The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders.

Linear chirp spread spectrum technique is widely used in underwater acoustic communication because of their resilience to high multipath and Doppler shift. Linear frequency modulated signal requires a high spreading factor to nearly reach orthogonality between two pairs of signals. On the other hand, nonlinear chirp spread spectrum signals can provide orthogonality at a low spreading factor. As a result, it improves spectral efficiency and is more insensitive to Doppler spread than the linear counterpart. To achieve a higher data rate, we propose two variants (half cycle sine and full cycle sine) of the M-ary nonlinear sine chirp spread spectrum technique based on virtual time-reversal mirror (VTRM). The proposed scheme uses different frequency bands to transmit chirp, and VTRM is used to improve the bit error rate due to high multipath. Its superior Doppler sensitivity makes it suitable for underwater acoustic communication. Furthermore, the proposed method uses a simple, low-power bank of matched filters; thus, it reduces the overall system complexity. Simulations are performed in different underwater acoustic channels to verify the robustness of the proposed scheme.

Multi‐user communication traditionally uses either code division multiple access (CDMA) or time‐division multiple access schemes. The chirp signals resilience to multipath and the Doppler effect makes it suitable for underwater acoustic communication. This paper presents a binary phase‐coded hyperbolic frequency‐modulated (BPC‐HFM) for multi‐user underwater acoustic communication. The proposed scheme has the advantage of both linear frequency‐modulated signal, which is immune to multipath and Doppler offset, while PN sequence is encoded in such a manner that a waveform possesses either a phase shift of 0 or 180 depending upon the code which results in a unique orthogonal waveform for every user. As a result, it helps to reduce multiple access interference; in addition, the time‐reversal mirror technique is used to enhance the performance of the system in the high multipath environment due to the virtue of hydro‐acoustic channel. Furthermore, it uses a relatively simple matched filter‐based receiver. To substantiate the robustness of the proposed multi‐user waveforms three types of hydro‐acoustic channels using the different numbers of multiple users are chosen for simulation and the bit error rate (BER) of the system is calculated. Furthermore, the results are compared to binary phase‐coded linear frequency‐modulated signal and conventional Direct sequence spread spectrum CDMA.

This study investigates acoustic propagation and transmission loss in shallow water at an unexplored site in the northern Arabian Sea near the Pakistan coastline using a normal mode theoretical framework. Sound propagation in shallow water with range-independent bathymetry was analyzed using a customized Kraken C program to compute eigenvalues and eigenfunctions. The sound speed profile and clay silt sediment samples of the northern Arabian Sea, which characterize the water column and ocean bottom, respectively, were determined. Coherent and incoherent transmission losses for frequencies ranging from 50 to 500 Hz were calculated across different ranges and depths. Results indicate significant intensity fluctuations with increasing range, leading to higher transmission loss. Low frequencies (50–225 Hz) exhibit more significant transmission loss, while higher frequencies (230–500 Hz) show reduced loss. Transmission loss is higher for receivers at 19 m depth compared to those at shallower depths (8 m and 12 m) because the receivers are positioned near the layer of bottom sediments. Factors such as source and receiver depth, sediment properties, bottom roughness, and sound frequency significantly influence transmission loss. The novel dataset for the region supports the assessment of sonar performance, underwater communication, navigation, and marine life exploration.

This paper presents a systematic literature review on Intelligent Process Automation (IPA), focusing on its role in modernizing business operations. It explores the integration of AI, including machine learning and robotic process automation, and their combined impact on improving efficiency and supporting human-centric tasks. The review examines the synergy between AI and human expertise, highlighting how this collaboration enhances decision-making, reduces errors, and speeds up task execution. It also investigates how digital assistants within the IPA framework redefine operational processes, supported by case studies and empirical research. The paper addresses challenges and ethical concerns related to AI integration, offering insights on risk management and ethical AI use. In conclusion, it emphasizes the paradigm shift created by AI-driven IPA, optimizing business processes and focusing on cognitive tasks, and contributing to the ongoing discussion about AI's transformative impact on human-machine collaboration.