This paper addresses the challenge of energy-constrained maritime monitoring networks by proposing an unmanned aerial vehicle (UAV)-enabled integrated sensing, communication, powering and backhaul transmission scheme with a tailored time-division duplex frame structure. Within each time slot, the UAV sequentially implements sensing, wireless charging and uplink receiving with buoys, and lastly forwards part of collected data to the central ship via backhaul links. Considering the tight coupling among these functions, we jointly optimize time allocation, UAV trajectory, UAV-buoy association, and power scheduling to maximize the performance of data collection, with the practical consideration of sea clutter effects during UAV sensing. A novel optimization framework combining alternating optimization, quadratic transform and augmented first-order Taylor approximation is developed, which demonstrates good convergence behavior and robustness. Simulation results show that under sensing quality-of-service constraint, buoys are able to achieve an average data rate over 22 bps/Hz using around 2 mW harvested power per active time slot, validating the scheme’s effectiveness for open-sea monitoring. Additionally, it is found that under the influence of sea clutters, the optimal UAV trajectory always keeps a certain distance with buoys to strike a balance between sensing and other multi-functional transmissions.

In this work, we explore energy-efficient covert underwater acoustic backscatter communications (EC-UABCom) within the framework of the Internet of Underwater Things (IoUT). Specifically, a passive buoy node covertly transmits acoustic information passively to a maritime receiver by reflecting incident acoustic carrier signals from an autonomous underwater vehicle (AUV) transmitter. Simultaneously, the system exploits the uncertainty of underwater noise to mask the covert backscatter information, thereby evading detection by a submarine warden. To optimize the covert strategy, we first derive the warden’s optimal power-detection threshold that minimizes the detection error probability, accounting for underwater noise uncertainty. In response to this optimal detection strategy, we propose an energy-efficient covert policy by jointly optimizing the AUV’s transmit power and the buoy’s reflection coefficient to meet the covertness constraint. We then conduct a performance analysis, providing a closed-form expression for the expected detection error probability at the warden and outage probability at the receiver, thereby revealing their inherent trade-off. Numerical simulations validate the effectiveness of our approach compared to the state-of-the-art methods, demonstrating that higher transmit power and reflection coefficient degrade covertness while improving covert rate performance.

Sea surface temperature (SST) prediction is crucial for understanding global climate and marine ecosystems, and its anomalies can lead to extreme weather events. SST exhibits complex non-stationary over natural spatio-temporal processes. However, most of the existing deep learning methods for SST prediction only extract non-stationary features through the simple state transitions of classic CNNs or RNNs, which are too simplistic to capture higher-order non-stationary trends in complex SST sequences. Therefore, we propose a DSNet_SST network, aiming to enhance the extraction of non-stationary information from the spatio-temporal SST evolution. It incorporates two parallel modules: one for capturing high-order temporal non-stationarity based on stacked memory in memory (MIM) blocks and the other one for extracting spatial correlation non-stationarity by multiscale difference operation. A third module adaptively integrates these features to improve the accuracy and stability of SST prediction. The experimental results, relying on the OISST data obtained from both remote sensing satellites and in situ platforms, demonstrate the advantages of the proposed DSNet_SST over baseline methods in terms of SST prediction accuracy.

Considering the critical issue of interference management in cell-free massive MIMO with underlaid device-todevice (CF-mMIMO-D2D) maritime systems, in this paper, we propose a novel approach, namely CFD-HetGNN, utilizing the heterogeneous graph neural network (HetGNN) to deal with the joint optimization of beamforming and power allocation for both the CF-mMIMO users and D2D pairs. By modeling the CF-mMIMO-D2D as a heterogeneous graph, we take advantage of the HetGNN to capture the complex interactions among access points, mobile stations and D2D pairs, enabling an efficient solution of interference mitigation. Simulation results demonstrate that the proposed CFD-HetGNN significantly improves the spectral efficiency and achieves superior interference control compared to the traditional approach of matched filtering with equal power allocation (MF-EPA) as well as the learning-assisted methods of convolutional neural network (CNN), deep neural network (DNN) and long short-term memory (LSTM). Additionally, the CFD-HetGNN possess the robust scalability, making it applicable to the large-scale networks with low complexity.

With line-of-sight mode deployment and fast response, unmanned aerial vehicle (UAV), equipped with the cutting-edge integrated sensing and communication (ISAC) technique, is poised to deliver high-quality communication and sensing services in maritime emergency scenarios. In practice, however, the real-time transmission of ISAC signals at the UAV side cannot be realized unless the reliable wireless fronthaul link between the terrestrial base station and UAV are available. This paper proposes a multicarrier-division duplex based joint fronthaul-access scheme, where mutually orthogonal subcarrier sets are leveraged to simultaneously support four types of fronthaul/access transmissions. In order to maximize the end-to-end communication rate while maintaining an adequate sensing quality-of-service (QoS) in such a complex scheme, the UAV trajectory, subcarrier assignment and power allocation are jointly optimized. The overall optimization process is designed in two stages. As the emergency area is usually far away from the coast, the optimal initial operating position for the UAV is first found. Once the UAV passes the initial operating position, the UAV’s trajectory and resource allocation are optimized during the mission period to maximize the end-to-end communication rate under the constraint of minimum sensing QoS. Simulation results demonstrate the effectiveness of the proposed scheme in dealing with the joint fronthaul-access optimization problem in maritime ISAC networks, offering the advantages over benchmark schemes.

To liberate indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks from wired fronthaul, this paper proposes a multicarrier-division duplex (MDD)-enabled two-tier terahertz (THz) fronthaul scheme. In our scheme, two layers of fronthaul links rely on the mutually orthogonal subcarrier sets in the same THz band, while access links are implemented over sub-6G band. However, the proposed scheme leads to a complicated mixed-integer nonconvex optimization problem incorporating access point (AP) clustering, device selection, the assignment of subcarrier sets and the resource allocation at both the central processing unit (CPU) and APs. Hence, in order to address the formulated problem, we first resort to the low-complexity but efficient heuristic methods thereby relaxing the involved binary variables. Then, the overall end-to-end optimization is implemented by iteratively optimizing the assignment of subcarrier sets and the number of AP clusters. Furthermore, an advanced MDD frame structure consisting of three parallel data streams is tailored for the proposed scheme. Simulation results demonstrate the effectiveness of the proposed dynamic AP clustering approach in dealing with the networks of varying sizes. Moreover, benefiting from the well-designed frame structure, MDD is capable of outperforming TDD in the two-tier fronthaul networks. Additionally, the effect of the THz bandwidth on system performance is analyzed, and it is shown that empowered by sufficient bandwidth, our proposed two-tier fully-wireless fronthaul scheme can achieve a comparable performance to the fiber-optic based systems. Finally, the superiority of the proposed MDD-enabled fronthaul scheme is verified in a practical scenario with realistic ray-tracing simulations.

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.

In order to maximize the spectral efficiency (SE) in multicarrier-division duplex (MDD) enabled cell-free massive MIMO (CF-mMIMO), a heterogeneous graph neural network (HGNN), referred to as CF-HGNN, is specifically introduced to optimize the power allocation (PA). To efficiently manage the interference invoked, a meta-path based mechanism is applied in CF-HGNN to enable individual access point (AP) and mobile station (MS) nodes to aggregate information from the interfering and communication paths with different priorities during message passing. Moreover, the proposed CF-HGNN employs the adaptive node embedding layer and adaptive output layer to make it scalable to the various numbers of APs, MSs and subcarriers. For comparison, a quadratic transform and successive convex approximation (QT-SCA) algorithm is proposed to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99% of the SE achievable by QT-SCA but using only 10−4 times of its operation time, and it can outperform the conventional learning-based and greedy unfair methods in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user-centric clustering.

Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at the BS for uplink grant-free access for the massive machine type communication (mMTC) with limited channel resources. To enhance the overloading performance of the BS, we develop a general framework for the synergistic amalgamation of the spatial division multiple access (SDMA) technique with the CS-based grant-free NOMA. We derive a closed-form statistical beamforming and a dynamic beamforming scheme for the inter-cluster interference suppression when applying SDMA. Based on this, we further develop a joint adaptive beamforming and subspace pursuit (J-ABF-SP) algorithm for the multiuser detection and data recovery, with a novel sparsity level decision method without the accurate knowledge of the noise level. To further improve the data recovery performance, we propose an interference cancellation-based J-ABF-SP scheme (J-ABF-SP-IC) by using the initial signal estimates generated from the J-ABF-SP algorithm. Illustrative simulations verify the superior user detection and signal recovery performance of our proposed algorithms in comparison with existing CS-based grant-free NOMA techniques.

Long-term chlorophyll-a (Chl-a) prediction has the potential to provide an early warning of red tide, and support fishery management and marine ecosystem health. The existing learning-based Chl-a prediction methods mostly predict a single point or multiple points with monitoring data. However, the monitoring data are subject to sparse sampling and difficult to be measured in a large-scale and synchronous way. Moreover, the advanced learning-based models for point Chl-a prediction, such as long short-term memory (LSTM) and convolutional neural network (CNN)-LSTM, are unable to fully mining the spatio-temporal correlation of Chl-a variations. Therefore, by using the satellite remote sensing data with extensive coverage, we design a framework, namely Ca-STANet, to simultaneously predict the Chl-a of all the locations in a large-scale area from the perspective of spatio-temporal field. Specifically in our method, the original data are firstly divided into multiple sub-regions to capture the spatial heterogeneity of large-scale area. Then, two modules are respectively operated to mine the spatial correlation and long-term dependency features. Finally, the outputs from the two modules are integrated by a fusion module to fully mine the spatio-temporal correlations, which are exploited to attain the final Chl-a prediction. In this paper, the proposed Ca-STANet is comprehensively evaluated and compared with the legacy methods based on the OC-CCI Chl-a 5.0 data of the Bohai Sea. The results demonstrate that the proposed Ca-STANet is highly effective for Chl-a prediction and achieves higher prediction accuracy than the baseline methods. Moreover, as the OC-CCI Chl-a 5.0 data have many missing areas, we introduce DINEOF method to fill the data gaps before using them for prediction.

Changes in oceanic variables, such as sea surface temperature (SST) and chlorophyll-a (Chl-a), have important implications for marine ecosystems and global climate change. The deep learning methods relying on convolutional neural networks can be employed to extract the spatial correlation for the prediction of oceanic variables. However, these methods are inflexible in the cases where some regions, e.g., land and islands, are invalid for the prediction of oceanic variables. By contrast, the graph convolutional network (GCN) is capable of capturing the large-scale spatial dependency existing in the irregular data. Owing to this, in this paper, we propose a GCN-based method for the prediction of oceanic variables, including SST and Chl-a, with high accuracy, which is referred to as OVPGCN. The proposed OVPGCN consists of three modules aiming to fully extract the spatial correlation and temporal dependency via modeling the multi-characteristics of the spatio-temporal dynamic evolution. In particular, three modules are implemented to extract the stationary and non-stationary variations in the recent spatio-temporal sequences, the spatial differences between different sites, and the periodic features in historical data, respectively. The well-designed OVPGCN is applied to the monthly SST and Chl-a prediction in the Bohai Sea and the Northern South China Sea (NSCS). The performance demonstrates that the proposed OVPGCN is highly effective and enables to achieve much higher prediction accuracy than the state-of-the-art methods.