This paper proposes a multiple access control (MAC) protocol for integrated sensing and communication in underwater acoustic networks (ISAC-UANs) with dynamic target monitoring, localization, and tracking, where some ISAC nodes are deployed to collaboratively communicate and detect targets via integrated underwater acoustic signals. The proposed integrated underwater sensing and communication (USAC)-MAC protocol focuses on addressing the challenges of packet collisions and resource contention in the dual-function network. Specifically, the protocol is divided into three stages: target monitoring, target localization, and the target tracking phase. The target monitoring phase is carried out to initialize the network topology and search for detection targets. The conflict-free concurrent transmission constraints are established based on topological information to enable conflict-free and efficient data transmission. After detecting the target, the protocol transfers into the target localization phase, which localizes the target applying the time of arrival (TOA) method. We model the optimization problem to minimize the Cramér-Rao lower bound (CRLB) and employ the particle swarm optimization (PSO) algorithm to solve it under the constraint of the geometric position of nodes. During the target tracking phase, we enhance target detection accuracy by dynamically selecting detection node clusters based on predicted target trajectories. Concurrently, we schedule packet transmission times to minimize the communication frame length. Thereby, a joint optimization problem for the CRLB of localization and the frame length of communication is established and addressed by the simulated annealing (SA) algorithm, ensuring detection precision while reducing communication latency. Simulation results demonstrate that the proposed protocol exhibits superior performance with higher throughput, shorter frame length, shorter information update times, and more precise detection accuracy.

The integrated satellite-terrestrial network (ISTN) with inter-satellite free space optical (FSO) links and satellite-to-ground (S2G) radio frequency (RF) links is becoming an important enabler for the Internet of Things (IoT). However, investigating the performance of the ISTN remains several challenges, i.e., the high mobility and long propagation delays of S2G links, and the highly correlated line-of-sight S2G channels. To address these challenges, we propose a hybrid RF/FSO cooperative satellite-terrestrial communication system that integrates the space time block code with cooperative transmission to enhance the coverage probability and communication reliability of satellite downlink transmission. We model the inter-satellite FSO channels by considering pointing and tracking errors, and the S2G RF channels using the shadowed-Rician fading model. Subsequently, we derive the probability density function and cumulative distribution function for both RF/FSO signal-to-noise ratio (SNR) under channel estimation errors and the sum of two RF SNRs from the same distribution family. Finally, for the proposed system, closed-form expressions of the outage probability (OP) and the upper bound for the average bit error probability (BEP) are derived. The proposed system outperforms SISO and MISO systems by reducing average BEP, outage probability, and robustness to channel estimation errors.

Multi-node underwater acoustic sensor networks (UASNs) are critical for applications such as environmental monitoring and target detection. However, they face unique challenges, including long propagation delays, narrow bandwidth, and the complex marine environment. This makes topology optimization for achieving high coverage, low delay, and high throughput difficult especially under high-load application scenarios. To address these difficulties, We propose a unified framework, termed RLMA-UNTO, which integrates reinforcement learning (RL) with metaheuristic algorithms for multi-objective topology optimization in UASNs. The framework synergistically combines the global exploration strengths of population-based metaheuristics with the local refinement capabilities of an actor–critic RL module. Simulations under high-load conditions for network sizes of 15 to 70 nodes show that RLMA-UNTO consistently outperforms its traditional metaheuristic variants, as well as benchmark optimizers. Specifically, averaged over all network sizes from 15 to 70 nodes, the RLMA-UNTO-based optimizers achieve consistently higher coverage than their metaheuristic counterparts. The average network throughput is increased by about 20 to 24 bits per second (bps). At the same time, the average end-to-end delay is reduced by 43 to 56 seconds (s), corresponding to a 23% to 25% reduction. Similar performance margins are also observed when comparing these RL-enhanced schemes with the benchmark optimizers.

Scheduling-based Medium Access Control (MAC) protocols significantly augment the ability for simultaneous transmissions while mitigating signal interference, thereby elevating the bandwidth utilization efficiency crucial for data-collection-oriented underwater acoustic sensor networks (UASNs). However, existing scheduling-based MAC protocols for UASNs primarily analyze the interference between pairwise links or nodes, neglecting the cumulative interference effect fostered by the concurrent transmissions of multiple nodes. Besides, the existing multi-node interference models used in Radio Frequency (RF) based wireless networks only describe the total strength of cumulative interference signals and do not consider scenarios in which the interference from multiple nodes does not align at the receiver. This paper introduces an innovative interference model to extensively quantify the non-aligned overlapping interference from multiple packets on a single valid packet in segments. Based on this segmented interference model, we establish a transmission time constraint that can avoid excessive interference in high-interference segments and utilize low-interference segments for parallel reception, thereby enhancing the degree of time reuse. The optimization of transmission slot scheduling is modeled as a sequential decision-making process, wherein the action space is delineated by considering the constraints related to multi-packet interference. Furthermore, the Reinforced Packet Level Slot Scheduling (R-PLSS) algorithm leveraging the principles of approximate dynamic programming is proposed to allocate transmission slots within each frame. The simulation results demonstrate that the R-PLSS algorithm avoids the practical packet demodulation failure caused by excessive interference from multiple packets and can comprehensively improve the channel utilization efficiency.

Rejecting outliers before applying classical robust methods is a common approach to increase the success rate of estimation, particularly when the outlier ratio is extremely high (e.g. 90%). However, this method often relies on sensor- or task-specific characteristics, which may not be easily transferable across different scenarios. In this paper, we focus on the problem of rejecting 2D-3D point correspondence outliers from 2D forward-looking sonar (2D FLS) observations, which is one of the most popular perception device in the underwater field but has a significantly different imaging mechanism compared to widely used perspective cameras and LiDAR. We fully leverage the narrow field of view in the elevation of 2D FLS and develop two compatibility tests for different 3D point configurations: (1) In general cases, we design a pairwise length in-range test to filter out overly long or short edges formed from point sets; (2) In coplanar cases, we design a coplanarity test to check if any four correspondences are compatible under a coplanar setting. Both tests are integrated into outlier rejection pipelines, where they are followed by maximum clique searching to identify the largest consistent measurement set as inliers. Extensive simulations demonstrate that the proposed methods for general and coplanar cases perform effectively under outlier ratios of 80% and 90%, respectively.

Underwater acoustic communication systems face considerable challenges due to the joint distortion of delay and Doppler in time-varying channels. Traditional grid-based channel estimation methods suffer from a trade-off between resolution and computational complexity. In this paper, we propose a Newtonized off-grid parameter estimation approach with second-order optimization techniques to address this challenge. By modeling the Doppler estimation part as a generalized likelihood maximization, we derive the closed-form for the first- and second-order derivatives of the cost function. This allows for Newton iterations to refine coarse grid estimates to sub-grid precision with minimal computational overhead. Numerical simulations verify that the proposed method performs better in root-mean-square-error(RMSE) compared to traditional grid-based methods.

In this work, we propose a multi-aircraft cooperative handover scheme for satellite-to-aircraft communication systems. Specifically, considering the dual characteristics of aircraft resource demands and three satellite states (normal, congested, and failed), multiple aircraft collaborate to make handover decisions while maintaining network stability and avoiding congestion. We formulate the cooperative handover problem as a multi-objective optimization problem to minimize communication latency and network congestion while maximizing connection stability. To solve this problem, we first model the handover scheme into the Markov decision process to facilitate seamless satellite-aircraft handover. Then we develop a multi-agent deep deterministic policy gradient (MADDPG) algorithm with centralized training and decentralized execution architecture. Due to the time-varying nature of the action space and the constraint that action selection is limited to currently visible and undamaged satellites, we implement an action mask approach to effectively filter out illegal actions instead of using conventional negative reward methods. The simulation results demonstrate that the proposed framework effectively reduces handover frequency, minimizes communication latency, and achieves better network load balancing, validating its feasibility and effectiveness in satellite-toaircraft communication systems.

Low earth orbit (LEO) satellite communication systems have become the indispensable part of sixth generation (6 G) communications. However, since the LEO and geostationary earth orbit (GEO) satellite systems will inevitably share limited frequency resources, the communication signal from LEO satellites have the possibility to cause harmful interference to the GEO systems. To address this issue, this paper proposes an adaptive beamforming strategy to mitigate the interference while improving the system spectral efficiency (SE). In specific, we formulate the problem as the nonlinear mixed integer programming (NMIP) optimization problem, and apply the weighted minimum mean square error (WMMSE) and alternative optimization algorithm to obtain the closed-form solutions. Furthermore, to reduce the high complexity of beamforming when the LEO system serves a massive number of ground users (GU), we propose a channelaware user clustering scheme utilizing the channel correlation between GUs so that all GUs within the same cluster share the same precoding vector. Extensive simulations show that the proposed scheme effectively mitigates the interference.

The acoustic-based internet of underwater things (IoUT) is considered as one of the most challenging environments for communication because of issues in the underwater acoustic communication (UWAC) channel, such as multipath propagation and Doppler shift. Accurately estimating these effects without sacrificing a significant portion of the bandwidth is extremely difficult, underscoring the need for robust and sophisticated techniques. In this paper, we propose an acoustic-based unique word orthogonal frequency division multiplexing (UW-OFDM) scheme to enable communication between IoUT nodes over a doubly-selective channel. The proposed scheme employs a joint time-frequency channel estimation approach by leveraging the time-domain guard interval to identify the channel paths while utilizing only 3.1% of the frequency-domain subcarriers, compared to 25% in conventional methods, to track the channel path coefficients. The proposed method significantly enhances spectral efficiency while maintaining resilience to multipath propagation and Doppler shift impairments inherent in UWAC. Furthermore, the scheme eliminates inter-block interference, which is critical in UWAC due to its distinctive propagation characteristics. We evaluate the performance of the proposed methods using both simulations and real-world experimental tests over a 300-meter underwater channel. The results demonstrate that the proposed approach offers a reliable IoUT communication solution, achieving up to a 5 dB improvement in bit error rate and up to 17.56% higher subcarrier utilization compared to conventional schemes. In addition, the scheme exhibits strong robustness against Doppler shift effects with similar peak-to-average power ratio performance and a modest increase in computational complexity.

The archipelagic environment is an important feature of island distribution. The archipelago environment, an important feature of island distribution, allows for more wireless communication nodes to be deployed, which helps achieve large-scale network coverage at sea. This provides communication convenience for real-time and 3-dimensional oceanographic observations. On the other hand, air-sea-ground networking should overcome the complex communication conditions brought by the archipelago and shallow water. In this article, we propose an air-sea-ground integrated observation system based on ad hoc network. It combines underwater acoustic communications and LoRa communications to construct an air-sea-ground transmission system. The core equipment of this system includes the scientific instrument interface module (SIIM), LoRa relay node, and underwater acoustic communication (UAC) modem. The UAC Modem and LoRa used as communication modules are connected to various ocean observation sensors through the SIIM. Opportunistic routing and transmission control are implemented by embedded chip programming. The system has been tested on Zhairuoshan Island, Zhoushan Archipelagoes, China, and the transmission success rate exceeds $90 \%$. The preliminary verification proves the effectiveness of the proposed system, which is of great significance to marine scientific research, environmental protection, and marine economic development.

In this paper, we investigate the multiple autonomous underwater vehicles (AUVs) collaborative data collection and tra- jectory planning scheme in integrated sensing and communication for underwater acoustic networks (ISAC-UANs). To collect data efficiently, AUVs traverse the overlapping communication region of sensor nodes instead of accessing every sensor node. In addition, the sensing function is required to enable obstacle avoidance. To this end, we first propose the time division multiple access (TDMA)-based communication and mono-static sensing strategy for ISAC-UANs. Secondly, we formulate the data collection and trajectory design problem into a min-max problem to minimize energy consumption and enhance the network throughput. To solve this problem, we decouple it into three sub-problems: the sensor node clustering problem, the cluster traversal problem, and the trajectory planning problem. The first sub-problem is to determine the AUV traversal area to collect data, which is solved by an overlapping communication regions based clustering algorithm. The second sub-problem is to determine the AUVs' cluster traversal sequence minimizing the traversal length, which is solved by an min-max ant colony optimization (ACO)-based algorithm. The third sub-problem involves planning the optimal trajectory for AUVs to reach the data collection area while avoiding obstacles, which is solved by the soft actor-critic (SAC)-based online trajectory planning algorithm. Extensive simulations demonstrate that the proposed scheme outperforms benchmarks in terms of trajectory length, energy consumption, and network throughput.

In this paper, we investigate the coverage path planning (CPP) scheme for autonomous underwater vehicles (AUVs) cooperative environment detection in integrated underwater acoustic communication and detection networks (UCDNs), where multiple AUVs detect unexplored oceanic environments and avoid obstacles. Firstly, we present the detection range prediction model related to oceanic environmental parameters and propose the detection and communication scheme in UCDNs. Secondly, to conduct the cooperative environment detection mission, we formulate the CPP problem as a mixed combinatorial and sequential quadratic optimization problem to maximize the coverage ratio and minimize the path length of AUVs. To solve this problem, we investigate the multi-agent proximal policy optimization (MAPPO)-based CPP scheme. In specific, the CPP problem is modeled as a partially observable Markov decision process (POMDP). Since the path planning of the AUVs is not only related to the local information but also the other AUVs' information, the information should be shared among AUVs based on the UCDNs. Furthermore, we introduce the MAPPO-based algorithm under the centralized training with decentralized execution (CTDE) architecture. Extensive simulations are carried out to demonstrate the strength of the proposed scheme.

The characteristics of volatile ocean environments and complex acoustic communication channels have posed great difficulties to the design of real-time data transmission in underwater wireless sensor networks (UWSNs). In this paper, we develop a centralized cross-layer protocol that mitigates network interference and maximizes concurrent transmissions to reduce end-to-end delay. Instead of optimizing individual layers separately, we blend the traditional layered architecture and combine the physical layer, medium access control (MAC) layer, and network layer functions together. Specifically, we optimize the power control in the physical layer, link scheduling in the MAC layer, and routing in the network layer jointly to achieve a global optimization of end-to-end delay. Firstly, the joint design problem is formulated as a mixed integer linear programming (MILP) problem, which is an NP-hard problem and hard to solve mathematically. Then, we propose a bio-inspired-algorithm-based solution, namely discrete improved artificial bee colony (DIABC) algorithm, aiming at finding an approximate optimal cross-layer scheduling scheme. To further reduce end-to-end delay, we optimize the uplink frame structure and routing metric in the centralized cross-layer framework. The simulation results show that the proposed protocol achieves network performance improvement in terms of end-to-end delay, service rate, and energy consumption.

The Internet of Underwater Things (IoUT) is an emerging field of the Internet of Things (IoT) that extends IoT technologies to ocean environment. Recently, underwater wireless sensor networks (UWSNs) have shown great potential in IoUT and are envisioned to facilitate various IoUT applications. Considering the broad categories of IoUT applications, the routing protocol in UWSNs is expected to provide data transmission with different requirements. However, due to potential conflicts between different requirements of IoUT applications, most existing work only takes a certain requirement into account to optimize the corresponding transmission objective, which is often achieved at the cost of other requirements. To address this problem, a multi-objective routing (MOR) protocol is proposed in this paper to cater to the diverse demands of IoUT applications simultaneously. Specifically, the MOR protocol considers three key requirements in IoUT applications enabled by UWSNs, i.e., energy consumption, end-to-end delay, and link quality, to formulate the multi-objective routing optimization problem. Additionally, this paper also considers link congestion as an optimization objective to avoid frequent use of the local optimal communication links. Then, to solve the multi-objective routing optimization problem, a heuristic scheme based on the non-dominated sorting genetic algorithm II (NSGA-II) is proposed. Simulation results have shown the feasibility and effectiveness of the proposed MOR protocol and verified the necessity of multi-objective optimization in IoUT.

In this paper, we propose a value of information (VoI)-based packet scheduling scheme (VBPS) in autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs), where AUVs act as mobile sensor nodes to collect data from areas not accessible to static nodes and then relay data via static nodes. VoI is a performance metric to measure the importance of data packets with different levels of urgency. The proposed scheme aims to avoid collision with the ongoing packet transmission of static nodes without their accurate global information. In specific, the static node localization stage and the topology construction stage are carried out to obtain the local information. Furthermore, the transmission scheduling stage is implemented to avoid packet collision and formulates a combinatorial optimization problem maximizing VoI under the constraint of packet collision avoidance. To solve this complicated problem, a low-complexity distributed search algorithm is proposed, which exploits the spatial-temporal reuse to establish data packet collision constraints and then determines the next-hop node and data transmission time for AUVs. In addition, a collaborative search algorithm is proposed to avoid packet collision among different AUVs by enabling collaboration among AUVs. Extensive simulation results under various scenarios demonstrate the superior performance of the proposed scheme.

In this paper, we propose the multi-autonomous underwater vehicle (AUV) collaborative data collection in integrated underwater acoustic communication and detection networks (UCDNs). Specifically, multiple AUVs collaboratively traverse the sensor nodes to collect data while detecting the environment to avoid obstacles along the trajectory. We first propose a time division multiple access (TDMA)-based packet transmission and active bistatic sonar detection strategy for UCDNs to transmit the sensor data and detect the unknown environment. Furthermore, we formulate the collaborative data collection problem as a mixed combinatorial and sequential quadratic optimization problem to minimize the trajectory length of multiple AUVs. To solve this problem, we decouple it into two subproblems, i.e., the node traversal subproblem and the trajectory planning subproblem. The former subproblem is converted into the multi-traveling salesman problem (MTSP), which is solved by the Q-learning-based algorithm to improve the robustness. The latter subproblem is optimally planning each AUV's trajectory while avoiding obstacles, which is solved by the soft actor-critic (SAC) algorithm to online make continuous trajectory decisions. Simulation results demonstrate that the proposed scheme outperforms benchmarks in terms of energy consumption and overall trajectory length.

This paper studies autonomous underwater vehicles (AUV)-assisted underwater acoustic sensor networks (UASNs), where AUVs act as mobile sensor nodes to collect information from areas not accessible to static nodes and then relay data via static nodes. Due to the difficulty of obtaining the accurate global information of all the static nodes, we propose a novel packet scheduling scheme by utilizing local information obtained by AUVs. In the proposed scheme, the localization of static nodes stage and the topology construction stage are carried out beforehand to obtain the local information, based on which the transmission scheduling stage is implemented. Furthermore, in the transmission scheduling stage, we design a value of information (VoI)-based packet transmission scheduling (VBPS) strategy to avoid packet collision. Specifically, we introduce a performance metric, i.e., VoI, to measure the importance of data packets with different levels of urgency. Then, we formulate a combinatorial optimization problem to maximize VoI taking packet collision avoidance into consideration. A low-complexity distributed search algorithm is proposed to solve the problem, which exploits the spatial-temporal reuse to establish data packet collision constraints and then determines the next-hop node and data transmission time for AUVs. Extensive simulations under various scenarios are carried out to evaluate the performance of the proposed algorithm.

With the rapid development of autonomous underwater vehicles (AUVs) and the continuous improvement of marine exploration requirements, AUV formation has emerged as a promising technique for performing underwater tasks with greater flexibility, adaptability, and scalability. To achieve AUV formation control, underwater acoustic communication networks (UACNs) are widely used to support information exchange among AUVs. Considering broadcast communications in fully connected mobile UACNs, existing medium access control (MAC) protocols face severe packet collision risk or low reuse efficiency. To improve the timeliness and reliability of the information exchange among AUVs, we develop an efficient scheduling-based adaptive broadcasting MAC (AB-MAC) protocol. The AB-MAC protocol is incompletely centralized, which can be reflected in two aspects. First, to ensure the consistency of the transmission schedule in the network, the AB-MAC protocol selects a control node for each frame to schedule the transmission of all nodes according to the updated network topology. The slot lengths and transmission sequences are changeable in each frame to avoid packet collisions and minimize frame lengths. Second, to further improve the efficiency and robustness of the protocol, all nodes adaptively adjust their transmission time by integrating the information contained in their previously received control packets and ordinary packets. The transmission scheduling problem is formulated as a combinatorial optimization problem, which can be solved by our proposed improved Genetic Algorithm. Numerical results demonstrate that the AB-MAC protocol not only performs well in terms of network throughput and average update interval but also has a high level of robustness even in low-quality underwater acoustic channels.

The demand for transport between terminals within port areas, known as inter terminal transportation (ITT), is increasing. This paper proposes a dynamic rolling horizon scheduling strategy for ITT using a fleet of waterborne Autonomous Guided Vessels (waterborne AGVs). The strategy is dynamic in that it can handle dynamically arriving ITT requests. Every certain period of time, transport schedules are updated according to the current vessel states, dynamic waterway transport network, and ITT requests over a future time horizon. Specifically, the dynamic scheduling problem is mathematically modeled in a rolling horizon fashion considering time windows of ITT requests, capacity limits of waterborne AGVs and load/unload service times at terminals. Considering the computational complexity for possible large scale ITT scenarios, we further propose an efficient solution approach based on improved insertion, tabu search and restart heuristics. Initial routes are first constructed by inserting new ITT requests into the previously computed routes in the rolling horizon framework. Tabu search with two types of neighborhoods are then designed to improve the initial routes. Moreover, a select-remove-insert restart procedure is activated to diversify the search space whenever necessary. A waterborne ITT network in the port of Rotterdam is considered. Comprehensive simulations based on realistic ITT dataset are run to demonstrate the effectiveness of the proposed dynamic scheduling strategy. This work could be readily used to build towards a fully autonomous waterborne ITT system. Insights that support long term strategical decisions, such as the fleet size, could also be gained from the simulations.

With the development of the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have become an enabling system to support real-time and continuous data transmission. Existing scheduling-based medium access control (MAC) protocols ignored the limitations between the generation time and the transmission time of forwarding packets. This results in unavailability of some scheduled slots, and makes packets endure more waiting time on relay nodes and thereafter longer end-to-end delay. To reduce transmission latency, this article develops a novel scheduling-based MAC protocol, which schedules slots in the packet level. All packets to be generated and transmitted in a frame are predicted and scheduled. Considering our defined packet collision constraint and traffic-flow constraint, we formulate the slot scheduling problem in a frame into a combinatorial optimization problem, which ensures that all source packets transmitted in a frame can be delivered to the sink node with the minimum average end-to-end delay within the same frame. To solve this problem, two algorithms are proposed, namely, an optimal packet-level slot scheduling (PLSS) algorithm and a heuristic approximate PLSS (PLSS-A) algorithm. The performances of our proposed protocol using both algorithms are evaluated with different network scales, packet lengths, and offered traffic loads. Numerical results demonstrate that both PLSS and PLSS-A perform well in terms of average end-to-end delay and service fairness, and have an advantage in the network throughput in large-scale networks. Our proposed PLSS MAC protocol is predicted to be promising in large-scale UASNs with demand for real-time and long-term monitoring.

In wireless federated learning system, wireless communication and local computation have a significant impact on the learning latency due to the limited bandwidth and computing power of mobile devices. To reduce the learning latency, local stochastic gradient methods and gradient compression can be applied, which however would decrease the convergence rate. To tackle such issues, in this paper, the trade-off between the convergence rate and the learning latency is taken into account. We first formulate an optimization problem to maximize the convergence rate under the given training latency constraint via jointly optimizing the batchsize, compression ratio, and spectrum allocation. Then, by decomposing the problem into two subproblems, an adaptive algorithm is proposed to obtain the optimal solution. The results show that batchsize and compression ratio should be selected according to the computing power and channel state information of the devices to improve the convergence rate. Finally, experimental results are presented to verify the effectiveness of the proposed algorithm.

With the development of the Internet of Underwater Things (IoUT), two critical problems have been prominent, i.e., the energy constraint of underwater devices and large demand for data collection. In this article, we introduce an autonomous underwater vehicle (AUV)-aided underwater acoustic sensor networks (UWSNs) to solve these problems. To improve the performance of UWSNs, we formulate an optimization problem to maximize the energy consumption utility, which is defined to balance the energy consumption and network throughput. To solve this optimization problem, we decompose it into four parts. First, due to the constraint of communication distance, we construct a cluster-based network and formulate the selection of cluster heads as a maximal clique problem (MCP). Second, the clustering algorithm is proposed. Third, we design a novel media access control (MAC) protocol to coordinate data transmission between AUV and cluster heads, among intracluster nodes, as well as among intercluster nodes. Finally, path planning of AUV is formulated as a traveling salesman problem to minimize AUV travel time. Based on the above analysis, two algorithms, namely, AUV-aided energy-efficient data collection (AEEDCO) and approximate AUV-aided energy-efficient data collection (AEEDCO-A), are developed accordingly. The simulation results show that the proposed algorithms perform well and are very promising in UWSNs with demand for large-scale communication, large system capacity, long-term monitoring, and high data traffic load.

The interference between filtered multi‐tone (FMT) symbols exists and becomes conspicuous especially in frequency selective fading channel, like an underwater acoustic channel. This leads to the necessity of channel estimation and equalisation. In this study, the authors consider two approaches to utilise the channel sparsity to improve FMT channel estimation performance and further reduce bit error rate in underwater acoustic communications. While the first studies FMT sparse channel estimation in the time domain and is based on received FMT signal and known transmitted FMT signal that is reconstructed by pilots, the second is based on matched filtering demodulated FMT symbols and pilots. Theoretical analysis is performed followed by simulation and experiment accordingly. It is found that the first method is able to successfully estimate the sparse channel while the estimation performance of the second method is unsatisfactory due to poor orthogonality between bases of the channel estimation matrix. Moreover, the lake experiment is carried out with the first method and its results prove that sparse channel estimation has better performance than least square channel estimation.

Seismic signals can be used for vehicle classification. However, this task becomes difficult as a result of various noises. Convolutional neural networks (CNNs) have been employed successfully in many fields as a result of its ability to learn low-/mid-/high-level features. This letter investigates the application of CNN to classify vehicles by means of the seismic trace that the geophone recorded. The study has two primary contributions. First, a deep CNN architecture for vehicle classification by seismic signal is proposed. Second, considering the similarities between speech recognition and vehicle classification based on seismic signal, log-scaled frequency cepstral coefficient (LFCC) matrix is proposed to extract features of seismic signals as the input of CNN. The data from DARPA's SensIt project, which contain seismic signals from two kinds of vehicles, are used to evaluate the method. By combining the proposed LFCC matrix and CNN architecture, the algorithm produces a state-of-the-art result compared with other methods.

In this paper, we consider a power beacon (PB) assisted two-way relaying network, where two single antenna energy constrained users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. The key aim of the paper is to design the energy beamforming vector and time split parameter for optimizing the system performance. In particular, two different design objectives are investigated, namely, max-min rate design and sum rate maximization design. Due to the non-convex nature of the optimization problems, the global optimal solutions are difficult to obtain. Instead, we propose an alternating optimization design framework where near optimal performance can be achieved through iterative optimization. In addition, to further reduce the computation complexity, closed-form suboptimal designs are provided. Simulation results are presented to validate the effectiveness of the proposed alternating optimization design and suboptimal design. The outcomes of the paper suggest that adopting the proposed energy beamforming vector at the PB can substantially boost the system performance. Also, the topology of the network has a significant impact on the achievable performance.

This paper investigates a power beacon (PB) assisted two-way relaying network, in which two single antenna energy constraint users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. Considering user fairness, we propose the max-min design that maximizes the minimum rate of the two users and investigate the corresponding optimal energy beamformer and time split. Due to the non-convex nature of the optimization problems, an alternating optimization design is proposed in which the optimal beamformer and time split can be solved via a low-complexity algorithm and convex optimization, respectively. Our results show that the proposed alternating optimization design yields near optimal performance, and implementing multiple antennas at the PB can significantly improve the system performance.

In this paper, we consider latency-minimization resource allocation for a multi-user mobile edge computation offloading (MECO) system. First, we develop a novel partial computation offloading model and then formulate the weighted-sum latency-minimization problem by optimally allocating the communication and computation resources. After that, the closed-form expression for the optimal data segmentation strategy is derived. Based on this result, we transform the original problem into a piecewise convex optimization problem and propose a sub-gradient algorithm to find the optimal resource allocation solution. Moreover, we analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution is devised. Finally, numerical results show that the partial computation offloading model can achieve a better performance than other two baseline schemes.

To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious communication link between a suspicious pair via proactive jamming. Assuming that the legitimate monitor can successfully overhear the suspicious information only when its achievable data rate is no smaller than that of the suspicious receiver, the key objective is to maximize the eavesdropping non-outage probability by joint design of the jamming power, receive and transmit beamformers at the legitimate monitor. Depending on the number of receive/transmit antennas implemented, i.e., single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output (MIMO), four different scenarios are investigated. For each scenario, the optimal jamming power is derived in a closed form and efficient algorithms are obtained for the optimal transmit/receive beamforming vectors. Moreover, low-complexity suboptimal beamforming schemes are proposed for the MIMO case. Our analytical findings demonstrate that by exploiting multiple antennas at the legitimate monitor, the eavesdropping non-outage probability can be significantly improved compared with the single-antenna case. In addition, the proposed suboptimal transmit zero-forcing scheme yields similar performance as the optimal scheme.

Underwater acoustic communications (UAC) feature frequency‐dependent signal attenuation, long propagation delay and doubly‐selective fading. Thus the design of reliable UAC protocols is challenging. On the other hand, cooperative relay communications, which have been extensively studied in terrestrial environments, are promising paradigms for reliable communications. However, their application to UAC has not been thoroughly explored. In this study, the authors will design an asynchronous relaying protocol to achieve reliable underwater communications. This new scheme accounts for and takes advantage of the unique characteristics of UAC channels. To avoid time synchronisation difficulty in UAC, and facilitate energy‐efficient relay processing, asynchronous amplify‐and‐forward relaying is adopted in the protocol. In addition, precoded orthogonal frequency division multiplexing is chosen to address the frequency selectivity issue in UAC, while collecting ample multipath diversity provided by the channel and enabled by the asynchronous relaying design. To demonstrate the performance of the protocol, the end‐to‐end signal‐to‐noise ratio is derived, the average pair‐wise error probability is evaluated and the maximum collectable diversity is also proven. Simulations and comparisons are presented to corroborate the analyses and design.

Full‐duplex communication (FDC) can potentially double the network capacity by allowing a device to transmit and receive simultaneously on the same frequency band. In this study, a novel resource allocation and user scheduling algorithm is proposed to maximise the network throughput for a cellular network with full‐duplex (FD) base stations (BSs). The authors consider that FDC is utilised at the BS with imperfect self‐interference (SI) cancellation while user devices only work in the traditional half‐duplex (HD) way. In addition, to potentially cancel co‐channel interference caused by other users, the opportunistic interference cancellation (OIC) technique is applied at user side. Since FDC does not always perform better than HD due to residual SI (RSI), a joint mode selection, user scheduling, and channel allocation problem is formulated to maximise the system throughput. The optimisation problem is non‐convex and NP‐hard, thereby a suboptimal heuristic algorithm with low computational complexity is proposed. Numerical results demonstrate that user diversity gain, FD gain, and OIC gain can be achieved by the proposed algorithm, respectively. The performance of FDC depends on the intensity of RSI and the distribution of user devices.

Electronic countermeasure ECM attack has been an emerging threat to radar network in recent years. It is necessary to design a secured radar network against ECM attack. In this paper, we prove that the radar network with conventional measurement fusion schemes is insecure to deceptive ECM DECM attack. Then, a new measurement fusion scheme is proposed, which shows better security performance when DECM attack happens. Numerical simulations are presented to demonstrate the effectiveness of the proposed measurement fusion scheme. Copyright © 2016 John Wiley & Sons, Ltd.

In this study, a distributed scheme based on the Nash bargaining model is designed to coordinate co‐channel interference for small‐cell networks. The authors consider a scenario that resource blocks can be reused among different small cells. Different to existing works where resource allocation is conducted at the base stations, they propose the scheme where user initialises resource bargaining request to the serving base station once its quality‐of‐service cannot be satisfied because of the severe co‐channel interference from other users. Since the general bargaining problem is a non‐linear integer optimisation, the genetic algorithm is utilised to solve it. They also develop a low‐complexity bargaining model which only takes into account the strongest co‐channel interference. Simulation results show that the proposed distributed scheme can effectively reduce the outage probability of users and improve the system throughput. In addition, the proposed low‐complexity bargaining solution can achieve a close performance to the genetic algorithm‐based solution.

Vehicular ad hoc networks (VANETs) have stimulated interest in both academic and industry settings because, once deployed, they would bring a new driving experience to drivers. However, communicating in an open-access environment makes security and privacy issues a real challenge, which may affect the large-scale deployment of VANETs. Researchers have proposed many solutions to these issues. We start this paper by providing background information of VANETs and classifying security threats that challenge VANETs. After clarifying the requirements that the proposed solutions to security and privacy problems in VANETs should meet, on the one hand, we present the general secure process and point out authentication methods involved in these processes. Detailed survey of these authentication algorithms followed by discussions comes afterward. On the other hand, privacy preserving methods are reviewed, and the tradeoff between security and privacy is discussed. Finally, we provide an outlook on how to detect and revoke malicious nodes more efficiently and challenges that have yet been solved.

In underwater acoustic communications (UAC), frequency-dependent signal attenuation, long propagation delay and doubly-selective fading channels render reliable communications a challenging problem, especially at long distances. To enhance reliability and to extend range, relay communications have been extensively studied in terrestrial environments. However, their application to UAC has not been thoroughly explored. In this paper, we analyze the capacity of relay-aided (RA- )UAC. The result shows a prominent capacity increase in RA-UAC systems, when compared with traditional direct-link UAC. In addition, effects of various system parameters on capacity are also evaluated. These parameters include source-to-destination distance, transmit power allocation and relay location. To realize the benefits of RA-UAC, special considerations are to be taken in practical RA-UAC system designs. To account for and to take advantage of the unique characteristics of UAC channels, we develop a practical asynchronous amplify-and-forward (AF) relay system for UAC. To collect the ample multipath energy and diversity enabled by this relaying protocol, we also employ the precoded orthogonal frequency division multiplexing (OFDM) as the basic physical layer module. Our system resolves both the time synchronization difficulty and frequency selectivity of UAC. Simulations and comparisons are presented to verify our analysis and design.

In this paper, we compare the battery power efficiencies of various pulse-based modulations widely adopted for their low complexity. Taking into account circuit modules and battery imperfectness, we establish simple closed-form analytical formulas which can be used to conveniently determine the relative preference between arbitrary pulse-based modulation pairs in terms of their actual average battery energy consumption.

Coherent communications over doubly-selective fading channels are attracting increasing research interests because of the performance advantage over their noncoherent counterparts. In this paper, we use a simple windowing and dewindowing technique to improve the accuracy of an existing basis expansion model (BEM) and develop a windowed least-squares (WLS) estimator for doubly-selective fading channels. We also design the optimum pilot pattern for the WLS estimator. Our designs can considerably improve the channel estimation performance. Simulations are provided to corroborate our theoretical analysis.

Sensor nodes in wireless sensor networks (WSNs) are often expected to operate on batteries for a long period of time. Battery power efficiency (BPE) is therefore a critical factor dictating the lifetime of WSNs. In this paper, we aim to select the appropriate modulation scheme from a battery power efficiency perspective. Pulse position modulation (PPM) and on-off keying (OOK), as low-complexity pulse-based modulation schemes, are used for a case study of our methodology. The analysis is based on a general model that integrates typical WSN transmission and reception modules with a realistic nonlinear battery model. We first present the quantitative comparison results under general system design criteria. Then, we illustrate the comparisons with theoretical and numerical results under the bit error rate (BER) system design criterion.

Sensor nodes in wireless sensor networks (WSNs) are often expected to operate on batteries for a long period of time. Battery power-efficiency is a critical factor dictating the lifetime of WSNs. In this paper, we compare two pulse-based modulations, namely pulse position modulation (PPM) and on-off keying (OOK), both of which are suitable for WSNs due to their low complexity transceivers. The comparison is based on a general model that integrates typical WSN transmission and reception modules with realistic nonlinear battery models. We analyze and compare the battery power-efficiency of PPM and OOK using coherent detection, and with bit error rate (BER) and cutoff rate criteria. Our results reveal that in sparse WSNs, PPM is more battery power-efficient. In dense WSNs, OOK outperforms PPM. In addition, the battery power-efficiency of OOK increases as the required cutoff rate decreases.