Luận án tiến sĩ mô hình đặc tính kênh truyền cho thông tin thủy âm vùng nước nôn

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. Van Duc Nguyen 2. Van Tien Pham Hanoi - 2017 DECLARATION OF AUTHORSHIP I hereby declare that this dissertation titled, "Channel Modeling for Shal- low Underwater Acoustic Communications”, and the work presented in it are entirely my own original work under the guidance of my supervi- sors. 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 oth- ers, I have made exactly what was done by others and what I have contributed myself. Hanoi, August 27, 2017 SUPERVISORS PhD STUDENT 1. Van Duc Nguyen 2. Van Tien Pham Do Viet Ha ACKNOWLEDGEMENTS First and foremost, I would like to thank my advisor Associate Prof.

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 Communica- tion Lab, School of Electronics and Telecommunications, Hanoi Univer- sity 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 instruc- tions 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.

iv LIST OF FIGURES. vi LIST OF TABLES. DESIGN OF SHALLOW UWA CHANNEL SIMU- LATORS. Overview of Simulation Models for UWA Channels.

Rayleigh and Rice channels. Deterministic SOS Channel Models. Deterministic SOC Channel Models. The Geometry-based UWA Channel Simulator.

Developing the Reference Model from the Geometrical Channel Model. The Simulation Model. The Estimated Parameters of the Simulation Model. The Measurement-based UWA Channel Simulator.

The Reference Model from the Measurement Data. The Simulation Model. Estimated Channel Parameters of the Simulation Model 33 1. Comparison of the Two Channel Simulators.

The Proposed Approach for the Static UWA Channel. Results and Discussions. The Proposed Approach for the Case of Doppler Effects. The Measurement Data.

The Conventional Measurement-based Simulators. The Proposed Channel Simulator. MODELING OF DOPPLER POWER SPECTRUM FOR SHALLOW UWA CHANNELS. The Proposed Doppler Spectrum Model.

The Doppler Effects in Shallow UWA Channels. The Proposed Doppler Model for UWA Channels. The Description of Doppler Spectrum Measurements. Reference Model from the Measurement Data.

Parameter Optimizations of the Proposed Model. Measurement and Doppler Modeling Results. UWA-OFDM SYSTEM PERFORMANCE ANAL- YSIS USING THE MEASUREMENT-BASED UWA CHAN- NEL MODEL. ICI Analysis of UWA-OFDM Systems.

Ambient Noise Power. 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.1 The methodology behind the geometry-based channel modelling [17, 55].2 The methodology behind the measurement-based channel modelling [31, 56].3 The scheme of designing the geometry-based channel simulator [17, 55].4 The geometrical model for shallow UWA channels with randomly dis- tributed scatterers Si,n (•) on the surface (i = 1) and the bottom (i = 2) [55].5 The comparison between the normalized FCF of the reference model and that obtained by the geometry-based simulator.6 Illustration of the measurement setup in Halong bay.7 The measured |ĥ(τ, t)|2 for the transmission distance of 150 m.8 The measured and normalized PDP ρ(τ ) obtained for the transmission distance of 150 m.9 The comparison of the normalized FCF obtained by the two simulators to that of the reference model.10 The flowchart of proposed approach to design the static UWA channel simulator.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.12 The normalized Doppler power spectrum.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.14 The comparison between the normalized T-FCF of the reference model and that obtained by the conventional measurement-based simulator.15 The flowchart of the proposed approach for the case of moving Rx.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.17 The comparison between the normalized T-FCF of the reference model and that obtained by the proposed simulator.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.1 The 3-D geometry model for shallow environments with randomly dis- tributed scatterers Si,n (•) on the surface (i = 1) and the bottom (i = 2).2 The Spike-shape Doppler spectrum.3 Effect of the two Doppler components on the overall Doppler spectrum.4 Illustration of the measurement setup in Halong bay.5 The Doppler measurement scenario 1.6 The Doppler measurement scenario 2.7 The Doppler measurement scenario 3.8 The steps of parameter computations.9 The reference model S˜n (f ) compared with the proposed Doppler model S (f ) for four observed cases in scenario 1.10 The reference model S˜n (f ) compared with the proposed Doppler model S (f ) for six typical cases in scenario 2.11 The reference model S˜n (f ) compared with the proposed Doppler model S (f ) for six cases in scenario 3.12 The estimated trajectory movement of the Rx for scenario 3.1 Average SIR versus signal bandwidth for different numbers of sub-carriers.2 Average SINR versus signal bandwidth for different numbers of sub-carriers.3 Capacity of UWA-OFDM system versus signal bandwidth for different numbers of sub-carriers.4 Average spectra efficiency versus signal bandwidth for the number of sub-carriers N = 256, and SNR = 20 dB at the receiver.5 Required transmit power PT versus signal bandwidth to achieve an SNR of 20 dB at the receiver.6 Average spectra efficiency versus SNR at the receiver for the number of sub-carriers N = 256, and signal bandwidth B = 10 kHz.7 Average SIR, SINR versus SNR at the receiver for the number of sub- carriers N = 256, and signal bandwidth B = 10 kHz.8 Average spectra efficiency versus SNR at the receiver for the number of sub-carriers N = 256, and signal bandwidth B = 10 kHz.9 Average SIR, SINR, SNR versus transmit power for the number of sub- carriers N = 256, and signal bandwidth B = 10 kHz.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.11 Cumulative distribution function of the SNR, SIR, and SINR of sub- carriers for N = 256, and signal bandwidth B = 10 kHz.12 Results of Doppler spectrum measurement and modeling while the Rx moves with the consistent speed of VR = 0. 104 List of Tables 1.1 Parameters of the geometrical channel model .2 The performance comparisons .3 The performance comparison of the simulation approaches.1 Environmental conditions and system configurations of experiment .2 The optimal and derivative parameters of the proposed model.3 The optimal parameters for Doppler spectrum modeling derived from the measurement data in scenario 1 (as plotted in Fig.4 The optimal parameters for Doppler spectrum modeling derived from the measurement data in scenario 2 (as plotted in Fig.5 The optimal parameters for Doppler spectrum modeling derived from the measurement data in scenario 3 (as plotted in Fig. 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 appli- cations 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 uti- lization 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., limited availability of acoustic fre- quency 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 com- munication systems, however, call for a deep insight of the most impor- tant characteristics of real-world propagation environments. Similar to the other communication fashions, channel modeling is an initial inves- tigation because it provides hints to predict performance of communica- tion 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 en- 1 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 unfin- ished. 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 chan- nel capacity to determine the performance of the UWA-OFDM systems.