IMPLEMENTATION OF MIMO OFDM STF CODING FRAMEWORK FOR WIRELESS COMMUNICATION SYSTEM

IMPLEMENTATION OF MIMO OFDM

STF CODING FRAMEWORK FOR

WIRELESS COMMUNICATION SYSTEM

R VEERANNA SAKE POTHALAIAH Prof. K ASHOK BABU SRI INDU COLLEGE OF ENGG&TECHNOLOGY

(Affiliated to JNTU, Hyderabad) Ibrahimpatnam Hyderabad, Andhra Pradesh, India -501510

ABSTRACT

The current problems in wireless systems are multi-path propagation, frequency selective fading, inter-symbol interference and inter-carrier interference. A major challenge being transmission of information having high data rates over long distances. The Orthogonal Frequency Division Multiplexing (OFDM) systems solve a few of these problems which are mentioned above.

This Paper makes an attempt to simulate Space Time Frequency coded communication system using diversity schemes like MIMO and Multiple Input Single Output (MISO) and compares their performances over a fading channel having inherent noise.

On simulation in MATLAB 7.0.1, MIMO scheme performed better than MISO in comparison of bit error rates with respect to signal to noise ratios. Paper also compares the channel capacity obtained by various MIMO diversities as function of signal to noise (SNR) ratio and proves that as diversity increases the channel capacity increases.

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Keywords- OFDM, STF, MIMO, SISO, MISO.

I. INTRODUCTION

In wireless telecommunications, multipath is the propagation phenomenon that results in radio signals' reaching the receiving antenna by two or more paths as shown in Fig-1.1. Causes of multipath include atmospheric ducting, ionospheric reflection and refraction, and reflection from water bodies and terrestrial objects such as mountains and buildings. The effects of multipath include constructive and destructive interference, and phase shifting of the signal. Since the shape of the signal conveys the information being transmitted, the receiver will make mistakes when demodulating the signal's information. If the delays caused by multipath are great enough, bit errors in the packet will occur. The receiver won't be able to distinguish the symbols and interpret the corresponding bits correctly.

1.1 Inter Symbol Interference

Fig-1.2: Illustration of Inter Symbol Interfere

1.2 Frequency Selective Fading

In wireless communications, fading is deviation of the attenuation that a carrier-modulated telecommunication signal experiences over certain propagation media. The fading may vary with time, geographical position and/or radio frequency, and is often modelled as a random process. A fading channel is a communication channel that experiences fading. Selective fading or frequency selective fading is a radio propagation anomaly caused by partial cancellation of a radio signal by itself — the signal arrives at the receiver by two different paths, and at least one of the paths is changing (lengthening or shortening). This typically happens in the early evening or early morning as the various layers in the ionosphere move, separate, and combine. The two paths can both be sky-wave or one be ground-wave. Selective fading manifests as a slow, cyclic disturbance; the cancellation effect, or "null", is deepest at one particular frequency, which changes constantly, sweeping through the received audio. The effect can be counteracted by applying some diversity scheme, for example OFDM (with subcarrier interleaving and forward error correction), or by using two receivers with separate antennas spaced a quarter-wavelength apart, or a specially-designed diversity receiver with two antennas. Such a receiver continuously compares the signals arriving at the two antennas and presents the better signal.

2. PRINCIPLE OF OFDM

2.1 OFDM BLOCK DIAGRAM

Fig-2.2 Block Diagram of OFDM system

2.2.1 Channel Encoder --Convolution Coding

Channel coding refers to the class of signal transformations designed to improve the communications performance by enabling the transmitted signals to be better withstand the effects of various channel impairments, such as noise interference and fading OFDM is invariably used in conjunction with channel coding (forward error correction), and almost always uses frequency and/or time interleaving.

Frequency (subcarrier) interleaving increases resistance to frequency-selective channel conditions such as fading. For example, when a part of the channel bandwidth is faded, frequency interleaving ensures that the bit errors that would result from those subcarriers in the faded part of the bandwidth are spread out in the bit-stream rather than being concentrated. Similarly, time interleaving ensures that bits that are originally close together in the bit-stream are transmitted far apart in time, thus mitigating against severe fading as would happen when travelling at high speed. However, time interleaving is of little benefit in slowly fading channels, such as for stationary reception, and frequency interleaving offers little to no benefit for narrowband channels that suffer from flat-fading (where the whole channel bandwidth is faded at the same time).

The reason why interleaving is used on OFDM is to attempt to spread the errors out in the bit-stream that is presented to the error correction decoder, because when such decoders are presented with a high concentration of errors the decoder is unable to correct all the bit errors, and a burst of uncorrected errors occurs.

A common type of error correction coding used with OFDM-based systems is convolutional coding, which is often concatenated with Reed-Solomon coding. Convolutional coding is used as the inner code and Reed-Solomon coding is used for the outer code — usually with additional interleaving (on top of the time and frequency interleaving mentioned above) in between the two layers of coding. The reason why this combination of error correction coding is used is that the Viterbi decoder used for convolutional decoding produces short errors bursts when there is a high concentration of errors, and Reed-Solomon codes are inherently well-suited to correcting bursts of errors.

2.2.2 Mapping Symbols

The primary objective of spectrally efficient modulation techniques is to maximize bandwidth efficiency. The increasing demand for digital transmission channels has led to the investigation of spectrally efficient modulation techniques to maximize bandwidth efficiency and thus help ameliorate the spectral congestion problem.

Although QPSK can be viewed as a quaternary modulation, it is easier to see it as two independently modulated quadrature carriers. With this interpretation, the even (or odd) bits are used to modulate the in-phase component of the carrier, while the odd (or even) bits are used to modulate the quadrature-phase component of the carrier. As a result, the probability of bit-error for QPSK is given by the below expression:

………..Eq(2.1)

If the signal-to-noise ratio is high (as is necessary for practical QPSK systems) the probability of symbol error may be approximated:

………...Eq(2.2)

Implementation

Writing the symbols in the constellation diagram in terms of the sine and cosine waves used to transmit them:

…………Eq(2.3) This yields the four phases π / 4, 3π / 4, 5π / 4 and 7π / 4 as needed.

This results in a two-dimensional signal space with unit basis functions

………..Eq(2.4) ………...Eq(2.5)

The first basis function is used as the in-phase component of the signal and the second as the quadrature component of the signal.

Hence, the signal constellation consists of the signal-space 4 points

The factors of 1 / 2 indicate that the total power is split equally between the two carriers.

Fig-2.4: Transmitter section of QPSK

Conceptual transmitter structure for QPSK. The binary data stream is split into the in-phase and quadrature-phase components. These are then separately modulated onto two orthogonal basis functions. In this implementation, two sinusoids are used. Afterwards, the two signals are superimposed, and the resulting signal is the QPSK signal. Note the use of polar non-return-to-zero encoding.

Fig-2.5 Receiver Structure of QPSK

Receiver structure for QPSK. The matched filters can be replaced with correlators. Each detection device uses a reference threshold value to determine whether a 1 or 0 is detected

2.2.5 Generation of sub-carriers using the IFFT

An OFDM signal consists of sum of sub carriers that are modulated by using QPSK. If di are the complex QPSK symbols, Ns is the number of subcarriers, T be the symbol duration and Face the carrier frequency, then one OFDM symbol is

Fig-2.9: Example of four subcarriers within one OFDM symbol

Simplified block diagram of OFDM modulator is shown in the fig-2.10; it can be observed that the complex base band OFDM signal is in fact nothing more than the inverse Fourier transform of Ns QPSK input symbols.

2.2.3 Convolution Decoder Using Maximum Likelihood and Viterbi Decoding

Viterbi decoding is the best known implementation of maximum likelihood decoding. It reduces the computation load by taking advantage of special structure in code trellis. The complexity of viterbi decoder is not a function of number of symbols in the code word sequence. The algorithm involves calculating a measure of similarity or distance, between the received signal, at time ti, and all trellis paths entering each state at time it. The viterbi algorithm removes from consideration those trellis paths that could not possibly be candidates for maximum likelihood choice. When two paths entering the same state, the one having the best metric is chosen. This path is called the surviving path. This selection of surviving path is performed for all states. The goal of selecting the optimum path, can be expressed equivalently, as choosing the codeword with maximum likelihood metric, or as choosing the codeword with minimum distance metric

.Fig-2.11 Final solution of correct viterbi decoding when a transmitted sequence is 1101010001 and received sequence is 1101011001

2.2.4 FFT

FFT is Fast Fourier Transform. The FFT is a faster version of the Discrete Fourier Transform (DFT). The FFT utilizes some clever algorithms to do the same thing as the DFT, but in much less time. The DFT is extremely important in the area of frequency (spectrum) analysis because it takes a discrete signal in the time domain and transforms that signal into its discrete frequency domain representation. Without a discrete-time to discrete-frequency transform its not possible to compute the Fourier transform with a microprocessor or DSP based system. FFT is with the help of Butter-fly diagram as shown in the fig-2.13.

Fig-2.13: Butterfly diagram

2.2.8 QPSK Demodulator

Inspecting the constellation, it is noted that we can mathematically express each symbol as the sum of two signals, one signal corresponding to a sine at the carrier frequency and the other corresponding to a cosine at the carrier frequency.

Fig-2.14 Decomposition of QPSK signal

The QPSK signal is decomposed into the sum of two PSKsignals, one corresponding to a sine carrier (called the

in-phasecomponent) and the other corresponding to the cosine carrier (called the quadrature component). The decomposition is shown graphically in the fig-2.14.

To demodulate it is required to electronically separate the in-phase and quadrature components of the QPSK signal; we can demodulate each component using a dedicated binary PSK receiver. The demodulated in-phase signal will give us the least significant bit of each bit pair; the demodulated quadrature signal will give us the most significant bit of each bit pair, as shown in fig-2.15.

r(t)

QPSK receiver To user (see table below) Threshold comparators and logic circuitry (see table below)



X

sin(2fct)

 nT_s z

1

 nT_s z 2



cos(2fct)

X

In-phase component is zeroed out; output contains only effects of quadrature component and noise

Quadrature component is zeroed out; output contains only effects of in-phase component and noise

Fig-2.15 QPSK Receiver

3.1 INTRODUCTION TO STF

Space Time Frequency Block (STFB) coding schemes are used to enhanced the system performance and reliability by taking advantage of diversity of space, time and frequency inherent in MIMO-OFDM system. The coding distributes symbols along transmit antennas, time slots and OFDM sub channels. A STFB codeword may occupy several OFDM symbols which can increase the diversity order.

Fig-3.1: Illustration of STF-coded Transmission

3.2 STF BLOCK DIAGRAM

Fig-3.2: STF Block Diagram

The block diagram of the Space time Frequency (STF) coded OFDM system is shown in the above fig-3.2. The data stream is encoded by a convolution encoder, followed by an inter leaver. After symbol mapping, the tones enter the STF encoder and then forwarded to the OFDM modem of the different antennas. The OFDM modem considered at each antenna consists of M subcarriers. The M tones at each antenna are passed through inverse Fast Fourier Transform blocks (IFFT). A cyclic prefix is also added to each of the resultant signals. To avoid the inter-symbol interference, the guard time is chosen to be longer than the channel delay spread. The well chosen cyclic prefix length in the OFDM multiplexing system turns in a wideband frequency selective channel in to number of parallel independent frequency non selective channels. Finally the resulting signal is up-converted to the carrier frequency and transmitted across the mobile radio channel.

TRANSMITTED AND RECEIVED IMAGES OVER THE COMMUNICATION SYSTEM

Fig 5.1: original cameraman image

The image Fig 5.1 shown above is a *.TIFF format. But only binary images are of our interest so we convert this image to binary image using dither command. The binary image is stored as a 2 dimensional array of 0s and 1s.

Fig 5.2: binary image prior t

This image (Fig 5.2) is the binary transformed image of cameraman.

The obtained image(Fig 5.3) shows the tolerance of the system to severe fading and noise.

PLOT OF OFDM SYMBOL

The OFDM symbols are plotted as function of normalized time as shown in the fig-5.4. This is prior up conversion. The various signal amplitudes of various frequency components of the symbols are observed.

Fig 5.4: The amplitudes of frequency components of OFDM symbol

FREQUENCY DOMAIN REPRESENTATION OF OFDM SYMBOLS

The frequency domain representation of OFDM symbols are obtained by taking the Fast Fourier Transform of the OFDM symbols and then plotting the magnitude and phase response of the obtained symbols as function of normalized frequency as shown in the fig-5.5.

Fig 5.5 –The frequency and phase response of the OFDM symbol

PLOT OF UPCONVERTED OFDM SYMBOL

Fig 5.6: The amplitudes of frequency components of up converted OFDM symbol in Time Domain

COMPARISON OF ST, SF, STF CODED OFDM SYSTEMS

Here the three ST coded OFDM, SF coded OFDM and STF coded OFDM systems are simulated and bit error performances are compared. From the graph we can say that the STF coded OFDM systems out performs the other two systems

Fig 5.7: Comparison of ST, SF and STF BER plot

COMPARISON of MIMO AND MISO

Fig 5.8-The Bit Error Rate plot of MIMO vs. MISO Antenna diversities as a function of Signal to Noise ratio.

COMPARISION OF CHANNEL CAPACITY

The increase in diversity of of the communication system increases the channel capacity. The fig-5.8 proves the statement. Shannon Hartleys law is used as reference law. This plot proves the MIMO with the increasing diversity is ideal for future scenario where the bandwidth per user is expected to increase.

Fig 5.9 Comparison plot of Channel Capacities of various diversity orders for a MIMO scheme

CONCLUSION

On simulation in MATLAB 7.0.1 ,MIMO scheme performed better than MISO in comparison of bit error rates with respect to signal to noise ratios. also compares the channel capacity obtained by various MIMO diversities as function of signal to noise ratio and proves that as diversity increases the channel capacity increases.

REFERENCE

[3]“Modelling of Performance with Coloured Interference Using the EESM”, Nortel Networks, 3GPP TSG-RAN WG1 #37, 2004 [4]R. Yaniv, D. Stopler, T. Kaitz, K. Blum, ”CINR Measurements Using the EESM method”, Alvarion Ltd., 2005

[5]”System-level evaluation of OFDM – further considerations”, Ericsson, 3GPPTSG-RAN WG #35, R1031303, 2003 [6]ETSI TR 101 112, UMTS 30.03, V3.1.0, Annex B,Sections 1.2.3,1.3,1.4

About Authors:

R VEERANNA, graduated from Vignan Institute of Science and Technology,

Hyderabad in Electronics & Communications Stream. Now pursuing Masters in Digital Electronics and Communication Systems (DECS) from Sri Indu College of Engineering & Technology .

S. POTHALAIAH, graduated from the Department of ECE in National Institute of Technology Warangal in 2006, he obtained his M.E. degree from the department ECE, University College of Engineering, OU in 2008. He is working as Assoc. Prof, Department of ECE, Sri Indu College of Engg. & Tech., his interests are in Ad hoc wireless networks works, Image Processing, Control System and Bio Medical Signal Processing.

I express my gratitude to Prof. K ASHOK BABU Professor & Head of the Department (ECE) of and for his constant co-operation, support and for providing necessary facilities throughout the M-tech program. He has 15 Years of Experience, at B-Tech and M-tech Level and working as a Professor in Sri Indu College of Engg.& Technology.