STBC Based MIMO System using Moving Average Filter and 2-PSK Modulation Scheme

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 3, March 2015)

619 STBC Based MIMO System using Moving Average Filter and

2-PSK Modulation Scheme

Gaurav Maurya

1,

Prof. Pramod Patel

2

1_{M-Tech Research Scholar,}2_{Research Guide, TIT College Bhopal}

Abstract - The wireless communication system is a largely used in the present era of communication. The enhancements in the technology to make more reliable end to end connectivity and transfer of information lots of research being executed. The multiple input multiple output (MIMO) technique significantly improve the quality of communication. In this paper a new approach is proposed to make communication system more better. The system is MIMO based on space time block coding (STBC). Here the system is evaluated with QAM modulation and PSK Modulation. To make bit error rate i.e. figure of merit minimized additionally moving average filtering is used, and it can be seen that using PSK BER is better than QAM modulation.

Keywords - BER, MIMO, QAM, PSK, MAF and STBC.

I. INTRODUCTION

Demands for the higher transmission rates in an unfailing way are increased as wireless networks start to offer video and voice transmission in addition to the data transmission. Thus, recently, next-generation wireless networks have emerged to offer higher transmission rates with less transmission errors through the use of multiple antennas. Multiple antenna systems increase the reliability and spectral efficiency of the system through the use of diversity techniques and SM scheme, respectively. Diversity techniques are widely used to reduce the effect of multi-path fading. The probability of all the replicas of the same information symbol experiencing the same fading decreases as the number of diversity branches increase. MIMO systems introduce a spatial dimension to existing rate adaptation algorithms that implies to decide MIMO transmission type, STBC, spatial multiplexing or hybrid approaches, as well as modulation and coding type. However, in MIMO systems, correlations may occur between channel coefficients due to insufficient antenna spacing and the scattering properties of the transmission environment. This may lead to significant degradation in system performance.

The use of multiple antennas both at the transmitter and the receiver, which is commonly referred as MIMO, is a popular research area in wireless communications literature because of its reliability and spectral efficiency.

With the growth of applications that demand better quality of services, higher throughput and bandwidth, MIMO communication has emerged as a promising technology. The ideas behind the MIMO communication are either creating a multiple data pipes to increase the data rate and/or adding diversity to improve the reliability. The former idea is achieved through use of SM technique [3], which offers multiplexing gain, with effective detection algorithms at the receiver. However the latter idea is achieved with STBC schemes introduced in [1, 18].

II. SPACE-TIME CODES

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The turbo principle has been successfully used in lots of detection and decoding problems such as serial concatenation, equalization, multi-user detection, coded modulation, joint interference suppression and decoding.

Space-time Block Codes:

Space-time block codes (STBC) are a generalized version of Alamouti scheme [18] [19][21]. These schemes have the same key features. As a result, these codes are orthogonal and can achieve full transmit diversity specified by the number of transmit antennas. In another word, space-time block codes are a complex version of Alamouti’s space-time code in [17], where the encoding and decoding schemes are the same as there in the Alamouti space-time code in both the transmitter and receiver sides. The data sets are constructed as a matrix which has its rows equal to the number of the transmit antennas and its columns equal to the number of the time slots required to transmit the data set. At the receiver end, when signals are received, they are initially combined and then sent to the maximum likelihood detector where the decision rules are applied. Space-time block codes were designed to achieve the maximum diversity order for the given number of transmit and receive antennas subject to the constraint of having a simple linear decoding method. This has made space-time block codes a very novel scheme and most widely used. Space-time block codes and indeed many other space-time techniques including STTCs are designed for coherent detection where channel estimation is necessary.

There is a substantial literature addressing the channel estimation issue for multiple-input multiple-output (MIMO) systems, ranging from standard training based techniques that rely on pilot symbols [22] [23] [24] in the

data stream to blind [25] [26] which does not require pilot sequences and semi-blind [27] estimation where observations corresponding to data and pilot are used jointly. Other authors have considered non-coherent detection schemes based on differential encoding which do not require channel state information (CSI) [21] [28]. Although these methods avoid the need for channel estimation, they often suffer from problems such as error propagation. Training-based methods seem to give very good results on the performance of channel estimation at the receiver. Pure training-based schemes may be considered as an advantage when an accurate and reliable MIMO channel needs to be obtained. However, this could also be a disadvantage when bandwidth efficiency is required. This is because pure training-based schemes reduce the bandwidth efficiency considerably due to the use of a long training sequence which is necessarily needed in order to obtain a reliable MIMO channel estimate.

III. PROPOSED METHODOLOGY

The advancement in the technology is a continuous process of research on small factors to improve the performance of the system. The concept of making wireless MIMO channel based system with end to end reliability of information is achievable using the space time coding techniques in addition with the efficient modulation techniques, that helps to shield the signals from unwanted noise attacks and interferences.

[image:2.612.58.540.531.607.2]

In the below figure the same system is equipped with the Almouti STBC coding and PSK Modulation technique, and applying moving average filter at the receiver side to reduce the effect of distortions and interferences.

Fig. 3.1 Block diagram of proposed system

PSK / QAM

Modulation

Almouti

STBC

Coding

Transmit

Through MIMO

Channel

Adding

Noises

Recovering

from STBC

Combining

All Signals

PSK / QAM

Demodulation

Data

Input

Data

output

Moving

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Fig. 3.2 Flow chart of implemented simulation model of proposed system

The above mentioned flow chart is the step by step flow of computer algorithm implemented on simulation tool. The steps are as follows:

a. Start of simulation

b. Create an simulation environment using variables

c. Create Channel Model

d. Generate data to transfer over communication system

e. Modulate with PSK/QAM modulation scheme

f. Apply Almouti STBC Coding and power calculations

g. Transmit signal through MIMO channel

h. Generate Noises and add with signal

i. Combine Received signals

j. Remove STBC coding

k. Demodulate signal with PSK/QAM

l. Apply moving average filtering

m. Calculate BER and Compare and Display results

n. End of Simulation

The above mentioned algorithm gives the results by which the outcomes of the proposed methodology.

IV. SIMULATION RESULTS

The proposed system mentioned in the previous section simulated and the outcomes are displayed in terms of bit error rate (BER) vs signals to noise ratio (SNR). In the below figures all the simulation results analyzed wit PSK modulation and QAM modulation is displayed.

Fig. 4.1 BER performance of the MIMO system with 16, 32 and 64-QAM modulation and 10 iterations with Moving Average Filtering

In Fig. 4.1 the Almouti STBC coding based system and modulation system is QAM. The QAM modulation system is simulated with different variations e.g. 16-QAM, 32-QAM and 64-32-QAM. The whole simulation repeated for 10 times. The simulation results shows that BER performance is good for 16-QAM with moving average filter.

Start

Initialize Environmental Variables

Create Channel Models

Generate Data

PSK/QAM Modulation

Almouti STBC Coding and Power Calculation

Transmit Signal Through MIMO Channel

Generate and Add Noises

Combining Signals

Removing STBC

Moving Average Filtering

Calculate BER, Compare and Display Result

End

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[image:4.612.325.568.174.364.2]

In Fig. 4.2 the Almouti STBC coding based system and modulation scheme is QAM. The QAM modulation scheme is simulated with different variations e.g. 16-QAM, 32-QAM and 64-QAM. The whole simulation repeated for 20 times. The simulation results shows that BER performance is good for 16-QAM with moving average filter.

In Fig. 4.3 the Almouti STBC coding based system and modulation scheme is QAM. The QAM modulation scheme is simulated with different variations e.g. 16-QAM, 32-QAM and 64-QAM.

[image:4.612.50.294.447.633.2]

The whole simulation repeated for 30 times. The simulation results shows that BER performance is good for 16-QAM with moving average filter.

Fig. 4.4 BER performance of the MIMO system with 2, 4 and 8-PSK modulation and 10 iterations with Moving Average Filtering

In Fig. 4.4 the Almouti STBC coding based system and modulation scheme is QAM. The QAM modulation scheme is simulated with different variations e.g. 2-PSK, 4-PSK and 8-4-PSK. The whole simulation repeated for 10 times. The simulation results shows that BER performance is good for 2-PSK with moving average filter.

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Fig. 4.6 BER performance of the MIMO system with 2, 4 and 8-PSK modulation and 30 iterations with Moving Average Filtering

V. CONCLUSION AND FUTURE WORK

The wireless communication system with better end to end performance is proposed and implemented on simulation tool. The outcomes of the proposed methodology are explained in the previous section of the paper. From the simulation results it is clear that the system simulated with MIMO technology and the application of moving average filtering making performance far better than normal.

The better modulation approach make system more robust and the use of some efficient filtering technique will definitely enhances the system outcomes.

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