Improving QoS in Underwater Wireless ...

International Conference on Advances in Computing and Communication (ICACC-2011), vol. 1, pp. 102-107, 8-10 April 2011

at National Institute of Technology, Hamirpur, Himachal Pradesh, India P R O C E E D I N G O F I E E E-M T T S I C A C C–2011

Improving QoS in Underwater Wireless Communication with MIMO-OFDM Sartajvir Singh1, Ravneet Kaur2, Vishakha Sood3 1,3

RIEIT, Railmajra (Distt. S.B.S. Nagar), Punjab, India 2 SUSCET, Tangori (Mohali), Punjab, India 1

[email protected] 2 [email protected] 3 [email protected] Abstract: - As the technology is increasing in communication field, underwater wireless communication (UWC) has been introduced for military & commercial applications but not advance as Radio wireless communication because in UWC, Bandwidth is extremely limited. The attenuation of acoustic signal increases with frequency and range. A widespread technology Multi Input Multi Outputorthogonal-Frequency Division Multiplexing (MIMO-OFDM) is used in UWC for high data rate signal processing. MIMO-OFDM takes advantage of the multipath properties of environments using base station antennas that do not have Line of Sight (LOS). A set of independent orthogonal subcarriers are used to transmit data. The total bandwidth is divided into a large number of narrowband channels each one non-interfering with each other. In this paper we will investigate the way to improve Quality of Service (QoS) in UWC by achieving maximum possible multiplexing gain of MIMOOFDM using Space-time block coding (STBC) technique due to their ability to take advantage of both space and time diversity to reliably transmit data over a wireless fading channel that provide Inter Symbol Interference (ISI) free transmissions due to orthogonality & also covering some key aspects of the UWC system design such as: Doppler Estimation, Channel Estimation that providing reliable QoS. Thus, their Bit error-rate performance will also improve. Keywords: - Multi Input Multi Output (MIMO), Orthogonal frequency-division multiplexing (OFDM), Quality of Service (QoS), Multiplexing gain, Space-time block coding (STBC), Inter symbol Interface (ISI), Bit Error-rate performance.

I. INTRODUCTION Acoustic propagation in UWC is better on low frequencies while at high ones attenuation blocks high range transmissions. Due to the nature of the signal, the bandwidth of the systems is very limited and usual designs operate within a few tenths of kHz. Low speed propagation of the sound, 1500m/s approximately, causes high end to end delays and a remarkable Doppler effect is of extreme importance when dealing with multicarrier communications. A good understanding of the communication channel is important in the design and simulation of a communication system [1]. Underwater acoustic

communication links can be classified according to their range as very long, long, medium, short, and very short links [2]. Long-range systems that operate over several tens of kilometers may have a bandwidth of only a few kHz, while a shortrange system operating over several tens of meters may have more than a hundred kHz bandwidth. In both cases these factors lead to low bit rates [3]. While Bandwidth is limited in UWC, So diverse QoS guarantees over Underwater wireless networks has created an unprecedented technological-challenge to develop efficient coding and modulation schemes along with sophisticated signal and information processing algorithms to improve the quality and spectral efficiency of wireless communication links [4]. QoS is synonymous with an acceptable signal-tonoise ratio (SNR) level or bit error rate (BER) at the receiver, QoS is usually expressed in terms of minimum rate or maximum delay guarantees. II. BASIC DEFINATIONS A. Multiple-Input Multiple-Output(MIMO) In recent years, the use of multiple-input multiple-output (MIMO) wireless technologies that offer significant promise in achieving high data rates over wireless links have captured a lot of interest. MIMO is an antenna technology where multiple antennas are used in both ends of a transmission in figure 1. Therefore, either in transmitter and/or receiver more than one antenna is deployed. This technique allows increase the bit rate of the communications link without need to increase the transmitter power per antenna & the bandwidth [5]. With a MIMO system, the capacity can be improved by a factor equal to the minimum number of transmit and receive antennas if perfect channel state information (CSI) is available at the receiver, compared with a single-input single-output (SISO) system with flat Rayleigh fading channels [6].

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Fig.1 2x2 MIMO Block Diagram

Fig.2 OFDM Block Diagram

B. Orthogonal-Frequency Division Multiplexing (OFDM) OFDM is a frequency-division multiplexing (FDM) scheme utilized as a digital multicarrier modulation method represents a viable alternative to single-carrier modulation. A set of independent orthogonal subcarriers are used to transmit data. The total bandwidth is divided into a large number of narrowband channels each one noninterfering with each other [7]. Since data is multiplexed on many narrow band subcarriers, OFDM is very robust with typical multi-path fading (i.e., frequency-selective) channels & hence, an attractive way of coping with inter symbol interference (ISI) while providing increased spectral efficiency and improved performance. Furthermore, the sub-carriers can easily be generated at the transmitter and recorded at the receiver using highly efficient digital signal processing schemes based on Fast Fourier Transform (FFT). The main attraction of OFDM lies in its simplicity of implementation via FFT modulation/demodulation, for implementation in the next generation of acoustic transceivers in figure 2.Due to OFDM’s unique strength in handling high-speed transmissions over long dispersive channels with low equalization complexity. The combination of MIMO and OFDM has been designed to improve the data rate and hence QoS by exploiting the multiplexing gain and the diversity gain. C. Multicarrier Modulation Multicarrier modulation in the form of orthogonal frequency division multiplexing (OFDM) has prevailed in recent broadband wireless radio applications due to the low complexity of receivers required to deal with highly dispersive channels [8], [9].This fact motivates the use of OFDM in underwater environments. Multi-carrier modulation offers an alternative to a broadband single-carrier communication. By dividing the available bandwidth into a number of narrower bands, orthogonal frequency division multiplexing (OFDM) systems can perform

equalization in frequency domain and eliminate the need for complex time-domain equalizers. OFDM modulation and de-modulation can easily be accomplished using fast Fourier transforms (FFT) as in Fig.2. Under this model, equalization can be performed by multiplying each flat-fading channel output by a single complex tap value, thereby significantly reducing complexity by eliminating the need for long equalization filters to combat ISI. D. Diversity Diversity leads to improved link reliability by rendering the channel “less fading” and by increasing the robustness against co-channel interference. Diversity gain is obtained by transmitting the data signal over multiple (ideally) independently fading dimensions in time, frequency, and space and by performing proper combination at the receiver. Spatial (i.e., antenna) diversity is particularly attractive when compared to time or frequency diversity, as it does not incur expenditure in transmission time or bandwidth respectively [10]. Space-time coding realizes spatial diversity gain in systems with multiple transmit antennas without requiring channel knowledge at the transmitter. III. SPACE TIME CODING The Space Time Coding is a techniques used to improve reliability in a MIMO link. Redundancy is introduced in the transmitters with the hope that forward error correction (FEC) on the receiver will recover the original, useful data. Note that both the space–time processor and space–time decoding require channel state information [11]. The general structure of the considered system is depicted in Figure 3. The data bits d[i] are fed into the space–time encoder that output L vectors x[k] = [x1[k]……..x [K]] T of length NT. They are transmitted over a MIMO channel. The channel coefficients, [k] = , are assumed to be constant during one encoded frame so that the received signal Becomes [12].

Fig. 3 Structure of transmit diversity system with receive antennas

Y[k] = H · X[k] + N[k] Combining all L vectors x[k], y[k], and n[k] within one coded frame as column vectors into the matrices X, Y, and N, respectively, results in Y=H· X+N

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P R O C E E D I N G O F I E E E-M T T S I C A C C–2011 Space time codes may be divided into two subgroups: Space Time Trellis Codes (STTC): - STTC much complex than block codes, this types of codes distribute a trellis code into several antennas thus providing diversity and coding gain. As the trellis coding is convolutional the receivers relies on the Viterbi algorithm to decode the data, thus increasing the system complexity. Space Time Block Codes (STBC): - This technique is based on constructing a set of orthogonal code words which are transmitted along the antennas. The complexity of it is much less those STTCs and only linear operations are needed. The well-known Alamouti code is just a special case of STBC with double transmit antennas. Since STTCs are far more complex, STBCs will be explained in a more detailed manner. In the system deployed in SPACE'08 experiment Alamouti Coding was used. In each time block, a total of Mt symbols are received. These symbols can be expressed in a matrix forming a space-time symbol which will define the coding type.  D= ⋮ 

 ⋯  ⋱ ⋮   ⋯ 

Where  represents the symbol sent on the transmitter “M” at time instant “N”. The result of this is simple, linear and optimal decoding at the receiver. Many coding techniques, the use of redundancy make the system sacrifice its data rate. The rate of the code is given by the number of encoded symbols in one time block (note that many transmitters can send the same symbol) divided by the number of time slots necessary to complete the space-block symbol. In the receiver side, using optimal decoding scheme, the bit-error rate (BER) behavior of the Alamouti's code is equivalent to a Maximum Ratio Combining (MRC) of 2 symbols over  Receivers. This is a result of the perfect orthogonality between the symbols after receive processing: there are two copies of each symbol transmitted and  Copies received. Maximum likelihood decoding is performed with the only need of linear operations, thus maintaining the system complexity low, recall that the symbols will not be recovered after 2 time slots (N for a general STBC) thus introducing a little delay [11].

Fig.4 Bit error rate of Alamouti’s scheme for different modulation types and number of receive antennas

Although the commented scheme was for STBCs, STTCs are more robust against errors, but the receiver complexity is higher as dynamic programming algorithms are needed on the receivers for correct data decoding. The use of space time coding permits MIMO systems to operate with more transmitters than receivers. IV. TRANSMITTER DESIGN We consider a MIMO-OFDM transmission with two transmitters. Within each OFDM block, two independent data streams are encoded with either a convolutional code or a low-density paritycheck (LDPC) code [13]. The coded bits are mapped into information symbols using BPSK modulation, bits are also interleaved before modulation if convolutional code is used. Two OFDM blocks are formed from the two streams of information sequences and transmitted through two  transmitters simultaneously. On each transmitter, we use the zero-padded (ZP) OFDM format as in Table 1 [14]. Table 1 Signal bandwidth

B=12KHz

OFDM block duration Subcarrier spacing

 =25ms

Number of subcarriers

K=1024

Number of data carriers

 =K/4 256

Guard interval

Number of pilot carriers Number of null subcarriers

T=81.92ms ∆F=11.72Hz

 =672  =96

With BPSK modulation and parallel data streams from two transmitters, the uncoded data rate is 

!"#

= (2 × 2 × )/T +  = 24.36 kbps

Over the 12 kHz bandwidth, where  is the number of data carrier, T is the OFDM block

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P R O C E E D I N G O F I E E E-M T T S I C A C C–2011 duration, and  is the guard time. For coding, we use a 16-state rate 1/2 convolutional code with the generator polynomial (23, 35), and a rate 1/2 regular LDPC cycle code over Galois Field GF (64) with (n, k) = (1344, 672) bits [15]. With rate 1/2 coding, the overall data rate is !"# =.5 

!"#

= 12.18 kbps.

For each OFDM block, we generate  independent bit streams each of length !  log2M and encode them separately using the nonbinary low-density-parity-check (LDPC) codes [15]. Each coded bit stream of length  log2M is mapped into a symbol sequence of length  . A total of  OFDM blocks are formed with each block carrying  symbols from one symbol sequence. After proper pilot insertions, the  OFDM blocks are transmitted from  transmitters simultaneously. V. RECEIVER ALGORITHM The receiver algorithm used for data detection in the SPACE'08 experiment is the one explained in [16] and an overview is presented here commenting also the particularities of the MIMO channel. As a result of sending the signal through a channel, the received signal can be expressed in the frequency domain after the FFT demodulation as ) $ % (n)= ∑* '%( (n) % +,-. /+- 1  ,% +,-





0

Where t, r, k, n refer to the transmitter, the receiver, the frequency index and the time respectively is referring to the channel frequency response and n to the noise component of the received signal. A. Channel Estimation A critically important development for underwater acoustic communications was the demonstration of the feasibility of high-data-rate coherent modulation and detection in underwater acoustic communications [17]. The receiver employed an adaptive decision-feedback equalizer (DFE) with embedded carrier recovery. A decision-feedback equalizer is a nonlinear equalizer that contains a forward filter and a feedback filter. Unlike the linear equalizer, the DFE doesn't have to estimate the explicit InterCarrier Interference (ICI) coefficients. In the classical way, the DFE was widely used for InterSymbol interference (ISI) cancellation. In OFDM, information is sent among independent channels, so ICI can be treated as ISI but in a causal and anti-causal way. In ISI approaches, only symbols that were sent before would affect the current decision, but in the frequency domain and ICI, the

influence comes from the higher and the lower subcarriers [18]. The equalizer coefficients and carrier recovery parameters can be jointly estimated according to a minimum mean square error (MMSE) criterion [19]. B. Doppler Estimation The Doppler Effect can be viewed as caused by carrier frequency offsets (CFO) among the transmitters and the receivers [20], [21]. On each receiver, we assume a common CFO relative to all transmitters, as in [3, Chapter 11.5]. Hence, the CFO estimation algorithm in Section IV.B of [22] is directly applicable, where the energy on the null subcarriers is used as the objective function to search for the best CFO estimate. After Doppler shift estimation and compensation, the average energy on the null subcarriers is used to compute the variance of the additive noise and residual inter-carrier interference (ICI). This quantity is needed for the soft MMSE equalization. Relative motion of the transmitter and receiver induces Doppler spread. Noise is introduced by wind, shipping traffic, and various forms of ocean life. The presence of Doppler spread, due to source/receiver motion as well as motion of the water column (waves) that may not be well represented by a simple Doppler shift. VI. MIMO-OFDM SYSTEM A MIMO-OFDM system with four transmits and receives antennas is shown in Fig. 7. Though the figure shows MIMO-OFDM with four transmit antennas, the techniques developed in this paper can be directly applied to OFDM systems with any number of transmit antennas [5]. At time, each of two data blocks is transformed into two different signals, through two space–time encoders. When used with space-time coding (coding across both spatial and temporal axes), reliability can be further enhanced through the use of iterative equalization and decoding methods, similar to those used in the single-input/singleoutput (SISO).

Fig. 5 MIMO-OFDM system

The OFDM signal for the 23 transmit antenna is modulated by the 43 tone of the nth OFDM block. From the figure 5, the received signal at

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P R O C E E D I N G O F I E E E-M T T S I C A C C–2011 each receive antenna is the superposition of four distorted transmitted signals. To achieve transmit diversity gain and detect the transmitted signal, a space–time processor must extract the required signals for space–time decoders. VII. CONCLUSIONS We provided a brief overview of MIMO-OFDM technology in underwater wireless communication covering some key aspects of the system design such as; channel modeling, ICI analysis, channel estimation, Doppler estimation and space time block coding aimed at increasing the transmission rate and providing reliable QoS to users. . The use of multiple receivers for spatial diversity gains is becoming common in underwater communication systems. A modulation method offers low-complexity solutions for high rate communications over bandlimited acoustic channels. With gains from MIMO processing, we expect that more communication systems will include multiple transmitters in the future. As researchers master the techniques required for point-to-point communication links in the next 5-10 years, we expect that the research emphasis on underwater networking will increase. REFERENCES [1]

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