. Retrieved 3rd October, 2009. Pp 876-879.
- Bystrom, M. and Modestino, W. (1997). Combined source-channel coding schemes for video transmission over an additive white Gaussian noise channel. Journal of Center for Image
- Processing Research, Rensselaer Polytechnic Institute, Troy, NY 1218.
