o l . 6, issu e 4, oct - dec 2015 issn : 2230-7109 (online ... · iject vo l. 6, issu e 4, oct -...

IJECT Vol. 6, IssuE 4, oCT - DEC 2015 ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

w w w . i j e c t . o r g 62 InternatIonal Journal of electronIcs & communIcatIon technology

LDPC Based MIMO-OFDM System for Shallow Water Communication using BPSK

1Nitesh Upadhyay, 2Mukesh Tiwari, 3Jaikaran Singh1,2,3Dept. of Electronics and Communication, SSSIST Sehore, Madhya Pradesh, India

AbstractUnderwater communication is the recent area of research among researchers to explore the underwater activities. In underwater environments, acoustic wave is preferred over radio and optical waves as information carrier, because the latter two suffer from high attenuation and severe scattering correspondingly in the medium of water.The wireless communication inside water is quite similar to outside of the water of this a little effect of rarer vs denser factor affects the communication. In the proposed method using a wireless communication with multiple-input multiple-output (MIMO) systems enables increased spectral efficiency for a given total transmit power. Large capacity is achieved by introducing additional spatial channels that are exploited by using LDPC coding technique. The environmental factors that affect MIMO capacity. These factors contain channel complexity, the external interference, and channel estimation error. It was shown that, if multiple antennas are used at transmitter and or receiver can improve data rate and reliability. In the proposed work simulate our proposed phenomena on different window size in FFT, compare ours model with different two major modulation technique that is BPSK and QAM. Our results shows that better output for BPSK as compare to other famous technique. The overall system performance will improve by our proposed methodology. This proposed work we focused on two performance parameters they are, first one is the bit error rate (BER) and second one is the frame error rate (FER) and we see that for different window size BPSK perform better rather than others.

KeywordsBinary Phase Shift Keying, Quadrature Phase Shift Keying. Multiple Input Multiple Output.

I. IntroductionIn the last decade, underwater communication has become a dynamic field of analysis as there’s still a huge gap between the communication technology for terrestrial and underwater application [1]. Underwater acoustics is that the study of the propagation of sound in water and therefore the interaction of

Fig. 1: Under Water MIMO – OFDM

the mechanical waves that represent sound with the water and its boundaries. The water could also be within the ocean, a lake or a tank.Underwater acoustic channels are usually recognized united of the foremost tough communication media in use nowadays. Acoustic propagation is best supported at low frequencies, and therefore the information measure accessible for communication is extraordinarily restricted. As an example, an acoustic system could operate during a frequency vary between 10 and 15 kHz. Though the entire communication, information measure is extremely low (5 kHz), the system is indeed a band, within the sense that information measure isn’t negligible with relation to the center frequency. Sound propagates underwater at a very low speed of roughly 1500 m/s, and propagation happens over multiple methods. Delay spread over tens, or maybe many milliseconds ends up in frequency-selective signal distortion, whereas motion creates an extreme propagation. The worst properties of radio channels — poor physical link quality of a mobile terrestrial radio channel high latency of a satellite channel — are combined in an underwater acoustic channel. During this article we have a tendency to take a tutorial summary of the channel properties, about to reveal those aspects of acoustic propagation that are relevant for the planning of communication systems. This report is organized into completely different sections that address attenuation and noise, multipath propagation, and therefore the propagation. Implications of acoustic propagation extend to the far side the physical layer, and that we conclude the article within the final section by considering their impact on the planning of future underwater networks [12].As electromagnetic waves propagate poorly in sea water, acoustics provide the most obvious medium to enable UWAC. High speed communication in the underwater acoustic channel (UWAC) is interesting due to limited bandwidth, extended multi-path, refractive properties of the medium, severe fading, rapid time-variation and large Doppler shifts.

II. Problem in UWAC As terrestrial communication the medium of transmission is Air however, just in case of underwater acoustic communication the medium is differs that is water. so numerous issues have to be compelled to face for communication within the water.There are a unit following issues that happens in underwater acoustic communication-Propagation delay within the water is long. The transmission speed of acoustic signals in salty water is around 1500 meter/s that may be a distinction of five orders of magnitude less than the speed of Electromagnetic wave in free space. Correspondently, propagation delay in an underwater channel becomes significant. This is one of the essential characteristics of underwater channels.Multipath propagation happens thanks to totally different objects (fishes or turquoise animal), rocks, shallow or problem. In shallow water unwanted waves are generated however refraction and reflection of signals happens, in problem ray bending happens with a lot of pressure transmitters, one will have a multiple-input multiple output (MIMO) system conjointly.

IJECT Vol. 6, IssuE 4, oCT - DEC 2015

w w w . i j e c t . o r g InternatIonal Journal of electronIcs & communIcatIon technology 63

ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

Fig. 2: Problems in UWAC using in MIMO

We also hope to provide an insight into some of the open problems and challenges facing researchers in this field in the near future. We do not attempt to provide an exhaustive survey of all research in the field, but instead concentrate on ideas and developments that are likely to be the keystone of future underwater communication networks. This paper is divided into three main sections – one on historical perspective, another on underwater communications and a final one on underwater networking.

III. MIMO – OFDM In the recent years MIMO A channel is also tormented by weakening and also the impact of weakening can impact the standard of the signal. The entire phenomena reduce the signal to noise magnitude relation (SNR). In chance this can impact the error rate, forward digital knowledge is being transmitted. The quality of diversity is to supply the receiver with multiple versions of an equivalent signal.

BPSK / QAM Modulation

Serial To Parallel

ConversionLDPC Coding

OFDM Modulation

(IFFT)

Adding Cyclic Prefix

Remove Cyclic Prefix

OFDM Demodulation

(FFT)

LDPCDecoding

Parallel to Serial

ConversionDemodulation

Data Input

Data Output

Channel with Noises

Fig. 3: Block Diagram of the Proposed Shallow Water Communication System

If these is created to be affected in numerous ways in which by the signal path, the likelihood that they will all be affected at an equivalent time is significantly reduced. Consequently, diversity helps to stabilize a link and improves performance, reducing error rate. Wireless communication using multiple-input multiple-output (MIMO) systems enables increased spectral efficiency for a given total transmit power. Large capacity is achieved by introducing additional spatial channels that are exploited by using space-time coding. The environmental factors that affect MIMO capacity. These factors contain channel complexity, the external interference, and channel estimation error. It was shown that, if multiple antennas are used at transmitter and or receiver can improve data rate and reliability. In Fig. 2 the block diagram of the proposed approach is given. The major blocks are modulation of data using BPSK followed by Serial to parallel conversion of modulated signal. Now the signal is being coded with LDPC and modulated by orthogonal frequency division multiplexing (OFDM) and before transmission of signal cyclic prefix are added.

During transmission through channel signal is encountered with the noises and reached at the receiver. On the receiver the reverse process of transmitter is taken place and the data will be taken out.The above describe block diagram of the proposed methodology is then implemented on simulation tool and the step by step flow is shown in the given Fig. 3. Before to discuss the simulation one important thing to show here the bit error rate compression among the different modulation technique and also show the complexity level of communication technique. The execution of the simulation algorithm is explained step by step which are as follows:1. First step create a communication model – In OFDM system development, it is necessary to understand its performance characteristics. After this part presents the performance analysis of the MIMO - OFDMsystem in great detail. After this create the radio channel model for the bit error rate (BER) analysis.

(1)

( )( )

>∆−≤≤<∆−

=ttt

tttf

G

sG

,,0,1

)( (2)

( )ss

k tf

tkf 1,1

=∆−

= (3)

sGs tT +∆= (4)

(5)

2. Second step generate random data for transmission over system. There for want to add AWGN noise channel. In below the equation shows the AWGN gaussion noise model and also shows that the received signal is integrated only over the useful symbol period. For the current form of an OFDM system, the signal is transmitted even in the guard interval, so its power is not used for detection. Therefore, in the AWGN channel, the BER of a BPSK based OFDM system is all the same as that of a CPSK-based SCM system [2], but we need to take into consideration ‘‘the power loss associated with guard interval insertion.’’ When employing BPSK or QPSK at all the subcarriers, the BER is given by

(6)

where erfc (x ) is the complementary error function given by

(7)3. Third step modulate data with BPSK Modulation perform apply a BPSK modulation. Because when we send our signal in noise underwater acoustic channel is totally damaged. So the modulation is important thing BPSK (also sometimes called PRK, phase reversal keying, or 2PSK) is the simplest form of phase shift keying (PSK). It uses two phases which are separated by 180° and so can also be termed 2-PSK. It does not particularly matter



exactly where the constellation points are positioned, and in this figure they are shown on the real axis, at 0° and 180°.The general form for BPSK follows the equation:

(8)

This yields two phases, 0 and π. In the specific form, binary data is often conveyed with the following signals:

(9)

for binary “0”

(10)

for binary “1”where fc is the frequency of the carrier-wave.

IV. Convert Signal From Serial to Parallel One of the core concepts behind MIMO wireless systems coordinate system signal process within which time (the natural dimension of data communication data) is complemented with the spatial dimension inherent within the use of multiple spatially distributed antennas, i.e. the employment of multiple antennas placed at totally different points. Consequently MIMO wireless systems will be viewed as a logical extension to the sensible antennas that are used for several years to boost wireless. It is found between a transmitter and a receiver, the signal will take several methods. in addition by moving the antennas even a small distance the methods used can amendment.Same process apply for at the receiver end.

Fig 4: Shows MIMO System in UWAC

IV. Loss in UWAC

A. NoiseThe noise sources in an UWA channels can be divided into ambient noise and man-made noise. The ambient noise is caused by biological creatures, seismic phenomenon and movement of water such as waves. The ambient noise often follows some curves called Knudsen curves. These Knudsen curves show that the ambient noise is being reduced as the frequency increases. According to the curves the ambient noise will be reduced with about 17 dB per decade of frequency, [14]. For high frequencies the thermal noise can be very dominating, especially over 100-200 kHz, where the thermal noise increases with 20 dB per decade. Another source of ambient noise is water bubbles that can a_ect underwater communication. According to [5] in the 10-20 kHz band the dominant noise source may be resonant air bubbles in the area near the surface. The resonant frequency f0 is given by the following equation:

B. Loss(UWA-S Transmission loss is caused by attenuation and geometric spreading for signals that are transmitted directly to the receiver. Reflected signals will also have transmission loss due to the transmission coefficient, [14]. The geometrical spreading is caused by the movement of the wave-front, and for further distance the wave front is spread over a larger area. There are in general two types of geometrical spreading in underwater acoustic communication: Cylindrical and spherical. Cylindrical spreading is typical for shallow water UWA communication and has only horizontal spreading. Spherical spreading is typical for deep water UWA communication and can be seen as an omnidirectional point source, [2]. Geometrical spreading will cause the acoustic intensity to decrease with increased distance. Cylindrical wave propagation results in the following geometrical spreading of the intensity I(r).

C. Doppler SpreadingDoppler spreading occurs because of Doppler shift of frequencies of different signal components. This may occur because of movement by the transmitter and the receiver. Signals reflected by the surface may also encounter this effect because of movement in the surface cause by waves. Doppler spreading will give a change of the transmission frequency and a continuous spreading of the frequencies, [3]. The change of the transmission frequency can be compensated for in the receiver, but the general spreading is harder to deal with. Doppler spreading is considered to be a huge challenge for the modulation techniques and the multi-channel access methods.

D. Multipath and Time-DelayMultipath is a huge problem that might affect the communication severely, and will give inter Symbol interference (ISI). How much the multipath will occur depends on many physical factors, but the depth depended sound velocity is most important. In this is illustrated for 5 a depth depended sound velocity that will give a bending of the sound rays in the horizontal direction, as described in [14]. The choice of spacing size between the sensors needs to take this effect into account. Another problem is that the depth depended sound velocity changes through the season and thereby change the multipath through the season making it harder sign the UWA-SN system. This may lead to constructive or destructive interference.

V. Simulation ResultsThe result of the proposed method that is BER Performance of underwater acoustic communication using MIMO Technology shown in this section [21]. For simulation of proposed method we have to use MATLAB R2012b (8.0.0.783) software. To perform our new approach we have to take AWGN as a noise channel and as a reference modulation technique BPSK and QAM for testing purpose. The testing channel are artificially corrupted by AWGN [19] adaptive white Gaussian noise by using MATLAB and tested our proposed approach on different window size of FFT like 128, 256 etc. [19]. Basic configuration of our system where we have checked our proposed method is Manufacturer: Hewlett-Packard HP 4540s Processor: Intel(R) Core(TM) i3-3110M CPU @ 2.40 GHz 2.40 GHz with 4.00 GB (2.64 GB usable) RAM: System type: 32-bit Operating System De-noising performances are quantitatively measured by the BER and FER as defined in given equations, respectively. In the noisy channel here the BER is mainly expressed as a function of the normalized carrier-to-




noise ratio measurewhich is denoted by Eb/N0, (energy per bit to the noise power spectral density ratio), or Es/N0 (energy per modulation symbol to noise spectral density). Here for example, in the case here of QPSK modulation and AWGN channel, the BER as the function of the Eb/N0 is given by:

.The performance of proposed method shows in the BER performance of the suggested OFDM system. These result shows the overall system performance with the LDPC coded OFDM system for BPSK and QAM. In detail, over a certain threshold of received SNR, this system is able to solve the performance falloff problem caused by deep fading at certain specific sub-carriers.. Such reduction of the SNR variations is shown in the form of BER and in the FER. As combining LDPC codes and OFDM system, the performance variation reduced noticeably from about 10 dB to 3 dB at the 10-3 BER point versus the un-coded OFDM system.

Fig. 5: Shows the Compression of BER in Different Modulation Scheme

A comparison on the relative performancesof M-ary digital modulations, regarding the BER obtained as a function of Eb/No. BPSK is alsoincluded in order to have a reference term to compare with fig. 5.

Fig. 6: Shows the Compression of SER in Different Modulation Scheme

A comparison on the relative performancesof M-ary digital modulations, regarding the SER obtained as a function of Eb/No. BPSK is alsoincluded in order to have a reference term to compare with fig. 6.

0 5 10 15 20 25 3010

-5

10-4

10-3

10-2

10-1

100

E b/No (dB )B

it E

rro

r R

ate

Underwater Acous tic S ystem with MIMO-OF DM and 128 F F T S ize

B P S K Modulation4-QAM Modulation

Fig. 7: BER vs SNR curve of shallow water communication with MIMO-OFDM System using 128-Bit FFT Size

The second result (see Fig. 8) graph shows BER vs SNR graph for 256 bit FFT size of OFDM system by applying MIMO-OFDM system and the modulated with BPSK and QAM techniques. From the results it can be says that the shallow water communication system is better work with the MIMO-OFDM technology and the BPSK modulation and using LDPC coding technique than QAM counterpart.

0 5 10 15 20 25 30 3510

-5

10-4

10-3

10-2

10-1

100

E b/No (dB )

Bit E

rror

Rat

e




The third result (see Fig. 9) graph shows BER vs SNR graph for 512 bit FFT size of OFDM system by applying MIMO-OFDM system and the modulated with BPSK and QAM techniques. From the results it can be says that the shallow water communication system is better work with the MIMO-OFDM technology and the BPSK modulation and using LDPC coding technique than QAM counterpart.



0 5 10 15 20 25 30 3510

-6

10-5

10-4

10-3

10-2

10-1

100

E b/No (dB )

Bit E

rror

Rat

eUnderwater Acous tic S ystem with MIMO-OF DM and 512 F F T S ize



The fourth result (see Fig. 10) graph shows BER vs SNR graph for 1024 bit FFT size of OFDM system by applying MIMO-OFDM system and the modulated with BPSK and QAM techniques. From the results it can be says that the shallow water communication system is better work with the MIMO-OFDM technology and the BPSK modulation and using LDPC coding technique than QAM counterpart.

0 5 10 15 20 25 30 35 4010

-6

10-5

10-4

10-3

10-2

10-1

100

E b/No (dB )

Bit E

rror

Rat

e




VI. Conclusion and Future ScopeThe simulation results of the proposed communication model is calculated and shown in the previous section. From the results we can analysis that the system with the BPSK modulation and having higher FFT size gives better results than the lower FFT size and QAM modulation. In the upcoming technologies better transmitters and the efficient modulation technique will help to achieve better results than current performance.

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