CHAPTER 5 MIMO SC FDMA WITH ICI ESTIMATION AND OFDMA of LTE physical layer by considering different

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    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


    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


    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


    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 )



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    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


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