(IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 9, September 2012 Underwater Acoustic Channel Modeling: A Simulation Study Pallavi Kamal Taussif Khanna Electrical Engineering Institute of Information Technology University Teknologi Mara Kohat University of Science and Technology 40450 Shah Alam, Selangor Darul Pakistan Ehsan, Malaysia Abstract—Underwater acoustic (UWA) communications have or software simulation [1-5]. However, these efforts may been regarded as one of the most challenging wireless not be suitable for those contexts in which a high level communications due to the unique and complicated properties of model is required. In other words, how to accurately model underwater acoustic environments, such as severe multipath UWA channels for complicated UWA environments is still delay, large Doppler shift and fast environmental changes. Therefore, accurate UWA channel modeling is crucial for a challenging issue that has not been well resolved. achieving high performance UWA communication systems. Since Bellhop algorithm [6-7] is a beam tracing model for there is no generalized UWA channel mode, most of existing predicting acoustic pressure fields in ocean environments. channel models is either empirical measurement- or simulation- based channel models. In this paper, we propose a study of The beam tracing structure leads to a particularly simple simulation-based UWA channel modeling, which is based on the algorithm. Bellhop can produce a variety of useful outputs Bellhop algorithm and Time Variable Acoustic Propagation including transmission loss, eigenrays, arrivals, and Model (TVAPM) platform. Our study is capable of handling received time-series. It allows for range-dependence in the almost any type of steady and unsteady environmental motion top and bottom boundaries (altimetry and bathymetry), as except the modeling of breaking waves. well as in the sound speed profile. [8-9] adopted the Keywords-underwater acoustic channel modeling; Bellhop Bellhop algorithm to calculated transmission loss item of algorithm; VIRTEX platform ; environmental motion signal-to-noise ratio (SNR).  used Bellhop Gaussian beam tracing program for the acoustic field module.  I. INTRODUCTION adopted the Bellhop algorithm to simulate channel impulse response in the presence of effect of wind-generated Underwater Acoustic (UWA) communications have bubbles. been widely used in military and civilian applications for a long time, such as monitoring of underwater environments, The Time Variable Acoustic Propagation Model and unmanned underwater vehicle (UUV) communications. (TVAPM) platform  aims at generating time variable However, how to achieve high performance and reliable simulated acoustic channel responses between moving UWA communications is a challenging issue due to the sources and receivers in a realistically modeled complicated underwater environments (e.g., shallow water). environment. The main objective of this simulator is to One of the driving factors in the performance of certain properly take into account the Doppler effects induced by UWA communication systems is the Doppler spread, which source-receiver relative motion as well as the effects of that is often generated by sea-surface movement. The time- motion that propagate through the acoustic channel. varying nature of the sea surface adds complexity and often In this paper, based on the Bellhop algorithm and the leads to a statistical description for the variations in the TVAPM platform, we propose a study of UWA channel received signals. Severe multipath propagation and large Doppler shift due to source/receiver motion are another two modeling in complicated underwater environments. The factors that determine the performance of UWA content includes but not limited to the following aspects: (1) communications. The available bandwidth of the UWA source/array initial localization; (2) Doppler shift (in channel is limited and it highly depends on both frequency) estimation; (3) multi-path time delay estimation; transmission range and frequency. These introduced (4) TVAPM platform-based channel modeling with sea- characteristics restrict the reliable and high performance surface motion (e.g., wind speed). Our paper will provide a communications. useful reference for scientists and engineers who want to simulate any complicated UWA channels for Many works have already treated the problem of UWA communications. channel modeling, either based on empirical measurement 25 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 9, September 2012 The rest of this paper is organized as follows: Section II is the introduction of the Bellhop algorithm and the TVAPM platform. Section III is the study of Bellhop algorithm-based investigation of acoustic signal propagation and UWA channel properties; Section IV concludes this paper. II. BELLHOP ALGORITHM AND TVAPM A. Bellhop Algorithm The overall structure of BELLHOP algorithm is shown in Figure 1. In order to describe the environment and the geometry of sources and receivers, various files must be provided. In the most simple and general case, which is also typical, there is only one such file. It is referred to as an environmental file (.env) and includes the sound speed profile, as well as information about the ocean Figure 1. Structure of Bellhop algorithm  bottom. However, if there is a range-dependent bottom, B. TVAPM platform then a bathymetry file (.bty) with range-depth pairs defining the water depth must be added. Similarly, if The TV-APM is a valuable tool to discuss arrival there is a range dependent ocean sound speed, an SSP scattering due to the propagation of wind-driven sea file with the sound speed tabulated on a regular grid surface waves or relative motion between a source should be shown up. Further, if anyone who wants to and an array. Modeling in the future can be oriented specify an arbitrary bottom reflection coefficient to along the following guidelines: characterize the bottom, then one must provide a bottom Accounting for range dependent bottom reflection coefficient file with angle-reflection properties, shear included. coefficient pairs defining the reflectivity. Similar capabilities are implemented for the surface. Thus there Accounting for sound speed fields and sound is the option of providing a top reflection coefficient and speed temporal variability, in particular using a top shape (.aty file) . empirical orthogonal functions. Usually one assumes the acoustic source is omni- Considering the statistical distribution of travel directional; however, if there is a source beam pattern, times and amplitudes. then one must provide a source beam pattern file with Considering the inclusion of additional acoustic angle-amplitude pairs defining it. BELLHOP reads these models. files depending on options selected within the main environmental file. Plot programs (plotssp, plotbty, III. SIMULATION STUDY plotbrc, etc.) are provided to display each of the input Figures 2-5 provide results of a case study of underwater files.  acoustic channel properties. Parameter settings for the selected case can be found in Appendix. 26 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 9, September 2012 t-tau representation t-f representation 0 0 0.5 0.5 time (s) time (s) 1 1 1.5 1.5 2 2 0.68 0.69 0.7 0.71 0.72 -2000 -1000 0 1000 2000 delay (s) frequency (Hz) d-tau representation d-f representation 5 5 Doppler (Hz) doppler (Hz) 0 0 -5 -5 0.68 0.69 0.7 0.71 0.72 -2000 -1000 0 1000 2000 delay (s) frequency (Hz) Figure 2. Result of Static Source Figure 5. Results of simulated channel properties. IV. CONCLUSION Bellhop algorithm as well as its associated TVAPM platform has been validated to be a powerful to model underwater acoustic channels, especially convenient for instigating the properties of underwater acoustic channel under time-varying conditions. In addition, this channel modeling platform can be combined with other underwater acoustic communication and network simulation tool (e.g., ns2) to evaluate the performance of a whole system. Appendix: Parameter settings of the case study source_x = [1900 900 5]; source_v = [0 0 0]; source_nrays = 2001; source_aperture = 60; Figure 3. Result of Static Array source_ray_step = 10; fc = 15000; % frequency (Hz) fs_x = 5000; % input baseband signal sampling frequency (Hz) bottom_properties = [1465 1.5 0.06]; %bottom_properties(1) = compressional speed (m/s) %bottom_properties(2) = bottom density (g/cm3) %bottom_properties(3) = bottom attenuation (dB/wavelength) array_x = [2800 400 0]; array_v = [0 0 0]; first_hyd = 30; last_hyd = 60; delta_hyd = 10; % Wind induced sea surface wave U = 10; % wind speed theta = 20; % direction of propagation in degrees spreading = 'none'; REFERENCES Figure 4. Result of Source/Array initial positioning  A. F. Harris and M. Zorzi, “Modeling the underwater acoustic channel in ns2”, ACM proceedings of the 2nd international confernece on performance evaluation methodologies and tool, pp. 1-8, 2007 27 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 9, September 2012  S. H. Byun, S. M. Kim, Y. K. 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"Underwater Acoustic Channel Modeling: A Simulation Study"