Design and Evaluation of a MATLAB-Based Underwater Acoustic Communication Simulator

Author : Waqas Javaid

Abstract

Underwater acoustic communication (UAC) is a key enabling technology for marine exploration, environmental monitoring, and autonomous underwater systems, yet it is severely constrained by complex channel impairments [1]. This paper presents a comprehensive MATLAB-based simulation framework for end-to-end modeling and performance evaluation of underwater acoustic communication systems. The proposed simulator incorporates realistic physical-layer effects, including distance-dependent path loss, Thorp absorption, multipath propagation, Doppler shift, and ambient ocean noise [2]. A BPSK modulation scheme with coherent detection is employed to analyze system robustness under varying signal-to-noise ratios. Channel impulse response, spectral distortion, and time-domain signal degradation are systematically investigated. Bit error rate performance is evaluated to quantify the combined impact of underwater channel impairments. Simulation results demonstrate significant performance degradation due to Doppler and multipath effects, highlighting the need for robust receiver designs [3]. The proposed framework provides a flexible and extensible platform for advanced research in underwater acoustic communication system design and optimization.

1. Introduction

Underwater acoustic communication (UAC) has emerged as a critical technology for marine applications, including environmental monitoring, underwater exploration, autonomous underwater vehicles, and defense systems. Unlike terrestrial wireless communication, UAC faces unique challenges due to the highly dynamic and lossy underwater environment.

The acoustic propagation is affected by factors such as absorption loss, geometric spreading, multipath propagation, and temporal variations caused by currents and turbulence [4]. Additionally, relative motion between transmitter and receiver induces Doppler shifts that distort the signal and degrade communication performance. Ambient ocean noise, arising from natural sources such as wind, waves, and marine life, as well as anthropogenic sources like shipping, further complicates reliable data transmission. Accurate modeling and simulation of these impairments are essential for designing robust communication systems. While analytical approaches provide insights into channel behavior, they often fail to capture the complex interactions among multiple physical phenomena [5].

Table 1: System Parameters

Simulation-based frameworks allow for a comprehensive evaluation of system performance under realistic conditions. In this work, we present a MATLAB-based end-to-end simulator that integrates key physical-layer effects, including Thorp absorption, multipath delay spread, Doppler shift, and ambient noise. The framework employs BPSK modulation to analyze bit error rate (BER) performance and signal distortion across a range of signal-to-noise ratios (SNRs). It also provides visualization of channel impulse response, power spectral density, and received signal constellations, enabling detailed performance diagnostics. By systematically incorporating realistic channel impairments, this simulator serves as a flexible tool for evaluating and optimizing UAC systems [6]. The results highlight the critical impact of multipath and Doppler effects on BER and demonstrate the necessity of adaptive receiver designs. Furthermore, the platform is extensible for advanced modulation schemes, equalization techniques, and future underwater communication protocols [7]. This study bridges the gap between theoretical modeling and practical system evaluation, providing valuable insights for researchers and engineers working in underwater acoustic communication.

1.1 Importance of Underwater Acoustic Communication

Underwater acoustic communication (UAC) is a vital technology for numerous marine applications, including environmental monitoring, oceanographic data collection, autonomous underwater vehicles (AUVs), and defense systems [8]. The ocean environment is inherently challenging for communication due to its high attenuation, slow sound propagation, and complex interactions with water properties. Unlike radio frequency signals in air, acoustic waves are the most practical medium for long-range underwater communication. UAC enables reliable data exchange in environments where optical or electromagnetic waves fail. Effective underwater communication can support real-time monitoring, navigation, and coordination of submerged devices. With the growth of marine exploration and underwater robotics, the demand for robust acoustic communication has increased significantly [9]. Researchers are now focusing on developing adaptive and resilient communication systems. Accurate modeling of the physical channel is essential to understand system performance limits. Simulation frameworks allow designers to predict challenges before practical deployment. The work presented in this paper addresses these requirements by proposing a detailed MATLAB-based simulation environment.

1.2 Challenges in Underwater Acoustic Channels

The underwater channel is significantly different from terrestrial wireless channels due to several impairments that degrade signal quality. Acoustic signals experience, geometric spreading, and frequency-dependent, absorption, which reduce signal strength over distance.

Table 2: Underwater Channel Model Components

Multipath propagation caused by reflections from the sea surface, seabed, and obstacles introduces delay spread, causing inter-symbol interference [10]. Relative motion between transmitter and receiver generates Doppler shifts, leading to frequency offsets that distort the transmitted signal. Temporal variations in water temperature, salinity, and currents further affect signal propagation. Ambient noise from natural sources such as waves, rain, and marine life, as well as human activities like shipping, introduces additional disturbances. These combined effects result in significant challenges for reliable communication, including higher bit error rates and reduced data rates. Understanding the impact of each channel impairment is crucial for designing effective modulation and receiver schemes [11]. Simulation-based approaches are essential for evaluating these effects under controlled conditions. The MATLAB framework in this study incorporates these realistic underwater channel impairments.

1.3 Motivation for Simulation-Based Study

Analytical modeling of UAC channels provides theoretical insights but often fails to capture the full complexity of underwater environments. Physical experiments in real underwater scenarios are expensive, time-consuming, and logistically challenging. Simulation allows researchers to model multiple impairments simultaneously and systematically evaluate system performance. It provides flexibility to test different distances, depths, modulation schemes, and signal-to-noise ratios (SNRs). MATLAB, with its rich signal processing and visualization capabilities, is an ideal platform for implementing such simulations. A well-designed simulator can visualize channel impulse responses, power spectral densities, and received signal constellations, aiding in system understanding [12]. Simulation also enables BER analysis across varying SNR levels to quantify communication reliability. The proposed simulator bridges the gap between theoretical modeling and experimental validation. It serves as a tool for both academic research and practical system design. This motivates the creation of a comprehensive, modular, and extensible simulation framework for underwater acoustic communication.

1.4 Significance and Research Contribution

The proposed simulator contributes to the field by providing a flexible, high-fidelity tool for analyzing underwater acoustic communication systems. It allows researchers to systematically study the impact of physical-layer impairments on signal quality and BER performance. The simulation platform can support optimization of modulation schemes, receiver designs, and adaptive equalization techniques [13]. It also facilitates visualization of key channel characteristics, providing insights into multipath effects and Doppler-induced distortions. By bridging the gap between theoretical analysis and experimental implementation, the framework accelerates research in UAC system design. The simulator’s extensibility makes it suitable for testing advanced communication protocols such as OFDM, MIMO, or machine-learning-based receivers. Its modular structure supports scalability for larger network simulations. The study underscores the importance of comprehensive modeling in predicting real-world performance. Ultimately, the simulator enables efficient, cost-effective experimentation [14]. This work contributes to advancing robust and reliable underwater communication solutions.

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1.5 Significance of the Study

The proposed study is significant because it provides a comprehensive, realistic, and flexible platform for evaluating underwater acoustic communication performance. By integrating multiple channel impairments simultaneously, the simulator offers insights that are difficult to obtain through purely analytical models or limited field experiments. It allows researchers and engineers to predict system performance under realistic environmental conditions and optimize modulation, detection, and receiver design accordingly [15]. The visualization tools aid in understanding the impact of multipath propagation, Doppler shift, and ambient noise on signal integrity. The framework also supports educational objectives, enabling students to experiment with UAC systems in a controlled, cost-effective environment. Additionally, the simulator lays the foundation for future work on advanced protocols, adaptive modulation schemes, and multi-user underwater networks. By bridging the gap between theory, simulation, and practical deployment, this study contributes to the development of reliable and high-performance underwater communication systems. It provides a reference benchmark for future research in this rapidly evolving field.

2. Problem Statement

Underwater acoustic communication (UAC) faces severe challenges due to the complex and dynamic nature of the underwater environment. Acoustic signals experience high path loss, frequency-dependent absorption, and significant multipath propagation, which introduce delay spreads and inter-symbol interference. Relative motion between transmitter and receiver causes Doppler shifts, further distorting the transmitted signal. Ambient ocean noise from natural and human-made sources reduces signal-to-noise ratio and degrades communication reliability. Existing analytical models often oversimplify these impairments, while experimental studies are expensive, time-consuming, and environment-specific. Many simulation frameworks fail to integrate multiple channel effects simultaneously, limiting their applicability for real-world performance evaluation. Consequently, designing robust modulation schemes and receivers for practical underwater channels remains challenging. There is a critical need for a flexible, end-to-end simulation platform that can model all significant physical-layer impairments. Such a tool would enable systematic evaluation of system performance, including bit error rate and spectral distortion. This study addresses these gaps by proposing a comprehensive MATLAB-based UAC simulator.

3. Mathematical Approach

The underwater acoustic channel is modeled using a combination of distance-dependent path loss (PL(d)) and Thorp absorption  (alpha(f)), expressed as:

Where (k) is the spreading factor multipath propagation is represented by a discrete-time channel impulse response with path gains and delays.

The Doppler effect is incorporated as a frequency shift in the received signal where (f_D) is the Doppler frequency.

Ambient noise is modeled as additive Gaussian noise (n(t)), combining thermal and shipping contributions. Finally, the received signal is given by:

Which is demodulated using BPSK detection to evaluate bit error rate (BER). The underwater acoustic channel is modeled by combining distance-dependent path loss and frequency-dependent absorption, which together determine how the signal attenuates over distance and frequency. Multipath propagation is represented by multiple delayed copies of the transmitted signal, each with a specific gain, capturing reflections from the surface, seabed, and obstacles. The Doppler effect due to relative motion between the transmitter and receiver introduces a shift in the signal frequency, affecting both phase and amplitude. Ambient noise from natural and human-made sources is modeled as additive noise that superimposes on the received signal, reducing overall signal quality. The received signal is therefore a combination of the transmitted signal, multipath components, Doppler-induced frequency shifts, and ambient noise. The signal is processed at the receiver using matched filtering and coherent detection to recover the transmitted bits. By systematically including these impairments, the model provides a realistic representation of the underwater channel. Bit error rate is evaluated by comparing the transmitted and received data sequences. Time and frequency domain analyses are performed to visualize the effect of multipath and Doppler. This approach allows for comprehensive performance evaluation of underwater acoustic communication systems under realistic conditions.

4. Methodology

The methodology of this study is based on developing a comprehensive MATLAB-based simulator to evaluate the performance of underwater acoustic communication systems under realistic physical-layer impairments. The simulation begins with the generation of a random binary data sequence, which is then modulated using binary phase-shift keying to create the transmitted signal [16]. The signal is upsampled and pulse-shaped to emulate practical transmission conditions, including symbol duration and bandwidth. The underwater channel is modeled by incorporating distance-dependent path loss and Thorp absorption to account for frequency-specific attenuation over the propagation path. Multipath propagation is simulated by introducing multiple delayed and scaled copies of the transmitted signal, representing reflections from the sea surface, seabed, and obstacles [17]. Doppler shifts resulting from relative motion between the transmitter and receiver are applied to the signal, altering its frequency and phase characteristics. Ambient ocean noise, including thermal and shipping components, is generated and added to the received signal to emulate real-world conditions. The received signal is processed using coherent demodulation and matched filtering to extract the transmitted data. Sampling at symbol intervals allows for bit detection, and the recovered bits are compared with the original sequence to calculate bit error rate across different signal-to-noise ratios. Time-domain and frequency-domain analyses are performed to visualize signal distortion, power spectral density, and channel impulse response. Multiple simulation runs are conducted to ensure statistical reliability and reproducibility of results. The methodology also incorporates visualization of received signal constellations, allowing qualitative assessment of signal integrity. Parameter flexibility, including carrier frequency, bit rate, channel distance, and relative velocity, enables systematic performance evaluation under various conditions [18]. The modular design of the simulator allows future extension to advanced modulation schemes, equalization, and multi-user scenarios. Simulation outputs include detailed plots and performance metrics for comprehensive analysis [19]. Overall, the methodology combines realistic channel modeling, digital signal processing, and systematic performance evaluation to provide a complete framework for underwater acoustic communication studies.

5. Design Matlab Simulation and Analysis

The simulation begins with generating a random binary data sequence, which represents the information to be transmitted over the underwater acoustic channel. This data is modulated using binary phase-shift keying to create a baseband signal suitable for transmission [20]. The signal is upsampled and shaped using a rectangular pulse to approximate realistic symbol duration and bandwidth. The underwater channel is then modeled by including distance-dependent path loss and frequency-specific absorption, which reduce signal strength according to the propagation distance and carrier frequency. Multipath propagation is simulated by creating multiple delayed and scaled versions of the transmitted signal, representing reflections from the sea surface, seabed, and other obstacles [21]. Doppler shifts caused by relative motion between the transmitter and receiver are applied, resulting in frequency and phase distortion of the received signal. Ambient ocean noise, including thermal noise and shipping activity, is generated and added to the received signal to replicate real-world environmental conditions. The receiver performs coherent demodulation, multiplying the signal by a local carrier to bring it back to baseband. A matched filter is applied to maximize signal-to-noise ratio and reduce inter-symbol interference. The output of the matched filter is sampled at symbol intervals to detect transmitted bits. The received bits are compared with the original transmitted sequence to calculate bit error rate across different signal-to-noise ratios. Time-domain and frequency-domain analyses are performed to visualize the effects of multipath, Doppler, and noise on the signal. The simulator generates plots for channel impulse response, transmitted and received signals, power spectral density, and received constellation diagrams. Path loss over distance is also visualized to show the impact of absorption and spreading. Multiple simulation runs ensure statistical accuracy and reproducibility of results. Parameter flexibility, including carrier frequency, bit rate, distance, and relative velocity, allows systematic evaluation of system performance. The simulator’s modular design makes it easy to extend to other modulation schemes or adaptive equalization techniques. Overall, the simulation provides a realistic and comprehensive framework for studying underwater acoustic communication performance under challenging physical-layer conditions. It enables both qualitative and quantitative analysis, offering insights into signal distortion, error performance, and channel behavior. The results highlight critical impairments such as multipath and Doppler effects, guiding the design of robust communication systems for practical underwater environments.

Figure 2 illustrates the channel impulse response of the simulated underwater acoustic channel. Each stem represents a multipath component corresponding to reflections from the sea surface, seabed, or obstacles. The height of each stem indicates the relative gain of that path, while the horizontal axis represents the time delay. The first peak corresponds to the direct path between transmitter and receiver, while subsequent peaks indicate delayed echoes. The multipath effect introduces inter-symbol interference, which can degrade communication reliability if not properly mitigated. This visualization helps in understanding how energy spreads over time in the channel. Longer delays and stronger reflected paths indicate a more severe channel environment. The figure provides insight for designing matched filters and equalizers at the receiver. By quantifying delay and amplitude of each path, researchers can evaluate system performance under realistic multipath conditions. Overall, this plot highlights the significance of channel modeling for underwater acoustic communication systems.

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Figure 3 shows the power spectral density of the received signal after Doppler shift due to relative motion. The Doppler effect shifts the carrier frequency and spreads the spectrum, causing distortion in both amplitude and phase. The plot demonstrates how the received signal’s frequency content differs from the transmitted signal. Broadening of the spectrum indicates increased inter-symbol interference potential. The figure also reflects the impact of relative velocity on communication reliability. It helps researchers identify the frequency shifts that need compensation in the receiver. The spectrum can guide adaptive equalization or Doppler correction techniques. Peaks in the spectrum correspond to the carrier frequency and its multipath components. This visualization is critical for understanding how Doppler distortion interacts with multipath propagation. By analyzing this plot, one can estimate the required receiver bandwidth and filter design. Overall, Figure 3 emphasizes the importance of considering Doppler effects in practical underwater acoustic systems.

Figure 4 depicts the variation of path loss with increasing distance between transmitter and receiver. The plot combines the effects of geometric spreading and Thorp absorption loss. Path loss increases non-linearly as distance increases, indicating higher signal attenuation at longer ranges. This visualization helps in understanding how transmission power requirements scale with distance. The curve demonstrates that higher carrier frequencies experience more absorption, resulting in steeper losses. It provides a guideline for selecting suitable frequencies and transmission power for reliable communication. The plot also shows that even moderate distances can lead to significant signal degradation. By analyzing this curve, system designers can optimize link budgets and receiver sensitivity. It reinforces the importance of realistic channel modeling for performance prediction. Overall, this figure quantifies one of the most critical impairments in underwater acoustic communication.

Figure 5 compares the transmitted and received signals in the time domain. The top subplot shows the clean transmitted BPSK signal, while the bottom subplot shows the received signal after channel impairments and noise. Differences in amplitude and shape between the two signals illustrate the combined effects of multipath, Doppler shift, and ambient noise. Delayed peaks in the received signal reflect multipath components. Random fluctuations represent noise contributions, reducing signal clarity. This visualization allows direct assessment of how the channel distorts the transmitted waveform. It is useful for designing matched filters, equalizers, and synchronization algorithms. Observing the waveform also helps in detecting inter-symbol interference visually. The figure highlights the importance of signal processing techniques to recover transmitted data accurately. It serves as an intuitive demonstration of the physical-layer challenges in underwater communication systems.

Figure 6 shows the power spectral density of the transmitted and received signals. The transmitted signal exhibits a narrowband spectrum centered at the carrier frequency. The received signal shows spectral broadening due to multipath propagation and Doppler effects. Noise contributions also elevate the spectral floor, reducing signal-to-noise ratio. Comparing both spectra helps quantify signal distortion and bandwidth spreading. This analysis guides filter design and frequency-domain equalization strategies. Peaks in the received spectrum correspond to multipath components and Doppler-shifted signals. The figure provides insight into how physical-layer impairments affect frequency content. It is essential for evaluating spectral efficiency and detecting interference. Overall, this plot illustrates how channel characteristics impact the signal in the frequency domain and aids performance analysis.

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Figure 7 presents the bit error rate performance of the BPSK system under varying SNR conditions. The plot demonstrates the expected exponential decay of BER with increasing SNR. At low SNR, noise and channel impairments result in higher error rates. As SNR increases, the receiver is able to recover transmitted bits more reliably. The curve quantifies the impact of multipath, Doppler, and ambient noise on communication reliability. This plot is a key performance metric for evaluating modulation schemes and receiver designs. It helps determine the required SNR for a desired error performance. The figure also serves as a benchmark for comparing alternative modulation or coding schemes. It provides insight into system limitations under realistic channel conditions. Overall, it highlights the trade-off between transmission power, noise, and reliability in underwater communication.

Figure 8 shows the received signal constellation after coherent demodulation. The ideal BPSK constellation consists of two points along the in-phase axis. In the plot, scattering of points around the ideal positions illustrates the combined effects of multipath, Doppler shifts, and ambient noise. The dispersion indicates the probability of symbol errors and helps visually interpret the BER performance. The figure is useful for understanding phase and amplitude distortions in the received signal. Dense clustering near the ideal points corresponds to high SNR, while wider spread indicates degradation. It provides a qualitative tool for evaluating receiver performance and modulation robustness. This visualization also aids in designing phase correction and equalization algorithms. Overall, the constellation diagram serves as an intuitive representation of signal integrity under realistic underwater channel conditions.

6. Results and Discussion

The simulation results provide a comprehensive evaluation of the underwater acoustic communication system under realistic channel conditions. Figure 2 shows the channel impulse response, highlighting multipath propagation with multiple delayed paths of varying amplitudes, which cause inter-symbol interference and signal distortion. Figure 3 demonstrates the Doppler effect, showing frequency shifts and spectral broadening due to relative motion between the transmitter and receiver, emphasizing the importance of Doppler compensation in the receiver design [22]. Figure 4 illustrates path loss as a function of distance, confirming that attenuation increases nonlinearly with distance due to spreading and frequency-dependent absorption, which significantly affects link budget and power requirements. Time-domain analysis in Figure 5 compares transmitted and received signals, showing waveform distortion and noise effects, which reinforce the need for matched filtering and signal processing [23]. Figure 6 presents the power spectral density, where the received signal spectrum exhibits broadening and noise elevation compared to the transmitted signal, indicating energy spreading due to multipath and Doppler. The BER performance shown in Figure 7 reveals the trade-off between signal-to-noise ratio and communication reliability, with higher SNR reducing bit errors and demonstrating the limits imposed by channel impairments. The constellation diagram in Figure 8 highlights phase and amplitude distortions, visually illustrating the impact of multipath and noise on signal integrity. Overall, the results confirm that multipath and Doppler effects, combined with ambient noise, significantly degrade underwater communication performance [24]. The simulator provides insights into system design, showing the need for adaptive equalization, Doppler correction, and robust modulation schemes. By systematically analyzing these impairments, researchers can optimize transmitter power, carrier frequency, and receiver algorithms. Time-domain, frequency-domain, and constellation visualizations complement BER analysis to give a complete picture of performance. The study demonstrates that realistic modeling of the physical-layer environment is essential for reliable communication. The framework also allows testing of different distances, depths, velocities, and SNR conditions, highlighting sensitivity to environmental parameters. These findings guide the development of advanced modulation techniques such as OFDM or coded BPSK for enhanced reliability [25]. The simulation results validate the MATLAB framework as a flexible and extensible platform. The visualizations help identify dominant impairments and their interactions, aiding receiver and filter design. Quantitative results provide benchmarks for future underwater acoustic studies. Finally, the integrated analysis underscores the critical importance of considering all physical-layer effects simultaneously to accurately predict system performance in practical underwater environments.

7. Conclusion

This study presents a comprehensive MATLAB-based simulator for underwater acoustic communication, incorporating key physical-layer impairments including path loss, Thorp absorption, multipath propagation, Doppler effect, and ambient noise. The simulation framework enables evaluation of BPSK modulation performance under realistic channel conditions. Time-domain and frequency-domain analyses, along with constellation visualization, provide insights into signal distortion and spectral spreading. Bit error rate results demonstrate the critical impact of multipath, Doppler shifts, and noise on communication reliability [26]. The simulator allows systematic investigation of parameters such as distance, depth, velocity, and SNR, providing a flexible platform for performance optimization. Results highlight the need for matched filtering, Doppler compensation, and robust receiver designs. The modular design facilitates extension to advanced modulation schemes and adaptive equalization techniques. This work bridges the gap between theoretical modeling and practical performance evaluation [27]. Overall, the framework serves as a valuable tool for researchers and engineers developing reliable underwater communication systems. It lays the foundation for future studies in high-performance, long-range underwater acoustic networks.

8. References

[1] Freitag L., Stojanovic M., Singh S., Johnson M., Analysis of channel effects on direct‑sequence and frequency‑hopped spread‑spectrum acoustic communication, IEEE Journal of Oceanic Engineering, 26:586–593.

[2] Schmidt J.H., Kochańska I., Schmidt A.M., Performance of direct sequence spread spectrum UAC with differential detection, Archives of Acoustics.

[3] “Underwater acoustic communication,” Encyclopedia / Wikipedia overview (channel characteristics & modulation challenges).

[4] Naman H.A., Abdelkareem A.E., Multipath geometry channel model in shallow water acoustic communication, Journal of Marine Science and Application, 2023.

[5] Liu H., Ma L., Wang Z., Qiao G., Channel prediction for underwater acoustic communication: review and performance evaluation, Remote Sensing, 2024.

[6] “Underwater acoustics,” Encyclopedia / Wikipedia overview (sound propagation physics).

[7] Pranitha B., Anjaneyulu L., Review of research trends in underwater communications – a technical survey, ICCSP Proceedings.

[8] Abd S.A., Alwan I.M., Performance evaluation of OFDM in underwater acoustic communication, IJERT, 2013.

[9] Petroni A., Scarano G., Cusani R., Biagi M., On the effect of channel knowledge in underwater acoustic communications, Electronics, 2023.

[10] Xu Y. et al., Underwater acoustic modem challenges and survey, Juniper Publishers review (modems & channel effects).

[11] Stojanovic M., Recent Advances in High‑Speed Underwater Acoustic Communications, IEEE J. Oceanic Eng.

[12] Akyildiz I.F., Pompili D., Melodia T., Underwater acoustic sensor networks: research challenges, Ad Hoc Networks.

[13] Kilfoyle D.B., Baggeroer A.B., The state of the art in underwater acoustic telemetry, IEEE J. Oceanic Eng.

[14] Preisig J.C., Acoustic propagation considerations for underwater acoustic communications network development, ACM SALSA Workshop.

[15] Chitre M., A high‑frequency warm shallow water acoustic communications channel model and measurements, Acoustical Society of America.

[16] Yang T.C., Shallow water acoustic communications, IEEE Oceans Conference.

[17] Li B., Zhou S., Low‑complexity sparse channel equalizer for underwater acoustic communications, IEEE Trans. Signal Process.

[18] Tan H., Diamant R., A survey of techniques and challenges in underwater acoustic communications, IEEE Communications Surveys & Tutorials.

[19] Wang H., Adaptive modulation for underwater acoustic OFDM systems, Ocean Engineering Journal.

[20] Liu J., Time‑varying multipath propagation and Doppler effects in UAC, Journal of Marine Systems.

[21] Singh S., Multipath mitigation techniques for underwater acoustic communications, IEEE Trans. Vehicular Technology.

[22] Yang Z., Channel estimation and equalization techniques in underwater acoustic OFDM, IEEE Access Journal.

[23] Zhao X., Performance analysis of BPSK and QPSK in underwater acoustic multipath channels, International Journal on Smart Sensing and Intelligent Systems.

[24] Sun J., Doppler shift compensation algorithms for UAC, IEEE OCEANS Conference.

[25] Zhou Z., Underwater acoustic communications using spread spectrum techniques, IEEE Milcom Proceedings.

[26] Kilfoyle D.B., Baggeroer A.B., The impact of Doppler on UAC modems, Journal of Acoustical Society of America.

[27] Song W., Pados D.A., M‑ary orthogonal chirp modulation for UWA communications, arXiv preprint.

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