INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 9 September 2016

IJTC201609009 www. ijtc.org 480

Implementation of Turbo Codes in MIMO –OFDM

system for 4G applications Satinderpal Singh, Gurinder Kaur Sodhi

Research Scholar, Assistance Processor

spcheema@icloud.com, sodhigurinder123@gmail.com

Abstract: Channel Equalisation at the transmitter refers to pre-distorting the input signal so that the effect of the channel is

nullified during transmission. In any communication system, the emphasis is on estimating the channel response so as to retrieve

the transmitted input signal accurately at the receiver’s end. Orthogonal frequency division multiplexing (OFDM) is an efficient

multi-carrier modulation technique which can be combined with transmitter and receiver diversity communication systems. For

these systems, channel estimation and tracking must be performed since the receiver requires channel state information for

decoding. In this thesis, we have compared the 4QAM, 16QAM and 32QAM using the BER Values. Simulation results show that

the Bit Error Rate (BER) performance of the system is identical with that of the effect of noise, when this technique is

implemented for basic modulation schemes. Whereas, when the technique is implemented for Multiple Input Multiple Output

(MIMO) system, or a Multiple Input Multiple Output (MIMO) system with Orthogonal Frequency Division Multiplexing (OFDM)

modulation, it shows a better Bit Error Rate (BER) performance than that of the usual way of channel equalization in the

respective systems.

Keywords: Multiple Input Multiple Output (MIMO), Bit Error Rate (BER)

I. INTRODUCTION

Wireless communication is the use of EM waves to transfer

data between two users. Wireless communications has

developed into a key element of modern society. From

satellite transmission, radio and television broadcasting to

the now ubiquitous mobile telephone, wireless

communications has revolutionized the way societies

function. It has many advantages over the earlier successful

wired communication: These are its portability, flexibility

and coverage. Portability implies the freedom a hand-held

device like a cell phone offers the user. Flexibility implies

the ability to add/remove devices into existing networks

without any changes in hardware. Technologies such as

cellular radio enable users to move over a large area

providing them coverage.

OFDM transforms a frequency selective channel into a

large set of individual frequency non-selective narrowband

channels, which is suited for a MIMO structure that requires

a frequency non-selective characteristic at each channel

when the transmission rate is high enough to make the

whole channel frequency selective. Therefore, a

Fig 1.1 MIMO OFDM SYSTEM

MIMO system employing OFDM, denoted MIMO-OFDM,

is able to achieve high spectral efficiency.

However, the adoption of multiple antenna elements at the

transmitter for spatial transmission results in a superposition

of multiple transmitted signals at the receiver weighted by

their corresponding multipath channels and makes the

reception more difficult. This imposes a real challenge on

how to design a practical system that can offer a true

spectral efficiency improvement. If the channel is frequency

selective, the received signals are distorted by ISI, which

makes the detection of transmitted signals difficult. OFDM

has emerged as one of most efficient ways to remove such

ISI. However, this leads to inefficient use of the available

spectrum. Hence, we go for OFDM. A multicarrier

communication system with orthogonal sub-carriers is

called Orthogonal Frequency Division Multiplex (OFDM)

system. The word “orthogonal” indicates that there is a

precise mathematical relationship between the frequencies

of the carriers in the system. The basic principle of OFDM

is to split a high-data-rate sequence into a number of low-

rate sequences that are transmitted simultaneously over a

number of subcarriers. Because the symbol duration is

increased for the low rate parallel subcarriers, the relative

amount of dispersion in time caused by multipath delay

spread is decreased.

II. MIMO-OFDM ADVANTAGES

Favourable Properties: OFDM receiver does not need to

constantly adapt an equalizer as a single carrier system

would. OFDM system shows much favourable properties

such as high spectral efficiency, robustness to channel

fading, immunity to impulse interference, capability of

handling very strong echoes (multipath fading).

Implementation Complexity: OFDM implementation

complexity is significantly lower than that of a single-

carrier system with an equalizer.

Enhanced Capacity: In relatively slow time-varying

channels, it is possible to enhance capacity significantly by

adapting the data rate per SC according to the signal-to-

noise ratio (SNR) of that particular SC.

Robust against Interference: OFDM is robust against

narrowband interference because such interference affects

only a small percentage of the SCs.

IJTC.O

RG

INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 9 September 2016

IJTC201609009 www. ijtc.org 481

Fig 1.2 Turbo coding

Broadcasting Applications: OFDM makes single-frequency

networks possible, which is especially attractive for

broadcasting applications.

Principle of Turbo Coding

At the transmitter, the data stream is first encoded and

punctured with a coding rate R c . The turbo encoder is

constituted by a parallel concatenation of two recursive

systematic convolutional encoders separated by an inter

leaver. The first encoder processes the original data while

the second processes the interleaved version of data.

Y=Hs+n

where H is an N×M channel matrix, assumed to be perfectly

known at the receiver, with independent elements h ij of

zero mean and unit variance complex Gaussian random

variables; n=[n 1,n 2,...,n N ] T is an independent and

identically distributed (i.i.d.) additive white Gaussian noise

(AWGN) vector with zero mean and variance (N 0 = ).

III. LITERATURE SURVEY

Mr. Bhavin Gamit etal [1] Performance analysis of 2x2

MIMO for OFDM-DSSS based wireless system. This paper

includes comparison of performances of MIMO-OFDM-

DSSS system with ZF and MMSE equalizer on the basis of

BER using different modulation techniques in a scattering

environment .MMSE give best performance of BER v/s

SNR for 16QAM modulation technique

H.S. Shwetha etal [2] the performance analysis of MIMO

OFDM system with different M-QAM modulation and

convolution channel coding. In this, a good performance in

terms of low BER is achieved with the use of better channel

coding technique and modulation scheme. Convolution

coding scheme under Rayleigh multipath fading channel

improve performance with less signal-to-noise ratio (SNR).

Priyanka Dahiya etal [3] Turbo coded MIMO- OFDM

systems. Describe MIMO OFDM wireless communication

system with compressed technique i.e. turbo code. Found

better performance of MIMO OFDM system using turbo

codes approach with higher modulation order.

Anshu Jaiswal etal [4] This paper presents Channel

estimation in STTC for OFDM using MIMO with 4G

system. The goal is to reach 10ˉ ⁵ BER and high SNR to

evaluate the performance of system based on PSK

modulation technique over fast channel estimation. STTC &

viterbi algorithm give better results of BER than least mean

square algorithm

Mr. Khan Mustafa Nadeem etal [5] Increasing of channel

carrying capacity in 4G mobile communication using

MIMO-OFDM. In this paper, MIMO OFDM baseband

transceiver is implemented on an FPGA by proper selection

of one of sixteen configurations to fulfil the need of faster

data transmission on wireless communication system. Based

on more flexible properties, FPGA can be easily

reconfigured by the base station to ever changing future

demands.

Pravin K. Patil etal [6] Role of contributing factors

MIMO-OFDM in 4G-LTE wireless transmission

technologies from technical perspective. In this article BER

is calculated and measured w.r.t. SNR. The higher SNR at

receiver enabled by MIMO, along with OFDM which

provide improved coverage and throughput, especially in

dense urban areas.

Hardeep Singh et al [9] Channel State Information

Estimation In MIMO-OFDM Wireless Systems. In this

article, the improvement aspects for techniques of channel

estimation are discussed. In this paper they presented the

techniques used for channel state information estimation in

MIMO-OFDM systems. The techniques discussed are based

on training sequence based channel estimation. While

observing the CE algorithms we conclude that MMSE

algorithm outperforms LS algorithm. But the former has a

disadvantage as it is more complex than LS. So LS is to be

preferred if complexity is not desirable at the receiver while

MMSE is to be preferred if complexity is not an issue.

These are the simplest estimators and various other variants

are also available like Low Rank Minimum Mean Square

Error (LMMSE) which is a low rank MMSE. Though

performance is similar to MMSE but complexity is reduced

to great extent.

IV. TECHNIQUES USED

K-Best decoder:- K-Best decoder is a breath-first search-

based algorithm. Starting from the root node at level M+1

with d M+1=0, K-Best decoder expands each of the K

survival paths to all possible children nodes in the

constellation and computes their corresponding partial

Euclidean distances. Then, the K-Best decoder sorts all

distances and keeps only the K nodes with minimum

Euclidean distances until reaching the leaf nodes as

illustrated in Figure.

IJTC.O

RG

INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 9 September 2016

IJTC201609009 www. ijtc.org 482

Fig 1.3 K- Best Algorithms

The candidate with the minimum Euclidean distance is

chosen as an approximate of the ML solution. Whereas, a

list of the most likely candidates is retained in the case of

iterative receiver. We note that the candidate list does not

necessarily correspond to the lowest Euclidean distance.

Result

Fig 1.4 Clustering of the node

The nodes are place in the network

Fig 1.5 MIMO OFDM Graph

After implementing the OFDM technique the graph is

shown as above.

Fig 1.6 All QAM Techniques

All techniques are implemented in above graph.

Fig 1.7 Throughput

The throughput of all the techniques is shown as above

V. CONCLUSION

Pre-distorting the data symbols at the transmitter end using

an adaptive equalization filter is an effective technique

proposed for communication systems. This model ensures

considerable reduction in receiver complexity. The

MATLAB simulation results show considerable

improvement in BER performance for a MIMO- OFDM

system. The receiver detects the incoming symbols with

basic minimum distance algorithm, as the channel

equalisation is carried out at transmitter end itself thereby

reducing the receiver complexity. This technique is well

suited for multi-receiver communication system in a slow-

fading, „mirror‟ channel environment.

VI. REFERENCES

[1] John G. Proakis, Masoud Salehi, “Communication

Systems Engineering” Pearson Education International,

2nd

Edition, 2015.

[2] Mari Kobayashi, Joseph Boutros, and Giuseppe

Caire,”Successive Interference Cancellation with SISO

Decoding and EM Channel Estimation”, IEEE Journal

on selected areas in Communications, Vol. 19, No. 8,

August 2014.

[3] Ramjee Y. Lee, and W.R. Wu, “Adaptive Channel aided

Decision feedback Equalisation for SISO and MIMO

IJTC.O

RG

INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 9 September 2016

IJTC201609009 www. ijtc.org 483

systems”, IEEE Proc.- Commun., Vol. 153, No.5,

October 2006.

[4] Arogyaswami Paulraj, Rohit Nabar, Dhananjay Gore,

“Introduction to Space-Time Wireless

Communication”, Cambridge University Press, 1st

Edition, 2013.

[5] Dieter Schanfhuber, Gerald Matz, and Franz Hlawatsch,

“Adaptive Prediction of Time-Varying Channel For

Coded OFDM Systems”, Proc. IEEE ICASSP-

2002,Orlando (FL), May 2002, pp. 2549-2552

[6] Jungsub Byun, Nirmal Pratheep Natarajan, “Adaptive

Pilot Utilization for OFDM channel Estimation in a

Time Varying Channel”, Wireless and Microwave

Technology Conference, Clearwater (FL), August

2009, pp.1-5.

[7] G.J.Foshini and M.J Gans, “On the limits of wireless

communications in a Fading Environment when using

Multiple Antennas”, Wireless Personal

Communications, Ver.6, no. 3,pp.311-355, March

2014.

[8] E. Telatar, “Capacity of the multi antenna Gaussian

channels,” Eur. Trans. Telecommun., Vol.10, No. 6, pp.

585-595, Nov/ Dec. 2011.

[9] D. Gesbert et al., “From Theory to Practise: An

Overview of MIMO Space-Time Coded Wireless

Systems”, IEEE Journal on Selected Areas in

Communication, Vol. 21, No. 3, pp 281-302, April

2003.

[10] Schwartz, Bennett and Stein, Communication Systems

and Techniques, McGraw Hill, 2008, Chapter 10-11

[11] J.H.Winters, J.Salz, R.D.Gitlin, “The Impact of

Antenna Diversity on the Capacity of Wireless

Communication Systems”, IEEE Transactions on

Communications, Vol.42, No.2, pp.1740-1751, April

2006.

IJTC.O

RG