Accurate long-range acoustic ranging is a critical enabling technology for Internet of Underwater Things (IoUT), particularly in deep-sea environments where electromagnetic and optical signals fail to propagate efficiently. In this regard, we propose an Acoustic Ranging algorithm based on BELLHOP ranging model and Dynamic ranging-ratio model (ARBD). Specifically, in response to the limitations of traditional ranging algorithms such as the shape of the Sound Speed Profiles (SSP), lack of consideration for reflection effects, and complex nonlinear root-finding methods, we transform the BELLHOP acoustic toolbox into a BELLHOP ranging model. A novel ARBD algorithm is developed by combining the fast convergence solution strategy of the Dynamic Ranging-ratio Model (DRM) with ray tracing. Furthermore, the Cramér-Rao lower bound (CRLB) is derived to establish the theoretical performance limits. The results demonstrate that the proposed algorithm reduces ranging errors compared to traditional algorithms, while maintaining fast computational efficiency and adaptability, particularly in long-range distance measurements in deep-sea environments.

Low Earth orbit (LEO) satellite communications play a critical role in achieving global connectivity, yet they face significant challenges due to high satellite mobility and incomplete channel state information (CSI). Moreover, the integration of reconfigurable intelligent surfaces (RIS) in certain scenarios introduces additional complexities. In this paper, we propose a novel MIMO channel prediction framework tailored for LEO satellite communications involving unmanned aerial vehicle-mounted RIS (UAV-RIS), employing a spatiotemporal-attention (ST-attention) mechanism to capture both the spatial correlations among antennas and the temporal dynamics of rapidly varying channels. Furthermore, we leverage masked pretraining to enhance the model’s robustness under scenarios of severe CSI incompleteness, enabling effective reconstruction of missing channel information. Comprehensive simulations demonstrate that our approach outperforms traditional model-based predictors, whether historical CSI is fully available or only partially observed.

Satellite edge computing networks are gaining momentum, necessitating efficient task scheduling and resource allocation to optimize performance. This paper focuses on collaborative computing among multiple edge satellites, addressing the dynamic nature of satellite networks and energy constraints. We propose the Delay-Optimal Task Scheduling Offloading and Resource Allocation (DO-TSO-RA) algorithm to minimize task processing delay and ensure network stability. By decomposing the problem into task scheduling, offloading, and resource allocation subproblems and employing alternating optimization and sequential task scheduling algorithm to solve them. Finally simulations demonstrates superior performance of DO-TSORA in reducing task delay and improving resource utilization compared to existing methods.

Inter-satellite links (ISLs) enable the dense satellite network to remarkably enhance the communication performance of the satellite system. However, the increasing number of LEO satellites and the widespread application of ISLs lead to severe inter-satellite interference (ISLI), restricting communication performance. Therefore, it is essential to study and further restrain the increasingly severe ISLI issue to make full use of the microwave spectrum resources. Meanwhile, considering the importance of satellite network to ground coverage, we construct the problem as a multi-objective optimization problem that maximizes inter satellite signal-to-noise ratio (SINR) and coverage rate. Furthermore, we adopt ε-constraint method to convert it into a single-objective optimization problem and propose a low-complexity maximum spatial uniformity constellation design method. Finally, the influencing factors of ISLI are analyzed through simulation, and the feasibility and effectiveness of the proposed method are verified.

The deployment of low-Earth orbit (LEO) constellations, combined with inter-satellite link (ISL) technology, will drive the development of future sixth-generation (6G) and satellite Internet of Things (SIoT). However, the deployment of a large number of LEO satellites and corresponding ISLs intensifies the spectrum resource scarcity in space, which may result in severe ISL interference (ISLI). Therefore, this paper proposes a theoretical framework for optimal design of LEO satellite constellations to mitigate ISLI. Specifically, the system average signal-to-interference-plus-noise ratio (SA-SINR) metric is introduced to depict ISLI, and deterministic analysis expression for SA-SINR is derived. Meanwhile, an integral merging algorithm (IMA) is proposed to accurately calculate the ground coverage area of satellite networks, considering the need to cover ground users in a wide distribution range. Then, the optimal constellation design issue is formulated into a mathematical multi-objective optimization problem, and an improved particle swarm optimization (PSO) scheme is designed to solve it. Simulation results show that the constellation designed based on the proposed approach outperforms the existing Starlink and OneWeb constellations in terms of ISLI and coverage performances, validating the effectiveness of our proposal.

Advancements in communication, manufacturing, and launch technologies have significantly accelerated the development of Low-Earth orbit (LEO) satellite communication constellations. Simultaneously, the rapid expansion of LEO satellite platforms presents new opportunities for navigation enhancements and substantial benefits for internet of things (IoT) applications. This survey highlights the latest advancements in the integrated communication and navigation (ICAN) technologies, emphasizing key research issues. Specifically, we discuss the motivation and feasibility of integrating communication and navigation on LEO satellite platforms, analyzing various LEO-based navigation paradigms while assessing their respective advantages and limitations. We further explore the mutual promotion of navigation and communication under the ICAN paradigm and discuss waveform design, analyzing the characteristics of different waveform design approaches. Finally, we highlight the unique challenges and identify open research topics within the ICAN framework for future exploration.

The advantages of Low Earth Orbit (LEO) satellite navigation in addressing the increasing demand for high-precision and universally accessible positioning have garnered widespread attention. Our work builds upon our previous development of the Zadoff-Chu non-orthogonal multiple access (ZC-NOMA) waveform for integrated communication and navigation (ICAN). Expanding on this foundation, we conceive the ICAN-oriented multi-satellite asynchronous transmission (ICAN-MSAT) framework, which allows users to concurrently receive communication and navigation signals from multiple satellites without synchronization, thus increasing communication flexibility and leveraging the geometric distribution and robust signals of LEO satellite constellations to enhance navigation performance. Within the ICAN-MSAT framework, we propose a novel multi-satellite asynchronous transmission oriented subcarrier and power allocation (MSASP) algorithm to manage mutual interference between communication and navigation components and inter-satellite interference (INSI) caused by asynchronous transmission, with the latter often overlooked in most existing work. Simulation results demonstrate that the proposed algorithm meets the performance requirements of both navigation and communication, achieving higher communication rates under the same navigation accuracy constraints compared to current benchmarks.

Underwater positioning is a crucial technology for Autonomous Underwater Vehicles (AUVs) to effectively carry out a range of underwater tasks. With an underwater environment that is complex and constantly changing, it can be challenging for a single positioning technology to keep up with long-range, multi-target, and high-precision applications. Solutions in the field of underwater positioning are continually evolving and improving. A multi-sensor fusion positioning scheme, which integrates multiple sensors including the Inertial Navigation System (INS), Long Baseline (LBL), Doppler Velocity Log (DVL), Conductivity Temperature Depth (CTD) and Depth Gauge (DG), can provide more accurate and reliable positioning. In this paper, a Bayesian inversion based Multi-sensor Fusion Localization (BIMFL) algorithm is proposed to enhance the performance of underwater fusion positioning. The proposed algorithm combines the strengths of multiple sensors and provides more precise location information. BIMFL uses the residual values of the measured data from various sensors as observations. Bayesian inversion effectively integrates prior information with observational information, both of which have their covariance matrices considered, hence determining the appropriate parameters of the fusing positioning system. Additionally, an update iterative process based on the Extended Kalman Filter (EKF) is adopted in BIMFL to refine positioning results. Simulation and experimental results have shown that BIMFL surpasses traditional single-sensor and EKF-based filtering methods.

Autonomous underwater vehicle (AUV) has been widely used in underwater missions. Cooperative localization (CL) is a key technology especially for multi-AUVs collaborative operations. With great demands for accurate and real-time localization, the error dispersion in nonlinear fusion and information transmission difficulties caused by underwater environment limitations become challenges in multi-AUVs CL. In this article, a federated learning (FL) framework for multi-AUVs CL is designed, based on which a novel CL algorithm combining the FL and extended Kalman filter (EKF) is proposed. The proposed FL-EKF algorithm can fuse the advantages of EKF and FL adequately to realize high-precision real-time underwater CL in long-duration operations. Simulations and experiments are conducted to verify the performance of the proposed algorithm.

With the evolutional development of maritime networks, the explosive growth of maritime data has put forward elevated demands for the computing capabilities of maritime devices (MDs). Unmanned aerial vehicle (UAV) is able to alleviate the computing pressure of MDs by forwarding the computing tasks to the edge server on the coast. However, UAV relaying introduces a significant security challenge due to the vulnerability of line-of-sight (LoS) communication channels, which can be exploited for eavesdropping on computing tasks. In this paper, an efficient secure communication scheme is proposed for UAV-relay-assisted maritime mobile edge computing (MEC) with a flying eavesdropper. The secure computing capacity of MDs is maximized by jointly optimizing the transmit power, time slot allocation factor, computation optimization and UAV trajectory. Due to multi-variable coupling, the formulated optimization problem (OP) is non-convex. We first transform OP by introducing auxiliary variables. Then, the transformed OP is decomposed and solved in an iterative manner by applying block coordinate descent (BCD) and successive convex approximation (SCA). Numerical results show that the secure computing capability of the UAV-relay-assisted maritime MEC system of proposed secure communication scheme can be effectively improved compared with benchmarks.

In this paper, we study an uplink reconfigurable intelligent surface (RIS) aided orthogonal frequency division multiplexing (OFDM) system, where the RIS simultaneously acts as a passive reflector for a user’s quadrature phase-shift keying (QPSK) OFDM signal and an information source which sends information through spatial modulation of the reflected signal. We present a maximum-likelihood (ML) detector for the detection of RIS signals and analyze the error probability theoretically. We derive closed-form expressions of the error probability for the detection of RIS signals for the special case of flat-fading channels. Then we analyze the error probability of the detection of user’s signals. Analysis indicates that faulty detection of RIS signal results in channel estimation errors, consequently compromising the precision of OFDM signal detection. High correlation between code words limits the error propagation to the user but decreases accuracy for RIS signal’s detection. Addressing this, an index modulation with beamforming (IM-BF) code is proposed to enhance the transmission for the user, and a quadrature reflection modulation with beamforming (QRM-BF) code is proposed for a performance trade-off between the user and the RIS. Simulation results demonstrate the theoretical results, and show that the QRM-BF code has better performance for the RIS transmission, and both schemes outperform benchmark schemes from the literature for the user’s transmission. Simulation results also show that the IM-BF scheme significantly improves the user transmission performance compared to the state of the art.

The underwater sensor network plays a critical role in the ocean data collection owing to its capability of providing reliable and wide communication coverage. How to assure the lifetime performance of the network with a limited energy supply has always been a key research issue. In this article, we propose an underwater acoustic sensor network model which supports data delivery from each underwater sensor node (USN) to the sea surface sink node (SN) in either direct or relay-assisted transmission mode, i.e., transmission mode between each USN and the SN can be dynamically selected according to network’s current state. To optimize its lifetime performance, we model the mode selection and resource allocation issues jointly into a nonconvex and mixed-integer programming problem. In order to efficiently solve it, we further divide the original problem into two subproblems: 1) resource allocation subproblem and 2) joint mode and relay selection subproblem. We prove that the reformulated nonconvex resource allocation problem can be equivalent to a convex optimization problem, and its optimal solution can be found by using Lagrange dual decomposition method. For the second subproblem, some critical conditions are analyzed to determine optimal relays with the criterion of balancing energy consumption between USNs, and a matching approach is designed to obtain the mode and relay selection results based on USNs’ priorities. Simulation results validate that the proposed transmission scheme is effective and superior to the traditional time division multiple access scheme.

For the increasingly strong demand for low-orbit navigation enhancement, a novel navigation and communication integrated (NAVCOM) waveform for the LEO constellation is proposed, which can conduct communication and navigation simultaneously. Zadoff-Chu (ZC) sequence with controllable power is superimposed on the communication signal in the frequency domain as the positioning signal. Furthermore, positioning can be conducted with both time-domain correlation detection (TDCD) and frequency-domain phase estimation (FDPE) for higher ranging accuracy benefits from the Fourier invariance of ZC sequence. Interference between positioning and communication components is analyzed, and the Cramer-Rao low bound (CRLB) of ranging error is given. The performance evaluations show that the novel waveform can achieve high-precision positioning without signifilcantly affecting communication performance.

In this paper, we investigate the collaborative computation offloading method in satellite edge computing by allowing computation tasks to be executed by multiple satellites with computing capacity. The main purpose is to optimize the resource allocation to minimize the energy consumption of the network, which is formulated as a non-convex optimization problem. To solve it efficiently, we first provide the optimal task allocation scheme and then divide the original optimization problem into two subproblems based on an alternative optimization method. Although two subproblems are still non-convex, we can apply successive convex approximation method to deal with them and design an iterative algorithm to solve them. Finally, simulation results demonstrate the superiority and effectiveness of our proposed algorithm.

In this paper, we propose a SWIPT-based distributed node sleep scheduling method combining with clustering technology. The information transmission layer adopts a distributed topology structure. Active nodes are divided into cluster head nodes and cluster member nodes. The cluster head node acts as a relay of cluster member nodes, which can reduce the single-hop communication distance and greatly reduce the energy consumption of information transmission. The energy-carrying communication process is divided into three time slots: the first time slot, the sensor node collects energy, according to the residual energy of the node, the energy collected in the first time slot, and the requirements of network connectivity, the sleep scheduling optimization algorithm will divide the nodes into sleeping nodes and active nodes, and the active nodes are clustered according to the relative position of the information sending rate of the node; in the second time slot, the cluster head node receives the information sent by the cluster member node, and the sleeping node continues to collect energy; In three time slots, the cluster head node processes the collected data and sends it to the sink node, and the cluster member nodes and sleeping nodes continue to collect energy. Finally, according to the user's requirements for energy utilization and network coverage, an appropriate time slot division factor is selected. Compared with the centralized sleep scheduling method, the energy utilization and coverage of the network are improved.

Enabling relay-assisted device-to-device (RA D2D) transmissions is a promising way to fully explore the benefits of developing D2D communications in cellular systems. However, taking relay-assisted D2D mode into account make the scheduling at the base station (BS) even more challenging. This paper focuses on the scheduling issues for the cellular system involving relay-assisted D2D communications. Aiming to maximize the cell-wise energy efficiency (EE), we formulate the issue of joint scheduling on D2D mode selection, channel allocation, and power coordination into a mathematical optimization problem. The formulated problem is then decomposed into two subproblems which can be solved efficiently by referring to our previous work. Particularly, in the context of maximizing the EE, we present a strategy to coordinate the transmit powers for the cellular user equipment (UE) and D2D UE when they are sharing the same channel. Simulation results validate the effectiveness of our proposals on Improving the overall EE.

This paper considered a data rate maximization problem of relay satellite network and aimed to overcome the difficulty of direct transmission between the satellites and ground station. The proposed optimization problem was first formulated as a mixed integer non-convex programming which is difficult to solve. Then, to design an efficient solving method, the proposed optimization problem were approximately decoupled into two subproblems including relay selection problem and power allocation problem. Through a node virtualization method, the relay selection problem was converted to a maximum weighted matching problem. However, the power allocation problem was still a non-convex problem but could be approximated as convex problem by using difference of convex (DC) programming. Moreover, an iterative algorithm was designed to acquire the optimal solution of approximated convex problem based on the Lagrangian dual method. Finally, simulation results are provided to demonstrate the superiority of the algorithm.

In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

In the past several years, various visual object tracking benchmarks have been proposed, and some of them have been used widely in numerous recently proposed trackers. However, most of the discussions focus on the overall performance, and cannot describe the strengths and weaknesses of the trackers in detail. Meanwhile, several benchmark measures that are often used in tests lack convincing interpretation. In this paper, 12 frame-wise visual attributes that reflect different aspects of the characteristics of image sequences are collated, and a normalized quantitative formulaic definition has been given to each of them for the first time. Based on these definitions, we propose two novel test methodologies, a correlation-based test and a weight-based test, which can provide a more intuitive and easier demonstration of the trackers’ performance for each aspect. Then these methods have been applied to the raw results from one of the most famous tracking challenges, the Video Object Tracking (VOT) Challenge 2017. From the tests, most trackers did not perform well when the size of the target changed rapidly or intensely, and even the advanced deep learning based trackers did not perfectly solve the problem. The scale of the targets was not considered in the calculation of the center location error; however, in a practical test, the center location error is still sensitive to the targets’ changes in size.

Fuel-powered UAVs have long endurance of flight, heavy payload and adaptation to extreme environment. The mechanical operation and communication power are supported independently by fuel and batteries. In the paper, we study the energy efficiency of the communication system with a fuel-powered UAV relay. We consider a three-node communication network, consisting of a mobile relay, a source node, and a destination node. The UAV relay is able to change its 3-D trajectory to maintain high probability of LoS channels, receiving information from the fixed source node and transmitting it to the fixed destination node. The power allocation scheme and UAV's trajectory are designed to maximize the system energy efficiency, considering the constraints of speeds, UAV's altitudes, communication and mechanical energy consumption, the required data rates of the destination node and information-causality. We solve the power allocation sub-problem by splitting the domain of variables and transforming it into a convex optimization problem. And then a suboptimal scheme is provided to design the trajectory based on successive convex approximation method. Numerical results show the convergence of the proposed schemes and the performance of the proposed algorithms. The influences of time slots, constraints of fuel, communication power and required data rates are discussed.

To fully explore the benefits of developing device- to-device (D2D) communications in cellular systems, enabling relay-assisted (RA) D2D transmissions is a promising way. However, involving RA D2D mode will make the design of scheduling scheme at the base station (BS) side even more challenging. This work focuses on design of a scheduling scheme involving RA D2D mode for BS, which jointly considers power coordination, relay selection, mode selection, and resource allocation. Aiming to maximize the cell- wise throughput, we formulate such a scheduling issue into a mathematical optimization problem. We show how to decompose the formulated problem into two subproblems and solve them separately by using exiting algorithm and corresponding mathematical optimization theories. Particularly, the integer programming problem on mode and channel assignments is transformed into a linear programming problem to improve the solving efficiency. Simulation results validate the performance of the joint scheduling scheme in terms of cell-wise system capacity.

The Internet of Things (IoT) is a new network that connects massive devices which have the communication ability. With the requirement of various services and the wireless spectrums becoming increasingly scarce, we consider a nonorthogonal multicarrier transmission scheme that is spectrum efficient frequency division multiplexing (SEFDM) as the downlink transmission scheme for IoT network. SEFDM has the merit of improved bandwidth usage efficiency, but the serious inter carrier interference (ICI) will be introduced by multiplexing overlapped carriers. Thus, the signal detection is challenged for recovering the signal which is suffered from ICI due to the loss of the orthogonality. In this paper, a low complexity detector based on quasi-orthogonality compensation (QOC) is proposed in the downlink receiver. And the complexity of the QOC detector is also analyzed. Moreover, a novel detector which joint QOC and fixed sphere decoding (FSD) algorithm is proposed. The bit error rate performances of QOC and QOC-FSD detectors are evaluated by numerical simulations. Numerical results show that the QOC and QOC-FSD detectors can achieve better performance than conventional iterative detection (ID) and ID-FSD, respectively. Furthermore, QOC-FSD detector performs a lower complexity than ID-FSD detector.

In this paper, we consider a single cell downlink non-orthogonal multiple access (NOMA) network and aim at maximizing the energy efficiency. The energy-efficient resource allocation problem is formulated as a non-convex and NP-hard problem. To decrease the computation complexity, we decouple the optimization problem as a subchannel matching scheme and power allocation subproblems. In the subchannel matching scheme, a non-cooperative game is applied to model this problem. To discuss the existence of Nash equilibrium (NE), we introduce a super-modular game and then design an algorithm to converge to the NE point. Moreover, a greed subchannel matching algorithm with low complexity is given through a two-way choice between users and subchannels. However, for given subchannel matching scheme, power allocation is still a non-convex problem, which is difficult to get the optimal solution. We then transform the non-convex problem to a convex problem by applying a successive convex approximation method. Afterward, we provide an algorithm to converge to suboptimal solution by solving a convex problem iteratively. Finally, simulation result demonstrates that the energy efficiency performance of the NOMA system is better than the orthogonal frequency division multiple access system.

Efficient timing synchronization in an IDMA system must be obtained before signal detection and decoding. However, the research on timing synchronization in IDMA system has not been studied widely. In this paper a new timing acquisition scheme for asynchronous IDMA uplink based on sliding correlation is proposed and its performance in terms of the probability density function is developed. The simulation results show that the proposed scheme is effective even under high MAI conditions.

In this paper, an onboard Asynchronous Transfer Mode (ATM) switching fabric based on Ant Algorithm with blocking avoidance is proposed in satellite ATM network. The onboard ATM switching fabric is an improved Benes network based on Ants Flooding Mechanism and Blocking Avoidance Mechanism to solve bandwidth-limited and delay-bounded problems in satellite communication network. Compared with traditional self-routing Benes network, better performances of proposed onboard ATM switching fabric are achieved.