A Publisher for Research Motivation Volume 2, Issue 8,...

IPASJ International Journal of Electronics & Communication (IIJEC) Web Site: http://www.ipasj.org/IIJEC/IIJEC.htm

A Publisher for Research Motivation........ Email: [email protected] Volume 2, Issue 8, August 2014 ISSN 2321-5984

Volume 2, Issue 8, August 2014 Page 37

ABSTRACT To achieve high speed, data rate and simultaneous increase in range and reliability without consuming extra radio frequency requires MIMO-OFDM for wireless broadband communication. It has been demonstrated that multiple antenna system provides better BER and it can be further improved by using different modulation techniques.Typical constraints faced during OFDM transmission are: a large peak-to-average power ratio, which can result in significant distortion when transmitted through a nonlinear device, such as a transmitter power amplifier. In this paper we use lower order modulation like QPSK which will provide better error rate & better diversity performance with less computational complexity as compared to other modulation techniques. We have estimated BER verses SNR by using MIMO-SM &Rayleigh fading channel in order to provide better transmission rate with better reliability hence it will increase the system performance. The minimum PAPR estimated is 6.44 dB with optimum BER achieved up to 10-4 Keywords: Bit error rate (BER), Peak to average power ratio (PAPR), Orthogonal frequency division multiplexing (OFDM), Multiple input multiple output (MIMO), Spatial Multiplexing (SM), QPSK (Quadrature phase shift keying),SNR (Signal to noise ratio), Partial transmit sequence (PTS)

1. INTRODUCTION The ever increasing demand on wireless services, both for voice and data communications is a major motivational factor for developing MIMO-OFDM system. In particular the demand for multimedia services such as video-on-demand, video conferencing, etc, is expected to diversify services and increase the volume of data traffic. Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier-based technique for mitigating ISI to improve capacity in the wireless system with better spectral efficiency. MIMO (Multiple Output Multiple Input) systems are proposed which promise enhanced system capacity, throughput, data reliability and performance with acceptable BER proportionally with the number of antennas used at both transmitter & receiver [1]. Several wireless standards such as wireless-fidelity (Wi-Fi) (IEEE 802.11) and worldwide interoperability for microwave access (WiMAX) (IEEE 802.16) have thus incorporated MIMO technologies in the system. The combination of MIMO-OFDM is beneficial. The two major drawbacks which degrades the performance and efficiency of OFDM systems are bit error rate (BER) in fading environments and high Peak to average power ratio (PAPR).There are various techniques used to minimize PAPR. OFDM system provides high data rate wireless communications links with transmission rates approach 1Gbps. PTS reduction techniques has been elaborated to reduce PAPR which provides better result compared to other techniques. 1.1 Implementation of MIMO technology An example of practical implementation of MIMO technology in Wi-Fi system using spatial multiplexing (SM) is depicted in Fig 1, where a 54 Mbps radio channel is used to deliver 108 Mbps data, using 2x2 MIMO-multiplexing systems. The operation of the system can be summarized as follows. Firstly the client send data of 108 Mbps to the wireless network via the card adapter to the wireless network, then the encoder divides the data stream into two or slower sub-streams, then the transmitter send each sub-stream to a separate antenna for simultaneous transmission on the same radio channel. The signal will experience reflection off objects, creating multiple paths from the transmitting end to receiving end. Two antennas at the receiver side receive the multiplexed signal, and the receiver uses MIMO detection algorithms to unscramble the signal, in order to recover the transmitted data streams at 108 Mbps [2].

Performance Enhancement of MIMO-OFDM System using PTS to achieve optimum BER &

PAPR “Devashree H. Patil” 1, Rajesh S. Bansode 2 and Geeta H. Karande 3

1ME Student , TCET Kandivali, Mumbai

2 Assistant professor, TCET Kandivali, Mumbai

3 ME Student, TCET Kandivali, Mumbai




Figure 1 Wi-Fi system using spatial multiplexing

MIMO systems are applicable in transforming mutual information and also improve BER effectively. This system provides better performance than beam forming in terms of SNR improvement of 3-5 dB, spectral efficiency and PAPR reduction up to 5dB.The bottleneck of this system which degrades the performance of OFDM systems are bit error rate (BER) in fading environments and high Peak to average power ratio (PAPR).This system provides high data rate in wireless communications links with transmission rates which approach up to 1Gbps.The current paper deals with the software simulation of OFDM system using Matlab and Simulink. The section 2 briefs details about block diagram of OFDM transmitter and receiver. Section 3 describes about MIMO using Spatial Multiplexing (SM) followed by Section 4 where, mathematical modeling of QPSK and its constellation are discussed in brief. The final section 5 describes about BER which is later, concluding with simulation results.

2. ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING (OFDM) The Orthogonal Frequency Division Multiplexing (OFDM) is a technique based on the spread spectrum concept, which consists in the transmission of the data using a large number of carriers that have precise frequencies. Orthogonality of subcarriers is the main concept in OFDM. The property of orthogonality allows simultaneous transmission of additional sub-carriers in a tight frequency space without interference from each other. This acts as an excessive advantage in OFDM. The OFDM spectrum signal is depicted in Figure 2. Due to orthogonality the spacing between the signals is precise thereby saving the bandwidth requirement. The OFDM is used in many telecommunication applications because it’s high spectral efficiency, robustness to interference and to the distortion due to the multipath in case of wireless channel [3].

Figure 2 The Spectrum of OFDM signal

2.1 OFDM Transceiver Systems A simplified OFDM system block diagram is shown below in Figure 3. It represents the transmitter and the receiver respectively. The transceiver structure for OFDM utilizes the Fourier transform for modulation and demodulation. In order to make multicarrier systems a more practical technology an IDFT and DFT are used for the baseband modulation and demodulation respectively. At the transmitter, the incoming serial data is initially mapped to



Volume 2, Issue 8, August 2014 9

quadrature phase shift keying (QPSK), with a symbol rate . The data on each symbol is then mapped to a phase angle based on the modulation. For example, in case of QPSK the phase angles are employed at 0, 90, 180, and 270 degrees. The use of phase shift keying produces a constant amplitude signal and was selected for its simplicity and to reduce problems with amplitude fluctuations due to fading.

Figure 3 Block diagram of OFDM transreceiver System

The data is then demultiplexed by a serial to parallel converter resulting in a block of N complex symbols, X0 to XN-1. Then this parallel samples are passed through an N point IFFT by using rectangular window of length N. The parallel data output from IFFT results in complex samples x0 to xN-1.Assuming the incoming complex data is random it follows that the IFFT is a set of N independent random complex sinusoids summed together. The samples, x0 to xN-1. Are then converted back into a serial data stream producing a baseband OFDM transmit symbol of length T=N.Ts. Cyclic Prefix (CP) is a copy of the last part of the samples is appended to the front of serial data steam. After the CP has been added, the symbols are then converted back to a serial time waveform. This waveform is utilized has a base band signal for the OFDM transmission The CP combats the disrupting effects of the channel which introduce Inter Symbol Interference (ISI). At the receiver the whole process is reversed to recover the transmitted data, the CP is removed prior to the FFT this reverses the effect of the IFFT. The FFT of each symbol is then taken to find the original transmitted spectrum. The complex Symbols at the output of the FFT, Y0 , YN-1 are then decoded and the original bit steam recovered The phase angle of each transmission carrier is then evaluated and converted back to the data word by demodulating the received phase. The data words are then combined back to the same word size as the original data [4]-[5].

3. MIMO THEORY A MIMO system is defined constituting with multiple antennas at the receiver and transmitter ends. Traditionally, multiple antennas were employed to shape the radiation diagram of the antenna pattern, using a technique known as beam forming. However, multiple antennas at the transmitter and receiver may be used to exploit array, diversity, and/or multiplexing gains. In recent years, techniques that transmit over spatially uncorrelated antennas have received broad attention from the research community due to their potential to increase the reliability or the data rate of the wireless link. Spatial diversity and multiplexing are effective techniques to increase robustness and the data rate in wireless systems requiring low complexity. These techniques have been proposed to overcome wireless channel impairments by providing a more reliable transmission link. MIMO wireless communication refers to the transmissions over wireless links formed by multiple antennas equipped at both the transmitter and receiver. The key advantages of employing multiple antennas provide the more reliable performance through diversity and also achieves higher data rate through spatial multiplexing. Next generation wireless systems are required to provide high data rate and high performance over very challenging channels that can be either time selective or frequency-selective. The combination of MIMO and OFDM has the potential of meeting this stringent requirement since MIMO can boost the capacity and the diversity and OFDM can mitigate the detrimental effects due to multipath fading [6]. A general MIMO-OFDM system is shown in Figure 4 where, Mt transmit antennas, Mr receive antennas, and N-tone OFDM are used. First, the incoming bit stream is mapped into a number of data symbols through QPSK modulation[7].Then a block of Ns data symbols S = [S1,S2, …., SNs].are encoded into a codeword matrix C of size NT X Mt, which will then be sent through Mt antennas in T OFDM blocks, each block consisting of N sub channels. Specifically, …., will be



Volume 2, Issue 8, August 2014 0

transmitted from the jth transmit antenna in OFDM blocks 1, 2, · …. T. respectively. Where, denotes a vector of length-N, for all j = 1, 2,…,Mt and n = 1, 2, …, T [8]. The codeword matrix C can be expressed as

(1)

Figure 4 MIMO-OFDM model

3.1 Spatial Multiplexing MIMO In spatial multiplexing, incoming data is divided into multiple substreams and each substream is transmitted on a different transmit antenna. The data of each user is spatially multiplexed into substreams to be transmitted across Mt transmit antennas and each substream is spread by a spreading code. Though the spreading codes should be different among users, either the same code or different codes can be used in spreading substreams of a given user. Even though the use of different codes can cause code scarcity, it can achieve superior performance to the use of the same code because the substreams can be differentiated by both the spatial characteristics and their codes. In this system a high rate bit stream is decomposed into four independent 1/3 rate bit sequences which are then transmitted using multiple antennas. These signals get mixed in the channel as they use same frequency spectrum [9]-[10]. At the receiver individual bit streams are separated, estimated and merged together to yield the original signal. The input output relation for N transmitter antennas and M receiver antennas MIMO system is given by:

y = Hx + n (2)

Figure 5 8x8 MIMO using Spatial Multiplexing

4. Mathematical Modeling of QPSK Quadrature Phase Shift Keying uses four phases to encode two bit per symbol to minimize the bit error rate. QPSK transmits twice the data rate in a given bandwidth compared to BPSK at the same BER. QPSK modeling is complicated as compared to that of BPSK [11]. The symbols in the constellation diagram are expressed in terms to transmit them are:-

Sn(t) = (3)




Where, Constellation diagram is depicted in Figure 6, where four phases are labeled [A, B, C, D] corresponding to one of [0, 90, 180, 270] degrees phase change. Since there are four possible phases, 2 bits of information are conveyed in each time slot. This results in a two-dimensional signal space with unit basis function

(4)

(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 [12].

Table 1: QPSK mapping Phase

(Degrees) State Binary data

0 A 00 90 B 01

180 C 11 270 D 10

Figure 6 Signal constellation diagram for QPSK

5. PAPR REDUCTION TECHNIQUES The PAPR reduction methods are divided into three major categories as Signal distortion techniques, Signal scrambling techniques and Coding techniques. Some powerful schemes are the signal scrambling techniques contains into Selective Level Mapping (SLM) & Partial technique sequence (PTS) among which PTS is used for the work. In this paper PTS technique is used for reducing PAPR [14].The complexity and computation time is minimum compared to others. In a typical OFDM system with PTS approach several full IFFT operations are avoided in PTS, which is its advantage over SLM 5.1 PTS for reducing PAPR

Figure 7 Block Diagram of Partial Transmit Sequence

The basic idea of partial transmit sequences algorithm is to divide the original OFDM sequence into several sub-sequences, each sub-sequence is later multiplied by different weights until an optimum value is chosen. The block diagram of PTS algorithm is depicted in Figure 7. The data information in frequency domain X is separated into V




non-overlapping sub-blocks and each sub-block vectors has the same size N [14]-[16]. Assume that these sub-blocks have the same size and no gap between each other, the sub-block vector is given by

X = ∑Vv=1 bvxv (7)

Where, bv = ℮jфv(фv[0,2π]) {v=1,2,…..V} is a weighting factor been used for phase rotation[17]-[18]. The signal in time domain is obtained by applying IFFT operation on Xv, that is

X= IFFT (X) = X = ∑Vv=1 bv IFFT xv = ∑V

v=1 bv . xv (8)

Select one suitable factor combination b = [b1,b2…….bv] which makes the result achieve optimum [19]. The combination can be given by

b= [b1,b2…….bv] = arg min [b1,b2…….bv] (max 1<n<N) | ∑Vv=1 bv . xv | 2 (9)

Where, arg min (·) is the judgment condition that output the minimum value of function.

6. SIMULATED PARAMETERS AND RESULTS

Figure 8 Simulation of OFDM transceiver

The simulation model for OFDM is shown in above Figure 8, this model is used to calculate BER and data rate. Data rates up to 960 Mbps have been achieved. This data rate is been calculated using above simulink model. 6.1 Analysis of BER for various values of SNR To obtain the bit error rate performance over additive and multipath fading channels the parameters depicted in Table 3 have been assumed for modulation, channel coding and FFT size.

Table 3: Simulation parameters Parameters Type/Value Data rate 900Mbps-100Mbps

No. of carrier 1024 FFT Size 1024

Constellation Mapping QPSK SNR range 0-10

MIMO Technique 8X8 Spatial Mutiplexing Symbol duration 102.4μs

Length of Cyclic Prefix 1/8 of Symbol duration (12.8μs)

Channel Coding ½ convolutional coder Channels Model Rayleigh & Rician

The measure of performance used is bit error rate versus signal-to-noise ratio. The simulation contains a 1024 subcarrier OFDM system. Constellation mapping or message modulation is performed using quadrature phase shift keying (QPSK). The Rician & Rayleigh channel model is used for this study. Performance enhancement is done for the proposed system using MATLAB simulation. In communication systems, information bits are typically grouped into a frame or packet and transmitted to receiver. The received packet may be lost or include errors because of a noisy channel for transmitting the data. The bit error rate (BER) is the percentage of received bits that include an error. BER in a coded system depends on the ratio of the bit energy to noise spectrum density (SNR). Bit Error Rate (BER) is the basic parameter to access the quality of any digital transmission and quality measurement of recovered data. Using FFT approach as the number of subcarrier increases the better is accuracy due to higher number of points. Hence data rate




will also increase with this technology. In this section, The BER performance for the proposed communication technique is based on MIMO-OFDM system using spatial multiplexing. The system model has been tested for QPSK modulation with a rician fading channel. To analyze the result shown in table 4. It is observed that BER is reduced up to order of 10-4 with Eb/No ratio changing from 0dB to 10dB. The BER for 8x8 MIMO system is greatly reduced to 10-4..In Rican fading channel, BER of 0.0008 is achieved for the SNR value of 5 dB using Spatial multiplexing. The simulation results are shown in Figure 9 This shows optimum BER of 10-4 is achieved at 5.6dB SNR.

0 1 2 3 4 5 6 7 810

-4

10-3

10-2

10-1

100

SNR

BER

(log)

Figure 10 Plot of BER vs SNR using QPSK modulation & Rician channel

6.2 Optimum PAPR achieved using PTS The Figure 11(a) depicts the graph for reduction in PAPR. The Partial transmit sequence scrambling technique used to indicate reduction in PAPR. As shown in Figure 11(a) The PAPR of 9.17 dB is achieved without using PTS. Whereas, the reduction in PAPR of 6.45dB is achieved using PTS.As shown in graph of Figure 12. The estimation of the channel is done using the two channels, Rayleigh and Rician. It is done by implementing Modified channel estimation algorithms. Table 4. Indicate PAPR reduction with PTS at various phase shift. The PTS technique provides optimum PAPR of 2.71dB.Table 5 shows the difference in minimum PAPR achieved without and with PTS. Thereby improving the performance and efficiency of OFDM system. PTS technique provides better reduction in PAPR as compared to other scrambling techniques.

Figure 11(a) PAPR verses Phase shift without PTS

0 50 100 150 200 250 300 3500

5

10

15

20

25

30

35

40

fi

PAPR

PAPR against different phase shifts

Figure 11(b) PAPR verses Phase shift with PTS




Table 4: Optimum PAPR using PTS technique Phase shift

(Degrees) Optimum

PAPR (dB) 4 30.76 12 29.09 24 22.07 45 10.01 88 7.55

168 11.00 180 12.18 200 21.69 234 29.32 248 16.73 320 26.11 329 21.72 342 28.84 360 6.44

Figure 12 Optimum PAPR achieved at 360 phase shift with PTS

Optimum PAPR is achieved with PTS at phase shift 360 degree. The reduction in PAPR of 2.71dbB is achieved in order to improve the performance of the OFDM system.

Table 5: PAPR reduction with & without PTS Achieved

PAPR without PTS in dB

Achieved PAPR with PTS in dB

Reduction in PAPR using PTS

in dB

9.17 6.45 2.71

7. CONCLUSION The MIMO-OFDM is an efficient wireless system. It has the efficient use of available bandwidth since the sub channels are overlapping. With the analysis of result of simulation we can conclude that speed of data transmission rate is doubled and BER is reduced by 8 times. The performance of the MIMO-OFDM system is optimized with minimum bit error rate of 0.0008 ie, 8x10-4 with 5.6 dB SNR. OFDM with multiple transmit and receive antennas form a MIMO system to increase system capacity. It is apparent from table 1 that when the Rician factor and Signal to Noise Ratio increases, the Bit Error Rate decreases gradually. However, the occurrence of high PAPR restricts its application. The PTS provides a distortion less technique in eliminating the PAPR at the expense of additional complexity. Based on simulation result justifies that PTS technique succeeds reduction in the PAPR of OFDM signal. The Further, the research can be carried out in reducing the computation complexity of MIMO-OFDM System. The PTS provides optimum PAPR of 2.71dB.




References [1] S.S.Riaz Ahamed, Performance analysis of OFDM, Journal of Theoretical and Applied Information

Technology,2008 [2] University of Alberta, MIMO History. Retrieved on September28,2009 from

http://www.ece.ualberta.ca/~HCDC/mimohistory.html [3] G.J.Foschini, ”Layered space-time architecture for wireless communication in a fading enviroment when using

multiple antennas,” AT&T Bell Labs. Tech. J., vol1. 1, no.2, pp.41-59,1996 [4] K. Baum, B. Classon and P. Sartori, Principles of Broadband OFDM Cellular System Design, Hoboken, NJ: John

Willey & Sons, 2010. [5] V. Taro & N. shad and A. R. Calderbank, "Space-time codes for high data rate wireless communication:

performance criterion and code construc1ion:'IEEE Trans. on In/onn. Theory, vol. 44, pp. 744-765, March,1998. [6] Wikipedia,“MIMO”,http://en.wikipedia.org/wiki/MIMO. [7] D.Agrawal, et al.: “space-time coded OFDM for high data rate wireless communication. [8] L. Zheng and D. N. C. Tse, “Diversity and Multiplexing: a Fundamental Trade-Off in Multiple-Antenna

Channels,” IEEE Trans. Info. Theory, vol. 49, May 2003, pp.1073–96. [9] J. B. Wang andK.Yao, “Capacity scaling in OFDM Based spatial multiplexing systems,” in Proc. IEEE Vehicular

T232echnology Conf., 2002, pp. 28-32. [10] W. Zhang, X.-G. Xia, and P.C. Ching, ”High-rate full-diversity space-time frequency codes for broadband MIMO [11] E.A.-Dalakta,A.A.-Dweik&C.Tsimenidis (2012).Efficient BER Reduction Technique for Nonlinear OFDM

Transmission Using Distortion Prediction. IEEE Transactions On Vehicular Technology.Vol. 61,no.5, pp.230-36. [12] S. H. Han, J. H. Lee, “An overview of peak-to-average Power ratio reduction techniques for multicarrier

transmission”,IEEE Wireless Comm., pp. 56-65,April 2005 [13] J.Tellado-Mourelo, “Peak to Average Power Ratio Reduction for Multicarrier Modulation”, PhD thesis,

University of Stanford, Stanford, 1999.

A Publisher for Research Motivation Volume 2, Issue 8,...

Documents

Transcript of A Publisher for Research Motivation Volume 2, Issue 8,...