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CHAPTER 5

MIMO SC_FDMA WITH ICI ESTIMATION AND

SUPPRESSION

5.1 INTRODUCTION

Since past few decades different types of cellular networks were

launched and went successful on the radio links such as WiMAX, that became

very popular because of its high data rate (70Mbps) and support for providing

wireless internet services over 50km distance. The Universal Mobile

Telecommunication System(UMTS) Long Term Evolution (LTE) is an

emerging technology in the evolution of 3G cellular services. LTE runs on an

evolution of the existing UMTS infrastructure already used by over 80

percent of mobile subscribers globally. We have very limited resources in

cellular technologies and it is important to utilize them with high efficiency.

Single Carrier Frequency Division Multiple Access (SC-FDMA) &

Orthogonal Division Multiple Access (OFDMA) are major part of LTE.

OFDMA was well utilized for achieving high spectral efficiency in

communication system. SC-FDMA is introduced recently and it became

handy candidate for uplink multiple access scheme in LTE system that is a

project of Third Generation Partnership Project (3GPP). The Multiple Access

Scheme in Advanced Mobile radio system has to meet the challenging

requirements, for example high throughput, good robustness, efficient Bit

Error Rate (BER), high spectral efficiency, low delays, low computational

complexity, low Peak to Average Power Ratio (PAPR), low error probability

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etc. Error probability is playing vital role in channel estimation and there are

many ways to do channel estimation, like Wiener Channel Estimation,

Bayesian Demodulation etc.

This chapter investigates the performance of SC-FDMA and

OFDMA of LTE physical layer by considering different modulation schemes

(BPSK, QPSK, 16QAM and 64QAM) on the basis of PAPR, BER, power

spectral density (PSD) and error probability by simulating the model of SC-

FDMA & OFDMA. Additive White Gaussian Noise (AWGN) and

frequency selective (multipath) fading is introduced in the channel by using

Rayleigh Fading model to evaluate the performance in the presence of noise

and fading.

A set of conclusions is derived from our results describing the

effect of higher order modulation schemes on BER and error probability for

both OFDMA and SC-FDMA. The power spectral densities of both the

multiple access techniques (OFDMA and SC-FDMA) are calculated and

result shows that the OFDMA has high power spectral density. The

considered modulation schemes also have a significant impact on the PAPR

of both OFDMA and SC-FDMA such that the higher order modulations

increase PAPR in SC-FDMA and decrease PAPR in OFDMA. However, the

overall value of PAPR is minimum in SC-FDMA for all modulation schemes.

SC-FDMA is an OFDMA alternative technology. SC-FDMA is the

multiuser version of single carrier modulation with frequency domain

equalization (SC/FDE). The main objective of SC-FDMA is to introduce

transmission with lower PAPR than OFDMA but it is very sensitive to phase

noise and Carrier Frequency Offset (CFO), which will break the orthogonality

among subcarriers and generate Common Phase Error (CPE) as well as inter-

carrier interference (ICI) to distort the received signals. Also this is similar

situation in other OFDM communication systems. Thus the BER performance

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and throughput are seriously degraded. To improve system performance and

throughput, it is necessary to analyze the effects of phase noise, CFO and then

suppress the interferences. To overcome this problem, the SC-SFBC scheme

used here with modified FFT Algorithm. Single Carrier- Space Frequency

Block Coding (SC-SFBC) is used to reduce the peak to average power ratio

(PAPR) of the Multiple-Input Multiple-Output (MIMO) SC-FDMA signal.

Modified FFT algorithm is used to reduce the memory references thereby

increasing the throughput. The coding method is different from the Alamouti

scheme, the four frequencies involved in SC-SFBC scheme do not use

successive subcarriers any longer. Thus it avoids the degrading interactions

between phase noise and CFO estimations. The proposed structure is

evaluated using performance parameters such as BER & SNR. Structural

realization and analysis pertaining to Timing, Power and Throughput are

implemented in Virtex-4 and analysis is carried out in Altera respectively

5.2 PAPR IN OFDM SIGNAL

OFDM signals have high PAPR and are thus not power efficient .

High PAPR values lead to severe power penalty problem at the transmitter,

which is not affordable in portable wireless systems where terminals are

battery powered. To avoid this problem, PTS approach is introduced by

achieving high PAPR reduction. However, it causes high system complexity

and computational complexity which make it difficult to employ for OFDM

system. So, the modified FFT technique (Nicola et al 2005) is proposed to

lower the computational complexity while maintaining the similar PAPR

reduction performance compared with the ordinary PTS technique.To reduce

PAPR further, SC_SFBC technique can be combined with modified FFT

scheme. MIMO systems may be implemented in a number of different ways

to obtain either diversitygain or capacity gain. A modified FFT combined

with SC-SFBC technique to improve PAPR reduction performance in OFDM

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and MIMO-OFDM is presented in this chapter. The performance of the

modified FFT combined with SC_SFBC is evaluated to maximize.

In OFDM modulation technique, a block of N data symbols, ,nX

0,n 1, …, 1N , is formed with each symbol modulating the corresponding

subcarrier from a set ,nf 0,n 1, …, 1N where N is the number of

subcarriers. The N subcarriers are chosen to be orthogonal, i.e. nf n f ,

where 1/f NT and T is the original symbol period.

The resulting baseband OFDM signal x t of a block can be

expressed as

11 2

0 n

N j f tx t X enN n , 0 t NT (5.1)

The PAPR of the transmitted OFDM signal x(t), is the ratio of the

maximum to the average power and given as

0 0

0

2 2( ) ( )m ax m a x

2 1 2 t N T t N T

N T

x t x t P A P R

E x t x t d t N T

(5.2)

The PAPR of the continuous-time OFDM signal cannot be

precisely computed at the Nyquist sampling rate, which corresponds to N

samples per OFDM symbol. So, in discrete-time systems, instead of reducing

the peak of the continuous-time signal i.e. max ( )x t , it is better to reduce the

maximum amplitude of LN samples of ( )x t , where parameter L denotes the

oversampling factor. This is due to the fact that the PAPR of a continuous

time OFDM signal cannot be precisely described by sampling the signal using

N samples per signal period where some of the signal peaks may be missed.

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So oversampling is usually employed ( 1)L for better approximation of true

PAPR. The case 1L is known as critical sampling or Nyquist rate sampling.

Oversampling is implemented by LN -point IFFT of the data block with

( 1)L N zero padding.

The continuous PAPR of ( )x t is approximated by its discrete LN

samples i.e. nTx LN

, which is obtained from the LN –point IFFT of nX with

( 1)L N zero-padding can be represented as

11 2 /( ) 0

N j kn LNx n X enN n , 0,1,..., 1k NL (5.3)

To evaluate accurately the PAPR reduction performance from the

statistical point of view, the Complementary Cumulative Distribution

Function (CCDF) of the PAPR is used. It denotes the probability that the

PAPR of OFDM symbol exceeds a certain threshold 0PAPR .

( ( ( ))) Pr( ( ( ))) 0CCDF PAPR x n PAPR x n PAPR (5.4)

Due to the independence of the N samples, the CCDF of the PAPR

of a data block at Nyquist rate sampling is given by

0( ( ( ))) 1 (1 )PAPR NCCDF PAPR x n e (5.5)

Therefore, the CCDF of PAPR of L-times oversampled OFDM

signal can be defined as

0 0( ( ( ))) Pr( ( ( )) ) 1 (1 )

PAPR LNCCDF PAPR x n PAPR x n PAPR e

(5.6)

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5.3 SPATIAL BLOCK CODES

5.3.1 STBC

Space Time Block Coding (STBC) is the most straight forward

approach for applying Alamouti coding. In this case, precoding involves four

frequency components ( ( ), ( ), ( )and ( )) to be sent to the

antennas over four successive time intervals. As presented in Table 5.1,

Alamouti STBC is performed between k-th frequency component ( ) of

FFT output vector ( ) at time 4j, k-th frequency component ( ) of

FFT output vector ( ) at time 4j+1, k-th frequency component ( )

of FFT output vector ( ) at time 4j+2 and k-th frequency component ( ) of FFT output vector ( ) at time 4j+3. As a result for each

subcarrier, the precoded components are mapped onto four consecutive SC-

FDMA blocks and the frequency structure of the signal from one block to

an