Tài liệu Mô hình đặc tính kênh truyền cho thông tin thủy âm vùng nước nông (“channel modeling for shallow underwater acoustic communications”)

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 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 Specialization: Telecommunications Engineering Code No: 62520208 DOCTORAL THESIS OF TELECOMMUNICATIONS ENGINEERING SUPERVISORS: 1. Assoc.Prof. Van Duc Nguyen 2. Dr. Van Tien Pham Hanoi - 2017 DECLARATION OF AUTHORSHIP I hereby declare that this dissertation titled, "Channel Modeling for Shallow Underwater Acoustic Communications”, and the work presented in it are entirely my own original work under the guidance of my supervisors. I confirm that: • This work was done wholly or mainly while in candidature for a PhD research degree at Hanoi University of Science and Technology. • Where any part of this dissertation has previously been submitted for a degree of any other qualification at Hanoi University of Science and Technology or any other institution, this has been clearly stated. • Where I have consult the published work or others, this is always given. With the exception of such quotations, this dissertation is entirely my own work. • I have acknowledged all main source of help. • Where the thesis is based on work done by myself jointly with others, I have made exactly what was done by others and what I have contributed myself. SUPERVISORS Hanoi, August 27, 2017 PhD STUDENT 1. Assoc.Prof. Van Duc Nguyen 2. Dr. Van Tien Pham Do Viet Ha ACKNOWLEDGEMENTS First and foremost, I would like to thank my advisor Associate Prof. Dr. Nguyen Van Duc for for providing an excellent atmosphere for doing research, for his valuable comments, constant support and motivation. His guidance helped me in all the time of research and writing of this dissertation. I could not have imagined having a better advisor and mentor for my PhD. I would also like to thank Dr. Pham Van Tien for their advice and feedback, also for many educational and inspiring discussions. My sincere gratitude goes to the members in the Wireless Communication Lab, School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam. Without their support and friendship it would have been difficult to complete my PhD studies. I am also thankful to Dr. Nguyen Tien Hoa for his invaluable instructions in presenting my dissertation. I would also like to express my deepest gratitude to my parents, my husband, my son, and my daughter. They were always supporting me and encouraging me with their best wishes, they were standing by me throughout my life. Hanoi, August 27, 2017 PhD STUDENT Do Viet Ha Contents TABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ABBREVIATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Chapter 1. DESIGN OF SHALLOW UWA CHANNEL SIMULATORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.2. Overview of Simulation Models for UWA Channels . . . . . . . . 1.2.1. Rayleigh and Rice channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2. Deterministic SOS Channel Models . . . . . . . . . . . . . . . . . . . . . 1.2.3. Deterministic SOC Channel Models. . . . . . . . . . . . . . . . . . . . . 19 19 20 21 1.3. The Geometry-based UWA Channel Simulator . . . . . . . . . . . . . 21 1.3.1. Developing the Reference Model from the Geometrical Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.3.2. The Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.3.3. The Estimated Parameters of the Simulation Model . . . . 27 1.3.4. Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.4. The Measurement-based UWA Channel Simulator . . . . . . . . . . 1.4.1. The Reference Model from the Measurement Data . . . . . . 1.4.2. The Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3. Estimated Channel Parameters of the Simulation Model 1.4.4. Comparison of the Two Channel Simulators . . . . . . . . . . . . 28 29 32 33 34 1.5. The Proposed Approach for the Static UWA Channel . . . . . . 1.5.1. Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2. Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 36 38 i ii 1.6. The Proposed Approach for the Case of Doppler Effects . . . . 1.6.1. The Measurement Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.2. The Conventional Measurement-based Simulators . . . . . . . 1.6.3. The Proposed Channel Simulator . . . . . . . . . . . . . . . . . . . . . . . 39 40 41 45 1.7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Chapter 2. MODELING OF DOPPLER POWER SPECTRUM FOR SHALLOW UWA CHANNELS . . . . . . . . . . . . . . . . . . . . 53 2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.2. The Proposed Doppler Spectrum Model . . . . . . . . . . . . . . . . . . . . 2.2.1. The Doppler Effects in Shallow UWA Channels . . . . . . . . . 2.2.2. The Proposed Doppler Model for UWA Channels . . . . . . . 56 56 59 2.3. The Description of Doppler Spectrum Measurements . . . . . . . 2.3.1. Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2. Measurement Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3. Reference Model from the Measurement Data . . . . . . . . . . . 63 63 64 66 2.4. Parameter Optimizations of the Proposed Model . . . . . . . . . . . 67 2.5. Measurement and Doppler Modeling Results . . . . . . . . . . . . . . . 2.5.1. Scenario 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2. Scenario 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.3. Scenario 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 69 71 75 2.6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Chapter 3. UWA-OFDM SYSTEM PERFORMANCE ANALYSIS USING THE MEASUREMENT-BASED UWA CHANNEL MODEL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 78 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 3.2. ICI Analysis of UWA-OFDM Systems . . . . . . . . . . . . . . . . . . . . . . 3.2.1. SIR Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2. Ambient Noise Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3. SINR Calculation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 82 83 84 3.3. Capacity Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 iii 3.4. Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1. The SIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2. The SINR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3. Channel Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4. Transmit Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 88 89 90 92 3.5. Chapter Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 LIST OF PUBLICATIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 ABBREVIATIONS ACF Autocorrelation Function AOA Angles of Arrival AOD Angles of Departure AWGN Additive White Gaussian Noise BPSK Binary Phase Shift Keying CIR Channel Impulse Response FCF Frequency Correlation Function ICI Inter-Channel Interference INLSA Iterative Nonlinear Least Square Approximation ISI Inter-Symbol Interference LNA Low Noise Amplifier LOS Line of Sight LPNM Lp-Norm Method MESS Method of Equally Spaced Scatterers MSE Mean Square Error OFDM Orthogonal Frequency Division Multiplexing PDF Probability Density Function PDP Power Delay Profile PN Pseudo-Noise PSD Power Spectra Density Rx Receiver SINR Signal to Interference plus Noise Ratio SIR Signal-to-Interference Ratio SNR Signal to Noise Ratio SOC Sum-of-Cisoids SOS Sum-of-Sinusoids TCF Time Correlation Function iv v T-FCF Time-Frequency Correlation Function TVCIR Time Variant Channel Impulse Response TVCTF Time-Variant Channel Transfer Function Tx Transmitter UWA Underwater Acoustic WLAN Wireless Local Area Network WSSUS Wide-Sense Stationary Uncorrelated Scattering List of Figures 1 Multipath interference in UWA communication systems. . . . . . . . . . . 4 1.1 The methodology behind the geometry-based channel modelling [17, 55]. . 17 1.2 The methodology behind the measurement-based channel modelling [31, 56]. 18 1.3 The scheme of designing the geometry-based channel simulator [17, 55]. . . 22 1.4 The geometrical model for shallow UWA channels with randomly distributed scatterers Si,n (•) on the surface (i = 1) and the bottom (i = 2) [55]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.5 The comparison between the normalized FCF of the reference model and that obtained by the geometry-based simulator. . . . . . . . . . . . . . . . 29 1.6 1.7 Illustration of the measurement setup in Halong bay. . . . . . . . . . . . . 30 ˆ The measured |h(τ, t)|2 for the transmission distance of 150 m. . . . . . . 31 1.8 The measured and normalized PDP ρ(τ ) obtained for the transmission distance of 150 m. 1.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 The comparison of the normalized FCF obtained by the two simulators to that of the reference model. . . . . . . . . . . . . . . . . . . . . . . . . . 35 1.10 The flowchart of proposed approach to design the static UWA channel simulator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 1.11 The comparison between the normalized FCF of the reference model and that obtained by the measurement-based, the geometry-based, and the proposed simulators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 1.12 The normalized Doppler power spectrum. . . . . . . . . . . . . . . . . . . . 41 1.13 a) The reference T-FCF derived from the measurement results. b) The T-FCF of the channel simulation model designed by the conventional simulator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 1.14 The comparison between the normalized T-FCF of the reference model and that obtained by the conventional measurement-based simulator. . . . 44 1.15 The flowchart of the proposed approach for the case of moving Rx. vi . . . . 46 vii 1.16 a) The reference T-FCF derived from the measurement results. b) The T-FCF of the channel simulation model designed by the proposed simulator. 48 1.17 The comparison between the normalized T-FCF of the reference model and that obtained by the proposed simulator. . . . . . . . . . . . . . . . . 49 1.18 a) The error of the simulation T-FCF designed by the conventional measurement-based simulator. b)The error of the simulation T-FCF designed by the proposed simulator. . . . . . . . . . . . . . . . . . . . . . . 51 2.1 The 3-D geometry model for shallow environments with randomly distributed scatterers Si,n (•) on the surface (i = 1) and the bottom (i = 2). . 57 2.2 The Spike-shape Doppler spectrum. . . . . . . . . . . . . . . . . . . . . . . 61 2.3 Effect of the two Doppler components on the overall Doppler spectrum. . . 62 2.4 Illustration of the measurement setup in Halong bay. . . . . . . . . . . . . 64 2.5 The Doppler measurement scenario 1. . . . . . . . . . . . . . . . . . . . . . 64 2.6 The Doppler measurement scenario 2. . . . . . . . . . . . . . . . . . . . . . 65 2.7 The Doppler measurement scenario 3. . . . . . . . . . . . . . . . . . . . . . 65 2.8 The steps of parameter computations. . . . . . . . . . . . . . . . . . . . . . 68 ˜ The reference model Sn (f ) compared with the proposed Doppler model 2.9 S (f ) for four observed cases in scenario 1. . . . . . . . . . . . . . . . . . . 73 ˜ 2.10 The reference model Sn (f ) compared with the proposed Doppler model S (f ) for six typical cases in scenario 2. . . . . . . . . . . . . . . . . . . . . 74 ˜ 2.11 The reference model Sn (f ) compared with the proposed Doppler model S (f ) for six cases in scenario 3. . . . . . . . . . . . . . . . . . . . . . . . . 76 2.12 The estimated trajectory movement of the Rx for scenario 3. . . . . . . . . 77 3.1 Average SIR versus signal bandwidth for different numbers of sub-carriers.. 88 3.2 Average SINR versus signal bandwidth for different numbers of sub-carriers. 89 3.3 Capacity of UWA-OFDM system versus signal bandwidth for different numbers of sub-carriers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 3.4 Average spectra efficiency versus signal bandwidth for the number of sub-carriers N = 256, and SNR = 20 dB at the receiver. . . . . . . . . . . . 92 3.5 Required transmit power PT versus signal bandwidth to achieve an SNR of 20 dB at the receiver. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.6 Average spectra efficiency versus SNR at the receiver for the number of sub-carriers N = 256, and signal bandwidth B = 10 kHz. . . . . . . . . . . 94 viii 3.7 Average SIR, SINR versus SNR at the receiver for the number of subcarriers N = 256, and signal bandwidth B = 10 kHz. . . . . . . . . . . . . . 94 3.8 Average spectra efficiency versus SNR at the receiver for the number of sub-carriers N = 256, and signal bandwidth B = 10 kHz. . . . . . . . . . . 95 3.9 Average SIR, SINR, SNR versus transmit power for the number of subcarriers N = 256, and signal bandwidth B = 10 kHz. . . . . . . . . . . . . . 96 3.10 Noise power, average ICI power, and average desired signal power versus transmit power for the number of sub-carriers N = 256, and signal bandwidth B = 10 kHz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 3.11 Cumulative distribution function of the SNR, SIR, and SINR of subcarriers for N = 256, and signal bandwidth B = 10 kHz. . . . . . . . . . . . 97 3.12 Results of Doppler spectrum measurement and modeling while the Rx moves with the consistent speed of VR = 0.5 m/s. . . . . . . . . . . . . . . 104 List of Tables 1.1 Parameters of the geometrical channel model . . . . . . . . . . . . . . . . . 29 1.2 The performance comparisons . . . . . . . . . . . . . . . . . . . . . . . . . 39 1.3 The performance comparison of the simulation approaches. . . . . . . . . . 50 2.1 Environmental conditions and system configurations of experiment . . . . . 63 2.2 The optimal and derivative parameters of the proposed model. 2.3 The optimal parameters for Doppler spectrum modeling derived from . . . . . . 70 the measurement data in scenario 1 (as plotted in Fig. 2.9.) . . . . . . . . 72 2.4 The optimal parameters for Doppler spectrum modeling derived from the measurement data in scenario 2 (as plotted in Fig. 2.10). . . . . . . . . 72 2.5 The optimal parameters for Doppler spectrum modeling derived from the measurement data in scenario 3 (as plotted in Fig. 2.11). . . . . . . . . 76 3.1 System specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 ix INTRODUCTION 1. 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 2 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. 2. 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 1 MHz [92]. When the bandwidth is between 10÷20 percent of the center of signal, the communication system is called wide-band. Although the 3 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 1 and 2. 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 4 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. 1. 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 5 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. 3. 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 6 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- 7 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. 4. Goals of the Dissertation This dissertation aims at developing accurate and efficient approaches of