INTRODUCTION 1. Overview of the Dissertation Underwater acoustic (UWA) communication systems have been devel- oped for the past three decades [25]. They can be used in potential applications such as environmental monitoring, o shore oil exploration, and military missions. Nevertheless, UWA communications have a plethora of diculties, so they display many challenges for further developments. The reason can be explained by a large demand on high frequency utilization as well as high data rate access under very complexity shallow underwater environments. All these requirements, without doubt, call for intensive research e orts on how to cope with problems faced by current UWA communications, e.g., limited availability of acoustic frequency spectrum, complex time variations in UWA fading channels, and urgent needs for good quality of service. Therefore, this dissertation is devoted to investigate UWA communication systems by considering all these challenges. In particular, two goals are aimed at, which are known as: i) UWA channel modeling and ii) performance analysis of UWA communication systems The design, development, performance analysis, and test of such communication systems, however, call for a deep insight of the most important characteristics of real-world propagation environments. Similar to the other communication fashions, channel modeling is an initial investigation because it provides hints to predict performance of communication systems before doing further high cost implementations as hardware designs [75, 96]. The task of channel modeling is to reproduce the real channel conditions. In other words, the statistical properties of the real channel such as path loss, multipath fading and Doppler e ect should be represented by channel modeling. For this reason, this dissertation presents the analysis and modeling UWA channels in shallow water en-
MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY DO VIET HA MÔ HÌNH ĐẶC TÍNH KÊNH TRUYỀN CHO THÔNG TIN THỦY ÂM VÙNG NƯỚC NÔNG CHANNEL MODELING FOR SHALLOW UNDERWATER ACOUSTIC COMMUNICATIONS DOCTORAL THESIS OF TELECOMMUNICATIONS ENGINEERING HA NOI - 2017 INTRODUCTION Overview of the Dissertation Underwater acoustic (UWA) communication systems have been developed for the past three decades [25] They can be used in potential applications such as environmental monitoring, offshore oil exploration, and military missions Nevertheless, UWA communications have a plethora of difficulties, so they display many challenges for further developments The reason can be explained by a large demand on high frequency utilization as well as high data rate access under very complexity shallow underwater environments All these requirements, without doubt, call for intensive research efforts on how to cope with problems faced by current UWA communications, e.g., limited availability of acoustic frequency spectrum, complex time variations in UWA fading channels, and urgent needs for good quality of service Therefore, this dissertation is devoted to investigate UWA communication systems by considering all these challenges In particular, two goals are aimed at, which are known as: i) UWA channel modeling and ii) performance analysis of UWA communication systems The design, development, performance analysis, and test of such communication systems, however, call for a deep insight of the most important characteristics of real-world propagation environments Similar to the other communication fashions, channel modeling is an initial investigation because it provides hints to predict performance of communication systems before doing further high cost implementations as hardware designs [75, 96] The task of channel modeling is to reproduce the real channel conditions In other words, the statistical properties of the real channel such as path loss, multipath fading and Doppler effect should be represented by channel modeling For this reason, this dissertation presents the analysis and modeling UWA channels in shallow water en1 vironments, which have strong multipath and Doppler effects on signal propagations [97] Without discussing the performance of UWA communication systems under different propagation environments, this study seems to be unfinished In this view point, Orthogonal Frequency Division Multiplexing (OFDM) has been widely applied to acoustic transmission [10, 22, 42, 68, 86] since it can mitigate inter-symbol interference as well as has higher spectral efficiency than single carrier systems Thus, for the sake of completeness, we utilize analyses such as the signal-to-interference ratio (SIR), the signal-to-interference-plus-noise ratio (SINR), and the channel capacity to determine the performance of the UWA-OFDM systems We believe that the performance assessment reported here bridges the gap between the derived UWA channel models and their impact on the performance of the deployed UWA communication systems This chapter presents the general concepts in UWA channel modeling and a brief introductions to UWA channel characteristics Moreover, the motivations and the major contributions of this dissertation are highlighted in the remainder of this chapter Characteristics of Shallow Underwater Acoustic Channels The physical characteristics of UWA propagation environments are very different from those of terrestrial ones with electromagnetic waves UWA channels can be characterized by three main aspects [12, 30, 88]: the high transmission loss depending on signal frequencies, the time-varying multipath propagation, and the low transmit speed of sound in water (about 1500m/s) The fast time variations of UWA channels are mainly caused by the relative movement [88], internal waves [32], and surface waves [16, 82] These features make UWA channels the most difficult communication media in use today [88], and give rise to critical challenges for further developments Acoustic Frequency The frequency of underwater acoustics is in the range from 10 Hz to MHz [92] When the bandwidth is between 10÷20 percent of the center of signal, the communication system is called wide-band Although the bandwidth of UWA communication systems is small, the signal frequency is also small Thus, UWA communication systems are wide-band due to the low relative center frequency in comparison with the bandwidth [88] Transmission Loss The transmission loss of UWA propagation significantly depends on the signal frequency The three factors that attenuate UWA signals include spreading loss, absorption loss, and scattering loss The overall path loss A (l, f ) is defined as [88] l k l−l A (l, f ) = α(f ) r , lr where f is the signal frequency and l is the transmission distance, taken in reference to some lr The symbol k is the path loss exponent, which model the spreading loss and its value are usually between and The absorption coefficient α(f ), which increases rapidly with signal frequency, can be obtained using an empirical formula [20] Noise Noise in UWA channels consists of ambient noise and site-specific noise Ambient noise is always present in the background of the sea, while site-specific noise is unique to certain places The first one is often modeled as Gaussian and it is not white, while the latter one contains significant non-Gaussian components The power spectral density of ambient noise decays at a rate of approximately 18 dB/decade The attenuation growing with frequency whereas the noise decays with frequency, result in a signal-to-noise ratio (SNR) that varies over the signal bandwidth [88] Propagation Delay The speed of UWA waves increases with the salinity, temperature, and pressure of the water In shallow water environments, the temperature and pressure are almost unchanging; thus, the speed of sound in shallow water is considered to be a constant value (about 1500 m/s) The propagation delay τ can then be obtained as d τ= c where d and c are the propagation distance (in meters) and the speed of sound (in m/s), respectively Because of the low speed of sound, the propagation delay τ = d/c is about tens milliseconds for transmission distances of longer than ten meters Multipath In shallow water environments, the propagation of sound appears to be a complicated multipath, which is mainly caused by reflections at the surface and bottom The multipath interference in UWA communication systems is illustrated in Fig Each path has its propagation delay depending on its geometry The maximal propagation delay is called as the delay spread of the UWA channel Because of the multipath effect, the received signal is composed of various paths with different amplitudes, propagation delays, and phase shifts Figure 1: Multipath interference in UWA communication systems Doppler Effect Another characteristic of UWA channels is time varying, which is caused by two factors The first one is the result of the relative movement between the transmitter (Tx) and the receiver (Rx), while the latter one is caused by inherent changes in the transmission medium such as the changes in weather, surface wave, and storm, etc [9] A relative movement between Tx/Rx or a moving medium results in the change of frequency of the acoustic waves, which is called as Doppler shift An expression for the maximum Doppler frequency shift fD,max is given by [19] v fD,max = fc , c where fc and c are the transmitted signal frequency (i.e carrier frequency) and the sound speed, respectively; v stands for the speed of the observer The magnitude of Doppler effect is determined by the ratio a = v/c named as the relative Doppler shift, which is significant to the carrier frequency due to the low speed of sound The non-negligible Doppler shift is a distinctive characteristic of UWA channels in comparison with the radio channel Moreover, even without intentional movements, the inherent changes in transmission medium such as waves or drifts of transducers also lead to the Doppler shift In shallow water environments, reflections from the surface are the main reason of time-variant UWA channels The Doppler spread presents the spectral width spreading of the received signal, which depends on the wave height, wind speed, reflections from the surface and bottom of the sea UWA Channel Modeling Approaches and the State-of-the-Art The characteristics of UWA channels are very complex due to Doppler effects, high attenuations depending on signal frequencies, multipath effects, and additive color noises Therefore, it is very difficult to model exactly UWA channels, especially in shallow underwater environments, which have strong multipath effects on the signal UWA channel modeling is not new research in underwater communication systems However, over the past few decades, although large variety of UWA channel models have been proposed, there is still no typical model that can be applied for all UWA channels because of differences in geographical areas, weather conditions, and seasonal cycles [24, 70, 73, 88, 93, 96] Recent approaches of designing UWA simulators in literatures are classified into two main categories, which are the geometry and the measurement-based The UWA geometry-based simulator has been designed by using the geometrical channel model The well-known Bellhop code [69] is one of popular examples of this simulator The code built the UWA channel simulator by using the ray theory for a given geometry, but it did not consider the random channel variation [75] To deal with this issue, some studies run the Bellhop model in combination with environment conditions, such as temperature and salinity [89], wind speeds [28], and surface shapes [37] The simulated UWA channels obtained through such Bellhop channel simulator showed the statistical properties that are similar to those of the real UWA channels in some experimental scenarios The difficulty of specifying the environment conditions is one of the limitations of this simulator Another kind of the UWA geometry-based simulator is developed by combining the ray theory with statistical methods to describe the UWA propagation environment [13, 17, 27, 55, 73, 75, 103, 104] The statistical properties of the UWA channel were analyzed by using the probability density function (PDF) of the angle-of-arrival (AOA), and the angle-of-departure (AOD) as key parameters The AOD is, however, a derivative parameter of the AOA [55] In some research studies, the PDFs of the AOAs are assumed to be normally [103, 104] or uniformly [75] distributed Besides, in [13], the author approximated the PDF of AOA with the half-circular Rice PDF The geometry-based simulator can describe the overall UWA channel with fewer estimated channel parameters than the measurement-base one, and it is feasible to extend from one transmission environment to others without significant efforts However, the geometrical modeling is not able to provide the statistical characteristics of the simulated channel, which is close to those of the real UWA channel This is because of the time and spatially varying characteristics of the shallow UWA propagation environments The UWA measurement-based channel modeling approach have been investigated in [24, 74, 76, 85, 105] Almost all of these channel simulators are developed from given measurement data, which are obtained from a specific underwater environment Based on analyzing the measurement data, the distribution of the propagation paths are specified such as Rayleigh [24, 85], Rician [76], K-distributed [105], and lognormal [74] Furthermore, in the replay-based simulators [58, 83, 95], the time variant channel impulse responses (TVCIRs) of the measured UWA channel can be reproduced; or a new random TVCIR can be gener- ated so that its statistical properties are similar to those of the measured channel The measurement-based simulator does not require physical input parameters, which may not be easy to set In addition, the simulated channels obtained by this simulator are extremely realistic based on actual measurement data The disadvantage of the measurement-based simulator is that it can be only applied to the specific transmission environment, where the channel is measured The best way to expand the diversity of this simulator is to collect a large amount of measurement data at different time and locations [84] Moreover, for designing the measurement-based channel simulator, a large number of channel parameters, including path gains, Doppler frequencies, propagation delays, and phase shifts need to be estimated [56] There are some efficient computation algorithms to estimate these parameters, such as the rotational invariance techniques (ESPRIT) [34], the space-alternating generalized expectation-maximization (SAGE) [33], the iterative nonlinear least square approximation (INLSA) [31], the Lp -Norm Method (LPNM) [59] The measurements and computation efforts to estimate the large number of channel parameters make the measurement-based simulator more complex than the geometry-based one In Vietnam, despite of a growing need of UWA communication applications in the military and commerce, there is not many research papers on UWA communication, especially in the field of channel modeling [2, 3, 6] Some characteristics of UWA propagations in Vietnam sea have been investigated in some earlier research [1, 4, 5, 7, 8]; however, the results of UWA channel modeling have not been given In [6], the authors have simulated the UWA propagation rays by solving the Eikonal equation for given environmental conditions As mentioned above, these environmental parameters are hard to be specified due to the complexity of UWA propagation environments Besides, the simulated UWA propagation rays is time-invariant that may not be able to describe the real UWA channel in most of cases Goals of the Dissertation This dissertation aims at developing accurate and efficient approaches of designing shallow UWA channel simulators based on the measurement data of the real UWA channel in a specific shallow water environment The proposed approaches should fulfill the following requirements: • They should enable the accurate simulation of the shallow UWA channel characterized by the measured channel impulse response (CIR), power delay profile (PDP), and/or Doppler power spectrum • Determinations of the channel simulation model parameters should be done in a simple and efficient manner • The simulators designed by the proposed approaches should be suitable for the performance analysis of the UWA communication system based on Orthogonal Frequency Division Multiplexing (OFDM) technique To accomplish these goals, two simple and effective approaches are proposed for the design of UWA channel simulators for the two cases: (i) Fixed transmitter (Tx) and receiver (Rx) and (ii) Fixed Tx and Rx moving The simulation results show that the proposal of design approaches emulate the statistical properties of the measured UWA channels with high accuracy Furthermore, the statistical properties of UWA channels in terms of Doppler power spectral densities (PSDs) is also the objective of this dissertation In this respect, we present a thorough analysis of Doppler effects of shallow UWA channels having a time-variant surface motion and relative Tx/Rx movement As a result, the closed-form expression of Doppler power spectrum is proposed and validated through the measurement data Using the measurement-based UWA channel simulation model, a detailed analysis on the performance of UWA-OFDM communication systems was presented; then, appropriate transmission parameters such as the signal bandwidth, the number of sub-carriers, and the transmit power would be selected Scope and Delimitations The scope of the dissertation is for the approaches of designing shallow UWA channel simulators The design of measurement-based UWA channel simulators, which are derived from the measurement data of the real UWA channel in a specific shallow water environment, is mainly focused The aspects look into were the multi-path and Doppler effects of the measured shallow UWA channels The measurement results have been used for the input data of the simulators All parameters of the UWA channel simulation model are then derived from the measurement data without considering the physical aspects of the acoustic wave propagation Therefore, the obtained UWA channel simulation model is just valid for the specific transmission environment that the UWA channel is measured The dissertation does not cover the analysis of effects of geometrical or environmental parameters (e.g., the water depth, the salinity, the temperature, etc ) on the measured UWA channel The measurement data itself reflects the influence of these parameters Furthermore, the study only concentrates on the shallow environments; thus, the channel simulation model used in this dissertation does not capture the characteristics of UWA channels in the deep water Motivations and Contributions of the Dissertation In the previous section, the complexity of UWA channels has been discussed Furthermore, there is still no typical model that can be applied for all UWA channels because of the differences in geometrical and environmental conditions This implies that we have to consider the UWA channel characteristics specific to each different environments and locations Hence, the studies of UWA channels and their characteristics such as path loss, delay and Doppler spread have been paid much attention for implementing UWA channel simulators as well as real UWA communication systems For the design of UWA channel simulators, we can implement the previously mentioned approaches, geometry-based and measurement-based ones Each approach has its own advantages and disadvantages The performance of each approach is analyzed by comparing the statistical 100 water environment A closed-form expression of Doppler power spectrum model for underwater acoustic (UWA) channels was proposed The theoretical background of Doppler effects generated by the transmitter/receiver (Tx/Rx) movement, or by the motion of sea-surface was analyzed by using the geometry model for shallow UWA channels As a result, the Doppler power spectrum can be modeled as a summation of the Spike-shape and the Gaussian-shape The Spike-shape presents the Doppler component from the Tx/Rx movement, while the Gaussianshape presents the Doppler component from the sea-surface motion The proposed model is validated through curve fitting with the Doppler power spectrum measurement results of a real shallow UWA channel in Halong bay, Vietnam The optimal parameters of the proposed model are derived from the measurement results by applying an optimization algorithm called the Lp-norm method The curve fitting results show that our proposed model matches well with the measurements Therefore, the proposed Doppler model can accurately describe the Doppler effects for shallow UWA channels The proposed model can be used to design UWA channel simulators for the performance evaluation of UWA communication systems By using the measurement-based UWA channel model, the performance of UWA-OFDM systems under the influence of both the ICI and the noise effect was analyzed The UWA channel model was examined regarding the channel characteristics, ambient noise, and Doppler effect In contrast to other studies, which considered ocean noise as white noise, we calculated the SINR in the presence of both the ICI and the ambient noise as a function of the signal bandwidth and number of sub-carriers The system capacity of the UWA-OFDM system are then derived from the SINR results Moreover, the required transmit power for a given system capacity and transmission band was analyzed The transmit power should be chosen carefully in order to obtain the desired SNR by minimizing the ICI effect The optimized results of the number of sub-carriers, the 101 signal bandwidth, and the transmit power, provide practical guidelines for choosing proper transmission parameters for the considered UWA-OFDM system B Futures research directions This dissertation studied the approaches of designing shallow UWA channel simulators The statistical characterization of channel models and the performance analysis of the simulators were addressed For effective design of UWA channel simulators, a Doppler power spectrum model for UWA channels was proposed Furthermore, in this dissertation, channel modeling and system performance analysis are unified into one for more general visualization rather only demonstrate on one of them as most papers have done so far However, there is still unsolved problem, which will be described in the following • In this dissertation, the effective approach of designing UWA channel simulators was proposed For the case of static UWA channel (i.e there is no relative movement between the Tx and Rx), all channel parameters of the proposed simulator are exploited from the measurement data However, for the case of moving Tx/Rx, Doppler frequencies of propagation paths still need to be computed by the optimization method In the future work, we will apply the proposed Doppler model to obtain the Doppler frequencies without applying any optimization method • In the future work, we will apply the proposed Doppler model to evaluate the Doppler effect on the performance of UWA communication systems The inter-carrier interference (ICI) resulting from the Doppler effect in UWA-OFDM systems can be analyzed by using the proposed model Based on the analytical results, Doppler compensation algorithms can be proposed • In this dissertation, the UWA-OFDM system performance was analyzed by using the measurement-based UWA channel and the theoretical approximated ambient noise In the future work, we will 102 develop the noise model based on the actual measurement data and use it to analyze the system performance • The performance analysis of the MIMO UWA-OFDM system should be implemented APPENDIX Verification of the Relation between the Spike Doppler Frequency and the Rx Speed To compare the estimated Rx speed Vn,R from Eq 2.14 with the setting Rx speed VR , we launched a measurement in West Lake, a shallow water environment, in Hanoi, Vietnam on February 14, 2017 The water depth was about m while the Tx transducer and Rx hydrophone were secured at a depth of m The experiment configuration is set up as illustrated in Sect 2.3.1 The Doppler spectrum measurement data was collected for two different scenarios while keeping the consistent Rx speed of 0.5 m/s In the first scenario, the Rx, at a distance of 100 m, starts to move towards the fixed Tx Firstly, the Rx speed is increased, and when it reached the value of 0.5 m/s, we keep it unchanged for interval of 15 s Then, we collected the received signal during this interval The measurement result of Doppler spectrum is obtained by applying the concept of the spectrogram in Sect 2.3.3 Subsequently, the optimal parameters are estimated by using the proposed model in Eq 2.9 and the method of parameter optimization LPNM in Sect 2.4 The results of these parameters are A = −17.6 dB, fm = 0.6876 Hz, w = 6.4615 Hz, C = 202.8722, and fSpike = 4.1632 Hz Using the optimal value of fSpike = 4.1632 Hz and Eq 2.14, we can estimate the speed of Rx Vn,R = 0.5204 m/s, which matches well with the actual Rx speed VR = 0.5 m/s In a similar way, for the second scenario, the Rx moves away from the fixed Tx at a distance of 50 m We measured and modeled Doppler spectrum when the Rx speed is 0.5 m/s The optimal parameters of the proposed model A = −12.4, dB, fm = −0.9642 Hz, w = 5.7981 Hz, C = 240.0687, and fSpike = −4.3299 Hz are derived from the measurement results Consequently, the Rx speed Vn,R = 0.54124 m/s is computed from the optimal value of fSpike = −4.3299 Hz This speed matches well with the setting speed of Rx VR = 0.5 m/s 103 104 a) The Rx moves towards the fixed Tx -5 -10 Measured spectrum Reference model Proposed Model -15 -20 -25 -30 -35 -40 -20 -15 -10 -5 Doppler shift [Hz] 10 15 20 Normalized Doppler spectrum [dB] Normalized Doppler spectrum [dB] The comparison between the reference model and the proposed model for each scenario is shown in in Fig 3.12 It can be seen that the proposed model is in good agreement with the reference model (i.e the measurement data) b) The Rx moves away from the fixed Tx -5 -10 -15 -20 -25 -30 Measured spectrum Reference model Proposed Model -35 -40 -20 -15 -10 -5 10 15 20 Doppler shift [Hz] Figure 3.12: Results of Doppler spectrum measurement and modeling while the Rx moves with the consistent speed of VR = 0.5 m/s LIST OF PUBLICATIONS C1 Ha, D V.; N Van Duc, and M Patzold (2015), SINR analysis of OFDM systems using a geometry-based underwater acoustic channel model, in ”IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)”, pp 683-687 C2 D H Do; Q K Nguyen; D V Ha ; N Van Duc (2016), A time synchronization method for OFDM-based underwater acoustic communication systems, in ”2016 International Conference on Advanced Technologies for Communications (ATC)”, pp 131-134 J1 Ha, D V.; T V Chien; V D Nguyen (2016), Proposals of multipath timevariant channel and additive coloured noise modelling for underwater acoustic OFDM-based systems, International Journal of Wireless and Mobile Computing, Vol 11, No 4, pp 329-338 J2 Ha, D V; V D Nguyen (2016), Methods of designing shallow underwater acoustic channel simulators, Acoustics Australia, Vol 44, No 3, pp 439-448 J3 Ha, D V; V D Nguyen; Q K Nguyen (2017), Modeling of Doppler Power Spectrum for Underwater Acoustic Channels, Journal of Communications and Networks, Vol 19, No 3, pp 270-281 105 Bibliography [1] Cao, Bùi Văn ( 2012) Nghiên cứu ảnh hưởng yếu tố môi trường biển đến cự ly hoạt động thiết bị thủy âm Tạp chí Khoa học Công nghệ Hàng hải số 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These parameters should be chosen carefully in order to obtain the desired capacity and SINR with minimizing the ICI effect The results provide practical guidelines for choosing proper transmission... is infinite, then the discrete variables xi,n and αi,n become continuous variables x and α, respectively Consequently, the continuous forms of parameters, including the propagation distance Di... characteristics such as path loss, delay and Doppler spread have been paid much attention for implementing UWA channel simulators as well as real UWA communication systems For the design of UWA channel