Reinforcement Learning-Assisted Secure Reliable Underwater Wireless Acoustic Communications

Modeling position uncertainty of networked autonomous underwater vehicles

The deployment of Autonomous Underwater Vehicles (AUVs) for ocean observation is one of the important methods in marine scientific research. The stability of AUV attitude control determines the efficiency and safety of underwater operations. In practical systems, the actuators carried by AUVs have saturation, and the saturation of the actuators greatly affects the performance and stability of AUV attitude control. This article designs an anti-windup [Formula: see text] robust state feedback controller and an anti-windup [Formula: see text] robust dynamic output feedback controller for the pitch attitude control of AUVs to solve the pitch attitude control problem of AUVs subject to input constraints. Firstly, building a Linear-parameter varying (LPV) model for the AUV. Then, we design two anti-windup [Formula: see text] robust controllers based on LPV model. Simulation results verify the reasonable of LPV model and the effectiveness of proposed controllers. Proposed controllers can enable the system to quickly reach the desired attitude under input constraints.

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Increasing global energy demand has driven the exploitation of oil reservoirs in deep oceans. The high risk of oil spill due to unexpected failures of undersea facilities urges the deployment of autonomous underwater vehicles (AUV) for frequent and thorough inspections in these extreme environments that humans cannot easily access. To meet the long endurance and occasionally high power requirements of the AUV, we propose a lithium-bromine/oxygen fuel cell with a protected lithium metal anode to provide high specific energy at either low-power mode with seawater (oxygen) or high-power mode with bromine catholytes. The proof-of-concept fuel cell with a flat catalyst-free graphite electrode can discharge at 3mW/cm2 with seawater, and 9mW/cm2 with dilute bromine catholytes. The fuel cell can also be recharged with LiBr catholytes efficiently to recover the lithium metal anode. Scanning electron microscopy images reveal that both the organic electrolye and the bromine electrolyte corrode the solid electrolyte plate quickly, leading to nanoporous pathways that can percolate through the plate, thus limiting the cell performance and lifetime. With improved solid electrolytes or membraneless flow designs, the dual-mode lithium-bromine/oxygen system could enable not only AUV but also land-based electric vehicles, by providing a critical high-power mode to high-energy-density (but otherwise low-power) lithium-air batteries.

Multiple field-based methods to assess the potential impacts of seismic surveys on scallops

The integration of intelligent reflecting surfaces (IRS) into free-space optical (FSO) communication systems is considered as a promising and innovative approach to overcoming existing challenges due to the strict line-of-sight (LoS) requirements faced by conventional FSO setups, thereby ensuring more flexible and controllable links and expanding the communication coverage area. Nonetheless, the configuration of IRS-assisted FSO systems remains relatively novel, requiring investigation into optimizing IRS placement to enhance system performance while mitigating interference and obstacles in dynamic environmental conditions. This work aims to evaluate the improvement of the system performance achieved through optimized IRS positioning while accounting for the main degrading factors inherent in FSO systems and IRS design. We present a comprehensive model for a multilink terrestrial IRS-assisted FSO system, taking into consideration key degrading factors such as pointing errors stemming from both transmitter and IRS jitter, IRS dimensions, and turbulence-induced fading. Furthermore, we derive closed-form expressions for the probability density function of channel coefficients, average bit error rate, and outage probability. Our results indicate that optimal IRS placement is in proximity to the transmitter, taking into account all channel impairments. Furthermore, we showed that optimal IRS placement tends to shift towards the receiver side as the IRS jitter increases. Consequently, the analysis presented offers valuable insights for efficiently designing IRS-assisted FSO systems, particularly regarding IRS placement in the presence of pointing errors.

Wireless communication environment becomes more and more complex especially when facing vicious eavesdroppers and external jammers. Intelligent reflecting surface (IRS) can provide secure or anti-jamming communication approaches via passively reflecting with low power consumption and high cost efficiency. Unmanned aerial vehicle (UAV) also can be used to cooperatively transmit useful signals of legitimate users with vicious attackers. Carrying IRS on UAV can add new cooperative strategies, such as trajectory or height of UAV, numbers of reflecting elements or phase shift of IRS, to improve secure and anti-jamming performance of legitimate users. Two cooperative transmission schemes assisted with UAV and IRS are proposed to confront passive eavesdroppers or smart jammers in this paper. The first kind of cooperation is to select UAV relay or IRS reflecting strategy to improve energy efficiency of system with passive eavesdroppers. The second kind of cooperation is to jointly optimize phase shifts of IRS and trajectory of UAV to enhance transmission capacity with smart jammers. Simulation results show that, with the help of the cooperation of UAV and IRS, secure and reliable communications can be realized and system performance can be improved compared with no cooperation scheme or only one kind of cooperation with UAV or IRS scheme.

Underwater control applications, such as control of Autonomous Underwater Vehicles (AUV)s, have recently gained significant interest, and there is a growing demand for high-speed wireless communication between AUVs and base station. Acoustic communication provides low data rates and high propagation delays, both of which are not suitable for employing high control gains for the navigation of AUVs. On the other hand, Radio Frequency (RF) communication provides high data rate, but it is constrained by high attenuation due to high conductivity and permittivity of water resulting in a short working range. In this work, we propose an underwater networked control system with acoustic and RF hybrid communication, where an acoustic link is employed for long range communication and RF link is applied in close range. Our performance results indicate that the acoustic and RF hybrid communication system takes up to 38.5% less time to dock, up to 91% smaller steady state error and spends up to 39% lower communication energy as compared to the acoustic only system at the expense of up to 22.35% higher motive energy.

Deployment of autonomous underwater vehicle have became a focal point for many industrial application. The main challenge to the autonomous underwater vehicle is the finite mission time during the operation in a harsh environment. The most efficient way to reduce the operational and maintenance costs, and increase the efficiency of the system, the autonomous under water vehicles have to be recharged under water during the operation. This paper proposes an analysis of one side com-pensation topology and indexes some parameters to be considered when designing an inductive contactless power transfer system for AUV charging.

Intelligent reflecting surface (IRS) can control the reflection of electromagnetic wave, so the deployment of IRS can adjust the channel conditions to enhance the coverage and throughput in the 5G evolution networks. Since the IRS is only capable of passively reflecting the received signals by adjusting the phase-shift, its path-loss modelling method is different from existing two-hop communication networks, such as repeater, or relay. In this paper, the path-loss model for IRS in real urban scenario is proposed. Based on the theoretical analysis, the novel measurement methodology is proposed that the path-loss channel measurement campaign can be divided into separate measurement and cascade measurement, so that the measurement efficiency is dramatically improved under the constraint of modelling accuracy. The measurement data at 3.7 GHz verifies the accuracy of the proposed IRS path-loss model with the root mean squared error (RMSE) as 2.33 dB. The work investigated in this paper could be the foundation for IRS-based network link budget calculation and system level evaluation.

This article investigates physical-layer security (PLS) of a typical wireless-powered communication (WPC) system with the aid of intelligent reflecting surface (IRS) in the presence of a passive eavesdropper for Internet of Things (IoT), and proposes three IRS-aided secure WPC modes. Specifically, in mode-I, the IRS is deployed between hybrid access point (HAP) and wireless user (U) for co-located power station (PS) and access point (AP). In mode-II, the IRS is deployed between AP and U for separate PS and AP, while in mode-III, the IRS is deployed between PS and U for separate PS and AP. For each mode, the optimal phase shift is designed to maximize the reception of energy and information at the legitimate receiver. We comprehensively analyze the performance of each mode, and derive the closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) for each mode, respectively. The theoretical analysis and simulation results reveal that from the perspective of reliability, mode-I can achieve the best COP with the increased number of IRS elements, while mode-II and mode-III have a similar COP. From the perspective of security, mode-II can achieve the best SOP with the increased number of IRS elements, while mode-I and mode-III have a similar SOP. Moreover, under the condition of small transmission power at HAP/PS or small number of IRS elements, mode-I has the best EST, while as the power or the number increases, the EST of mode-II becomes the best one.

Autonomous Underwater Vehicles (AUVs) are platforms suitable for a wide variety of applications in the marine environment with economic and operational advantages. In these applications an AUV performs a given task as a mission. During the mission execution, the AUV will move around the environment following paths that allow it to fulfill the mission's objectives. To achieve this, a reliable Navigation System (NS) is required. In addition to this, the current operating concept includes the deployment of multiple AUVs on a given area, thus a communication system between vehicles is also required. In the underwater environment both navigation and communication systems deals with the particular characteristics of the medium that limits the use of conventional techniques. In this work, a complete NS for an AUV is presented. The developed NS is based on an inertial navigation scheme with velocity and position aiding. The position aiding takes advantage of the communication system onboard the vehicle, which avoids the use of additional positioning systems. The fundamentals of the applied solutions are described and experimental results and implementation details are provided. Also conclusions and future works are presented.

The paper investigates the security of an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) network, where a base station (BS) transmits confidential information to the ground user (GU) via IRS-assisted UAV. The study explores using UAVs with IRS to enhance the security and reliability of wireless communication systems, particularly in the presence of eavesdroppers and friendly jammers. The beamforming at IRS-assisted UAV and UAV trajectory are jointly formulated as a non-convex optimization problem, which is solved by the deep reinforcement learning (DRL) algorithm to maximize the sum secrecy rate of GU with the aid of jammer. We proposed a dual-DDPG (D3PG) algorithm that utilizes the deep deterministic policy gradient (DDPG) structure to effectively address these dual non-convex problems of the UAV-trajectory optimization and the UAV-IRS beamforming optimization. The proposed algorithm's effectiveness and robustness are demonstrated through simulation results, with the IRS significantly enhancing the sum secrecy rate. Extensive simulations show that the proposed DRL-based D3PG scheme outperforms the traditional optimization schemes and ordinary DQN schemes.

This paper tackles the complex problem of maximizing the ergodic sum-rate in intelligent reflecting surface (IRS)-assisted full-duplex (FD) cluster non-orthogonal multiple access wireless-powered communication networks. The primary challenge lies in the joint optimization of hybrid access point (HAP) beamforming vectors, IRS phase shifts, and uplink power allocation for sensors, while considering practical constraints— including channel uncertainties, a nonlinear energy harvesting model, self-interference at both the HAP and sensors due to FD transmission, the rank-one constraint of the IRS, and the limited buffer capacity of the sensors. These factors result in an NP-hard optimization problem that conventional approaches cannot solve efficiently. To address this, we propose utilizing a deep reinforcement learning framework based on the twin delayed deep deterministic policy gradient (TD3) algorithm. Our results show that TD3 improves the system’s average sum-rate by up to 12% and 48% over the deep deterministic policy gradient (DDPG) and random allocation benchmarks, respectively. The results also show that both TD3 and DDPG have similar computational complexity. Thus, TD3 achieves a favorable balance between performance and computational complexity, making it a scalable and practical solution for next-generation networks where spectrum efficiency and throughput maximization are critical in hyper-dense environments.

This article introduces a novel strategy for wireless communication security utilizing intelligent reflecting surfaces (IRS). The IRS is strategically deployed to mitigate jamming attacks and eavesdropper threats while improving signal reception for legitimate users (LUs) by redirecting jamming signals toward desired communication signals leveraging physical layer security (PLS). By integrating the IRS into the backscatter communication system, we enhance the overall secrecy rate of LU, by dynamically adjusting IRS reflection coefficients and active beamforming at the base station (BS). A design problem is formulated to jointly optimize IRS reflecting beamforming and BS active beamforming, considering time-varying channel conditions and desired secrecy rate requirements. We propose a novel approach based on deep reinforcement learning (DRL) named Deep-PLS. This approach aims to determine an optimal beamforming policy capable of thwarting eavesdroppers in evolving environmental conditions. Extensive simulation studies validate the efficacy of our proposed strategy, demonstrating superior performance compared to traditional IRS approaches, IRS backscattering-based anti-eavesdropping methods, and other benchmark strategies in terms of secrecy performance.