The Underwater Acoustic Communication Computer Science Essay

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Underwater acoustic communication is important due to their application in a wide range of areas. Some of them include development of military applications, pollution monitoring and natural hazards alarming. Signal propagation in underwater channels suffers from various effects such as multipath, scattering, fading, path loss, etc. In the project, we investigate the performance of communication techniques in two such channels which will help to improve the overall data rate as opposed to direct transmission. For the purpose of communication in underwater, electromagnetic waves are inappropriate to use as the electric and magnetic fields are attenuated immensely leading to inefficient transmission capability. Acoustic waves are used for this purpose which travel much faster and are the best possible solution for underwater wireless communication.

Cooperative communication is an efficient way of exploiting channel resources and utilizing channels having limited bandwidth. It allows single-antenna mobiles to reap some of the benefits of Multiple Input Multiple Output (MIMO) systems without compromising on size, cost and hardware complexity. In this, each wireless user not only transmits its own data, it shares the data of another user. This forms a virtual antenna array where single or multiple relays exist between the transmitter and the receiver..

In our project, we evaluate and compare the performances of cooperative communication schemes, AF (Amplify-and-Forward) and DF (Decode-and-Forward) for underwater acoustic channels using BPSK modulation technique. We also evaluate the performance of direct link transmission in an underwater channel and compare it with the cooperative communication schemes. This helps us to determine whether cooperative communication is more efficient for underwater acoustic channels as compared to direct transmission. All simulations are carried out in Matlab.

Objectives:

Following are the objectives that are to be achieved:

Learn the necessary background theory of relaying algorithms, Amplify and Forward (AF) and Decode and Forward (DF).

Learn and model underwater acoustic channels.

Evaluate the performance of cooperative schemes, Amplify and Forward (AF) and Decode and Forward (DF) in an underwater acoustic channel. Performance criterion to be BER for all cases.

Compare the two schemes and comment on their suitability in an underwater acoustic channel.

If time permits, evaluate these performances for multiple relays.

Report Layout:

In this report, chapter 1 gives an explanation of the background to the problem and specifies the objectives that are to be achieved for the successful completion of the project. Chapter 2 describes the signal and channel models used for the simulations. The first underwater channel model is based on the geometry of shallow water multipath. Each multipath component is characterized by a path gain and delay. The second underwater channel model is a Rician model and is affected by multipath, spreading and Doppler effect. The third channel model is a simple Rayleigh fading channel with AWGN. We also review the receiver model and BPSK modulation technique in this chapter. Chapter 3 gives a theoretical insight to cooperative communication where a system model with relays is developed and cooperative communication techniques, AF and DF are described. In this chapter, we also elaborate on the combining method used at the receiver. Chapter 4 contains numerical results obtained via simulation. In the first part, we evaluate the performance of cooperative schemes AF and DF in an underwater acoustic channel. In the second part, we evaluate the performance of these schemes for multiple relays. Chapter 5 presents our conclusion and future enhancements that can be made to the project.

Signal Model

The following figure represents the signal model used for the transmission of signal through three different types of channels as described later in this chapter.

In the above figure a successive stream of 0's and 1's passes through an encoder (not shown) and is converted to symbols or vectors. These symbols then pass through the modulator and are converted to analog signal so that they can be transmitted over a physical channel. While passing through the channel, the signal 's' suffers from channel noise 'n' and other channel effects such as fading and path loss. The signal 'y' received at the receiver then passes through the demodulator where the signal is demodulated to symbols followed by the decoder (not shown) where the symbols or vectors are converted to bits. Modulation taking place is BPSK modulation which has been explained in the next section.

Binary Phase Shift Keying (BPSK)

It is the simplest form of phase shift keying. 0's and 1's are represented by ±. It uses a sinusoid to modulate the sequence of data symbols (±. Data symbols are separated by 180°. It is only able to modulate at 1 bit per symbol. Hence the data rate is low as compared to other phase shift keying techniques like QPSK. The general BPSK equation is,

Here 'f' is the carrier frequency. All simulations are carried out using BPSK modulation.

Additive White Gaussian Noise (AWGN)

This is white thermal noise degrading the overall performance of the communication channel. It has a uniform power spectral density and Gaussian distribution. The Gaussian distribution is given by,

Here mean '= 0. AWGN is linearly added to the signal as can be seen from the following expression,

Here 'y' is the received signal and 'n' is the noise added to transmitted signal 's' after it passes through the channel. It should be noted that that no other channel effect like fading or path loss has been considered in the above equation.

The signal to noise ratio (SNR) is given by s/n. This is an important expression as it helps us to determine the quality of signal passing through the channel. In later chapters we have compared SNR with bit error rate (BER) to evaluate system performance.

Fading

Fading is a phenomenon that is caused due to transmitted signals encountering moving or stationary obstacles on their way in the communication channel. Hence the signal gets scattered and there are multiple copies of the signal reaching the receiver. In wireless communications, fading is caused either due to multipath propagation or shading. There are two types of fading, flat fading and frequency selective fading. In flat fading or amplitude varying fading, the bandwidth of the transmitted signal is less than the channel bandwidth. Hence all frequency components of the transmitted signal are similar in magnitude. In frequency selective fading, it's the opposite case. Fading is multiplicative and hence the fading scaling coefficient affects the signal in the following way,

Here 'h' is the complex scaling factor and 'n' is the Additive White Gaussian Noise (AWGN). Clearly from above, fading is multiplicative and noise is additive.

In fading, there are different models depending on the distribution of attenuation. These are Rayleigh, Rician, Nakagami, Weibull etc. For our project only Rayleigh and Rician are important and these have been discussed in their respective channel models.

Receiver Model

The transmitted signal received at the receiver first undergoes demodulation and then it is detected bit by bit. For a BPSK modulated signal only 1 bit per symbol is transmitted. The bit at the receiver is detected by,

Here 'y' represents the signal received at the receiver.

Cooperative Communication

Various advancements are being made in the field of wireless communications. The basic aim is to achieve the best possible bit error rate (BER) performance for different data transmission rates. Many channels are also plagued with limited bandwidth. This leads us to various communication techniques, Multiple Input Multiple Output (MIMO) systems being the forefront in efficient performance under adverse conditions. Cooperative communication allows single-antenna mobiles to reap some of the benefits of MIMO systems without compromising on size, cost and hardware complexity. It is an efficient way of exploiting channel resources and utilizing channels having limited bandwidth. Signal propagation in underwater channels suffers from various effects such as multipath, fading, limited bandwidth, path loss, etc. In this project we try to tackle these problems through the cooperative communication technique.

In Cooperative communications, the wireless users may increase their effective quality of service (BER, Outage probability, etc) via cooperation. This is achieved by establishing relay channels. A relay channel is a three terminal network consisting of a source, a relay, and a destination. However, this concept can be widely extended to larger network configurations.

As it can be seen from the above figure, the source transmits data to both relay and destination. The relay receives the data, performs an operation depending on the cooperation protocol and retransmits it to the destination. Hence the receiver receives two copies of the transmitted data, combines it and demodulates it to finally detect the transmitted data. Hence single antennae mobiles share their antennae to create a virtual MIMO system and establish a cooperative environment. It should be noted that the above figure refers to a single relay system. In real world, we have multiple relay systems which help in improving the overall performance. However for most of our simulations in this project, we adhere to single relay systems for simplicity and understanding. It should also be noted that the strategy of cooperative diversity can be exploited by exchanging the role of the source with relay. Hence users can not only transmit their own data, they also act as sharing devices to retransmit data of other users.

Equidistant Arrangement

In our simulations, we have considered the source, relay and destination to be equidistant from each other (1 km). Distance plays a major role as by increasing distance between the nodes, we increase the probability of signal attenuation. In case of long distances, there are chances of signals fading away by the time they reach the receiver directly or through multiple paths. Hence distance affects the signal quality in a major way. By making the distance between the source and destination different from the distance between the source and the relay or the relay and the destination or vice versa, we increase the chances of signal attenuation in one channel over the other. For example, it makes no sense to make the distance between the relay and source/destination much larger than the distance between the source and destination as this would defeat the purpose of cooperative technique which would give an inferior performance as compared to BPSK single link transmission.

Maximum Ratio Combining

Once the signal is received from the source and its copy from the relay, the two are combined before detection can take place. There are various methods of combining. Some of them are Equal Ratio Combining (ERC), Maximum Ratio Combining (MRC), Fixed Ratio Combining (FRC) etc. Here we consider Maximum Ratio Combing (MRC) which gives the best performance. In MRC, each received signal is multiplied with its corresponding conjugated channel gain. The channel's attenuation factor and phase should be known to the receiver.

Signal 'Y' achieved after MRC combination in a single relay system is,

Here and are the respective conjugate channel gain coefficients in source-destination and relay-destination channel. The disadvantage of this type combing method is that MRC only considers the last channel of the multi-hop system. Another drawback is the orthogonality of the channel being disturbed in case of large amount of users causing significant interference.

Cooperative Transmission Protocols

As discussed earlier, the relay in a relay channel not only retransmits the signal received from the source it also performs an operation on the signal before retransmitting. Hence it's involved in signal processing as well. This algorithm involved behind such operations refers to cooperative transmission protocols. In the project, our prime focus is on the evaluation of two such cooperative transmission protocols. These are Amplify and Forward (AF) and Decode and Forward (DF).

Amplify and Forward (AF)

It is the simplest method of implementing cooperative communications. In this method, the user (relay) receives a noisy version of the signal transmitted by the source and the noisy signal is simply amplified and retransmitted. The receiver then combines the information sent by the user directly and via relay. It receives two independently faded versions of the signal and can make better decisions on the detection of information. Combination at the receiver has been implemented using MRC.

The factor by which the signal at the relay is amplified before retransmitting is given by,

Here 'β' is the amplification factor, 'E' is the energy of the transmitted signal, '' is the channel fading coefficient and 'n' is the channel noise. Also 'S' denotes the sender and 'R' denotes the relay. This term needs to be calculated for every bit transmitted.

In case of Amplify and Forward, the signal received at the receiver from the relay after amplification is,

Here is the amplification factor, is the channel fading coefficient, is the signal received at the relay from the source and is the channel noise between relay and destination.

A clear drawback of this method is that the noise in the signal is also amplified. This method is often used when the relay has only limited computing time/power available or the time delay caused by the relay to decode and encode the message needs to be minimized.

Decode and Forward

In this, the user (relay) attempts to detect the source's bits, decodes it, re-encodes it and then retransmits the bits. This partnership has to be assigned mutually by the base station. The signal is re-encoded symbol by symbol. The receiver combines the information sent by the user directly and via relay. It receives two independently faded versions of the signal and can make better decisions on the detection of information.

In case of Decode and Forward, the signal received at the relay and receiver respectively is given by,

Here and the respective channel fading coefficient, is the signal received at the relay from the source, is the re-encoded signal and /is the respective channel noise.

In case of perfect re-encoding,. This method is used when the relay has enough computing power. Usually this is the case. There is no amplified noise in the sent signal, as in case of Amplify and Forward (AF). This is an advantage of Decode and Forward (DF) over Amplify and Forward. The signal received at the relay would contain error bits which may be corrected at the relay. However this would cause a time delay which is not acceptable under certain circumstances. Combination at the receiver is implemented using MRC. This method is much more time consuming and complex as compared to AF which is less time consuming and just simple amplification.

Amplify and Forward

Decode and Forward

Underwater Channel Models

Communication in underwater channels suffers from multipath propagation effect mainly due to reflections from bottom and top surfaces of the sea. Obstacle encountered in between source and receiver can also be accounted for this effect. Due to this effect, multiple copies of the same signal arrive from multiple paths causing signal interference and delayed echoes at the receiver. This leads to fading.

Multipath propagation in underwater channels

The data rates are low as communication is through acoustic waves as opposed to electromagnetic waves. There is strong signal attenuation in the form of path loss due to absorption of energy of transmitted signal by sea water especially in cases where the source and receiver are at large distances from each other. Sound frequency and viscosity of water also play a role in absorption. Scattering of signal also accounts for energy loss. Limited bandwidth is another area of concern. Doppler's effect plays comes into play when the distance between the transmitter and the receiver changes due to motion of sea water. This causes relative motion of the transmitter and receiver and alters the result of multipath superposition at the receiver.

In order to make the most efficient use of this medium, we use cooperative transmission and study its advantages over direct transmission. Cooperative transmission techniques have been discussed in the previous chapter. In this chapter, we describe two underwater channel models. The first underwater channel model is based on the Rician model whereas the second model is a geometry-based multipath model. A third model has been discussed towards the end which is a basic terrestrial Rayleigh fading model. This model was studied initially in order to compare the performance of cooperative communication and direct transmission under different conditions as opposed to underwater conditions.

Underwater Channel Model I

The channel model is a geometry based shallow water multipath model. This channel undergoes Rayleigh fading. We look at repeated surface-bottom reflections and take into account a certain number of multipath arrivals, P each of which undergo fading. Each multipath component is characterized by a gain cp and delay time τp which are computed from the propagation path length lp. The path gain magnitude is computed as,

Here Γp ≤ 1 is used to model loss due to reflection (we choose each reflection to introduce a √2 loss in amplitude) and A(lp) is the nominal acoustic propagation loss given by,

Assuming practical spreading, k =1.5, a carrier frequency fc = 15 kHz and absorption according to Thorp. For fc in kHz, a (fc) is given in dB/km as,

We assume a channel having range of 1 km, and the system mounted near the bottom.

The signal received directly from source at the receiver after passing through this channel is,

The signal received at the relay from the source is,

In case of Amplify and Forward, the signal received at the receiver from the relay after amplification is,

In case of Decode and Forward, the signal received at the receiver from the relay after re-encoding is,

Underwater Channel Model II

This is a Rician multipath fading channel. The transmitted acoustic signal is reflected by obstacles and surfaces of the sea. This leads to multiple signals from multiple paths at the receiver. These multiple paths interfere and cause fading. In Rician fading, there is a dominant stationary (non fading) signal component, such as line of sight (LOS) propagation path. There is a superposition of this dominant component with the multipath components. At the output of the detector, a DC component is added to the random multipath. Channel noise is present.

The Rician probability density is given by,

Here 'r' is the complex scaling factor, is average power of received signal, 'A' is amplitude of dominant component and ' is a Bessel function.

Here 'K' is the ratio of power of direct unfaded path to the overall power of the fading paths.

Rician fading scaling factor H is given by,

Here 'hd' is the fading factor due to direct component and 'hs' is the fading factor due to all other scattered components. The scaling factor is multiplicative.

Following are the channel equations,

In case of Amplify and Forward, the signal received at the receiver from the relay after amplification is,

In case of Decode and Forward, the signal received at the receiver from the relay after re-encoding is,

Another problem encountered during signal propagation is loss of intensity due to geometric spreading and absorption of signal energy by sea water. This causes signal attenuation which ultimately affects the signal to noise ratio.

Spreading transmission loss is given by,

Here 'R' is radial distance from the source and 'R1m' is distance at 1m from the source.

Other spreading losses due to decrease in acoustic pressure exponentially is given by,

Here '' is the absorption coefficient.

Signal propagation also suffers from Doppler shift. This effect comes into play when there is relative motion between transmitter and receiver due to motion of sea water. This causes a shift in apparent frequency after propagation and alters the result of multipath superposition at the receiver

For this model we take the value of 'K' to be 2, delay Spread 'Tm' to be 10ms and Doppler fading bandwidth 'Bd' to be 10Hz.

Channel Model III

This channel model is a simple Rayleigh fading channel model with thermal noise. This model was studied initially in order to compare the performance of cooperative communication and direct transmission under different conditions as opposed to underwater conditions.

Rayleigh fading is a special case of Rician fading in which the ratio . Rician distribution becomes Rayleigh distribution when the dominant component (LOS) fades away and multipath components become dominant. Hence fading is due to multipath components only.

It can be clearly seen that there is no affect of dominant component on the above equation.

Taking Rayleigh fading factor to be 'h', the channel equations are,

In case of Amplify and Forward, the signal received at the receiver from the relay after amplification is,

In case of Decode and Forward, the signal received at the receiver from the relay after re-encoding is,

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