The performance analysis of maritime communication networks is frequently constrained by the widespread yet unrealistic assumption of a uniform spatial distribution of vessels. This paper presents a unified stochastic geometry framework that integrates high-fidelity physical channel models with nonhomogeneous vessel topologies to provide a more accurate analytical foundation. We first systematically demonstrate that the macroscopic geographical distribution of vessels is a dominant factor governing overall network performance, with significant performance variations observed across different deployment scenarios. Subsequently, to analyze the spatial origins of this performance, we introduce the coverage probability density function (Cpdf) as a novel metric. The Cpdf enables a finegrained spatial decomposition of the total coverage probability, thereby identifying key geographical areas that contribute most to network performance and quantifying the precise spatial impact of physical phenomena, such as multipath fading nulls. Our results affirm the primacy of realistic spatial modeling and provide a new analytical tool for the design and optimization of next-generation maritime networks.

Geo-Opportunistic Routing Networks (Geo-OR) within the Internet of Underwater Things (IoUT) are crucial for addressing the unique challenges of underwater communication environments. By leveraging geographic location data, these networks can opportunistically select forwarding nodes, a strategy that proves particularly effective in the dynamic and unpredictable nature of IoUT. However, uncertainty in forwarder selection and the inability to verify nodes’ claimed positions remain critical challenges. This paper investigates how uncertainty in next-hop selection arises in Geo-OR when viewed from the perspective of potential adversaries operating in a non-cooperative manner. By integrating an octree-based search strategy with an angle-constrained 3D localization algorithm, we accurately identify and locate “hot” nodes within Geo-OR. Geometric methods are employed to analyze vulnerable relay regions, pinpointing areas especially susceptible to malicious attacks. Based on this analysis, a location spoofing attack is proposed to disrupt legitimate node transmissions and degrade network performance. Simulation results reveal that the inherent uncertainties in Geo-OR expose the network to significant vulnerabilities, severely compromising throughput. This study highlights the critical weaknesses of Geo-OR, offering valuable insights into the most vulnerable regions and contributing to the design of targeted defense strategies to mitigate these risks.

Distributed Internet of Underwater Things (D-IoUT) has broad application prospects in marine monitoring, resource development, and security protection. However, the spatio-temporal uncertainty of underwater acoustic networks and the presence of multiple collision domains pose severe challenges to MAC-layer scheduling and interference management. To address these issues, this paper proposes an adaptive scheduling MAC protocol based on channel sensing and reinforcement decision-making (CSRD-ASMAC), aiming to provide an efficient and adaptive transmission scheduling scheme for D-IoUT. The protocol first introduces a hierarchical channel-state classification framework to accurately characterize the channel environment for each slot. Building on pre-divided basic slots, it integrates channel sensing and slot prediction into a reinforcement learning framework and employs a mixed-action exploration strategy to update Q-values, enabling each node to adaptively select the optimal transmission slot. Simulation results show that CSRD-ASMAC effectively improves overall network throughput in multi-collision domain environments.

In the complex, time-varying Internet of Underwater Things (IoUT), traditional hierarchical routing struggles to balance reliability, low delay, and energy efficiency due to measurement delays, routing voids and frequent transmission collisions. To address these challenges, we proposes HRT-TCOR, a high-reliability, time-reversal-based cross-layer opportunistic routing protocol that tightly integrates time reversal, reservation-based MAC, and opportunistic forwarding to collaboratively optimize the physical, data link, and network layers. First, lightweight beacons enable void sensing and preselection of candidate-forwarding sets (CFS). Next, a hybrid sender-receiver cooperation strategy forms the CFS, while a two-tier suppression mechanism eliminates redundant transmissions. Finally, pipelined handshakes merge reception acknowledgments with channel reservations to eliminate inter-hop idle gaps and ensure seamless end-to-end reliable delivery. Simulations show that, under both good and poor channel conditions, HRT-TCOR consistently achieves the highest packet delivery ratio, a lower delay, and competitive energy efficiency compared to other protocols.

The development of the Internet of Underwater Things (IoUT) is of great significance to marine scientific research, deep-sea exploration and interdisciplinary data fusion. However, the existing medium access control (MAC) protocols usually face serious network congestion and unfair channel resource allocation challenges in large-scale IoUT, resulting in a decline in the overall performance of the system. To address these problems, this paper proposes a dynamic slot management MAC protocol based on POMDP (P-DSM-MAC). The protocol integrates network load estimation, ACB scheme optimization, dynamic slot allocation, and the sensing and multiplexing of idle/collision sub-slots through slot division to achieve efficient network resource management and sub-slot contention collision control. Simulation results show that P-DSM-MAC is significantly superior to existing protocols in key performance indicators such as network throughput, delay, and sub-slot contention collision rate, providing a feasible solution for the intelligent and dynamic optimization of IoUT in the future.

Knowledge discovered from telecom data can facilitate proactive understanding of network dynamics and user behaviors, which in turn empowers service providers to optimize cellular traffic scheduling and resource allocation. Nevertheless, the telecom industry still heavily relies on manual expert intervention. Existing studies have been focused on exhaustively exploring the spatial-temporal correlations. However, they often overlook the underlying characteristics of cellular traffic, which are shaped by the sporadic and bursty nature of telecom services. Additionally, concept drift creates substantial obstacles to maintaining satisfactory accuracy in continuous cellular forecasting tasks. To resolve these problems, we put forward an online cellular traffic prediction method grounded in Multi-Grained Spatial-Temporal feature Complementarity (MGSTC). The proposed method is devised to achieve high-precision predictions in practical continuous forecasting scenarios. Concretely, MGSTC segments historical data into chunks and employs the coarse-grained temporal attention to offer a trend reference for the prediction horizon. Subsequently, fine-grained spatial attention is utilized to capture detailed correlations among network elements, which enables localized refinement of the established trend. The complementarity of these multi-grained spatial-temporal features facilitates the efficient transmission of valuable information. To accommodate continuous forecasting needs, we implement an online learning strategy that can detect concept drift in real-time and promptly switch to the appropriate parameter update stage. Experiments carried out on four real-world datasets demonstrate that MGSTC outperforms eleven state-of-the-art baselines consistently.

The Internet of Medical Things (IoMT) stimulates the development of intelligent medical applications. As mental disorders become a global problem, emotion recognition has received widespread attention, as it can contribute to more comprehensive mental health monitoring and psychological assessment. Physiological signal-based emotion-aware monitoring is a particularly promising application due to its noninvasive and objective data collection. Recently, multimodal emotion recognition has been enhanced with wireless body area network (WBAN) access to IoMT, where wireless medical sensors are interconnected and abundant signals are acquired conveniently. However, how to synthesize these multisource physiological signals to facilitate emotion recognition is a challenging problem due to their heterogeneity and interference. To solve this problem, we propose a real-time differential multimodal transformer (Diff-MT), where the main components are the differential hyperinformation extraction (DHE) module, the multimodal global cross-attention encoder (MGCE), and the difference-augmented feature fusion (DFF). Ultimately, we endow the system with emotional awareness and distribute the state to IoMT devices. Extensive experiments demonstrate that the proposed Diff-MT exhibits superior performance compared to existing methods on the WESAD and DEAP datasets and is appropriate for IoMT-based healthcare.

In this paper, a D-S evidence theory information fusion method is proposed along with the basic concept of D-S evidence theory and the synthesis law. By applying this information fusion method to the condition monitoring of generating units through simulation experiments, the real-time operating status of the units can be reflected from different aspects, and a comprehensive and correct understanding of the environment or characteristics can be derived to make more accurate and complete estimates and decisions, thus improving the robustness of the whole system. From the experimental results, it can be seen that the D-S evidence theory information fusion method effectively reduces the uncertainty of fault diagnosis and improves the credibility of fault state identification.

Due to the rapid expansion of the unmanned aerial vehicle (UAV) industry, numerous application scenarios for UAVs have emerged. When UAS fly at high altitude, especially when 5G base stations or terminals are carried by UAS operating in the 3.5 GHz frequency range, the out-of-band signals emitted can pose serious interference risks to radiolocation services using nearby frequencies. In this paper, we analyze in detail the analysis method of inter-system interference compatibility by combining the ITU-R proposal and the actual deployment parameters of UAS. Then the deterministic calculation and analysis of the neighboring frequency interference between UAS and ground radar systems is performed. The analysis results show that the strictest interference occurs when the interference signal from the UAS enters the main band of the ground-based radar. And when the UAS and the radar system are farther apart in frequency, the interference is less. And the interference caused by the UAS carrying the base station is greater than the interference caused by the UAS carrying the terminal.

Communication capabilities are a fundamental need to establish a future 6G integrated space-air-ground communication system. 6G computing networks must provide high-performance computing platforms with limited resources, and will in the future include network nodes such as constellation satellites and high-altitude platforms. This paper first assesses the need for an integrated air-space communication system and the main technical difficulties of networking. Then the space-ground integrated 6G computing architecture is explained. This is followed by an in-depth discussion of the system model, which includes a communication model, a computational model, and an energy model. Finally, we describe the FedDCO approach in detail and focus on the integrated optimization of computational offloading and airborne platform deployment in a multi-user scenario. Simulation results show that the FedDCO approach can significantly improve the model convergence speed and solve the airborne platform deployment and computational offloading problems.

Ultra-low latency is one of the key service performance supported by the fifth-generation mobile communication technology (5G) new radio (NR) network. The 5G NR physical layer latency is composed of several parts, and is affected by many factors. This paper constructs the 5G NR physical layer round trip latency model firstly, and identifies the latency parts that can be reduced through the optimization mechanism. Then the main factors and reasons that affect physical layer latency are analyzed, and quantitative estimation and analysis on the physical layer average latency is taken. Finally, some feasible research suggestions on further reducing the physical layer latency are provided.

Recently, human action recognition has been widely used in the fields of health monitoring, human-robot interaction, medical treatment, and sports. Due to the availability of various wearable devices on the market, we can easily access sensor data for human action recognition. However, it is still a challenge to capture minute action processes as well as extract spatio-temporal motion patterns from serial sensor data. Therefore, we propose a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM. The finer-grained spatial information in the sensor signals is extracted by hierarchical multi-scale convolution. The multi-channel feature fusion through adaptive channel feature fusion retains important information and improves model efficiency. We capture temporal context information by dynamic channel selection-LSTM based on the attention mechanism. Extensive experiments on the Opportunity and PAMAP2 public datasets show that our proposed model achieves competitive performance compared to several state-of-the-art approaches.

Ubiquitous Internet of Things (UIoT) is required a 3-D network that stretches from space to air to earth. It involves numerous network components, such as satellites, HAPs, UAVs, terrestrial cellular networks, data centers, terrestrial gateways, as well as sharing and openness among operators. Correspondingly, wireless spectrum sharing becomes essential to energy efficiency and, thus, has to be investigated for green UIoT. Meanwhile, both blockchain and sixth-generation mobile communication technology hybrid cloud are recently intriguing technologies, and the enormous potential of combining the two has grown in prominence. For the reason of dependability and security, these two technologies are proposed to apply to spectrum sharing among UIoT devices. Particularly the blockchain’s unique smart contract technology can well complete the spectrum sharing procedure, as verified by the numerical results from our simulation study.

This paper looks into the realization of digital twin (DT) technology in unmanned aerial vehicle (UAV) networks. We propose, in particular, a framework for DT-based UAV applications in which distinct jobs in the digital twin interact with UAVs in the physical world via task manager scheduling. Furthermore, we investigate the use of 3D mmWave Radar imaging on UAVs and apply it to the process of radio frequency (RF) characterizing. After that, the method of 3D ray-tracing is employed to accomplish channel modelling of UAVs, which reflects RF domain digital twin match. Finally, we present numerical results to demonstrate that our developed digital twin platform can provide accurate RF presentation of UAV and therefore accomplish smart operating and administration of the actual UAV network.

Passive sensing technology enables Wi-Fi-based human activity recognition (HAR), which has been widely noted in recent years. This letter presents a novel Wi-Fi-based class-incremental human activity recognition system that allows for the gradual addition of new activity categories. To the best of our knowledge, this is the first attempt to recognize all previously learned activities under the constraint of limited samples for both the original and newly added activity classes. It is challenging in 1) how to prevent catastrophic forgetting of old activities and 2) how to leverage as few samples as possible to accurately recognize new activities. Therefore, a phased training and update strategy is proposed to avoid the knowledge-forgetting issue. Furthermore, to alleviate the unsatisfactory performance problem caused by insufficient samples of new categories, we design an amplitude-phase enhanced convolution neural network, which integrates an attention mechanism and dual loss function to enhance the feature discrimination and the generalization capability of the model. Extensive experiments show that our system can operate with promising perceptual accuracy in different datasets.

Nowadays, operators worldwide deploy 5G on FR1 to deliver 5G services with higher capacity for initial 5G deployment. However, concerning coverage areas and indoor penetration, it is necessary to find the coverage layer for 5G in low band. Due to all accessible low bands are occupied by existing technologies, such as Long-Term Evolution (LTE). Thanks to Dynamic spectrum sharing (DSS) technology, which can ensure LTE and NR harmonious coexistence in the same frequency band at the same time. On the basis of a comprehensive study of LTE and NR physical layer, DSS technology is introduced and the problem of LTE and NR interference coexistence was analyzed based on an FDD system. Furthermore, a field trial was conducted to examine interference coexistence of 5G NR and 4G LTE under DSS. Results show that LTE neighbor cell has more impact on DSS cell because of LTE always-on signals. Finally, some suggestions on DSS deployment are gave to the mobile operators for network construction.

5G co-construction and sharing has been successfully carried out in China, and has become a key measure to improve the quality and reduce the cost of 5G mobile network. The demand of traditional voice service is the basic service of 5G network construction. In this paper, the voice service solutions under different 5G networking modes are discussed, and the VoLTE voice service capability under the shared networking mode of 5G NSA access network is deeply studied. Two technical solutions are proposed for VoLTE being carried in the 4G shared anchor cell of the contractor or in the home 4G cell. Two schemes are analyzed and compared. The 4G anchor point establishment and release process, service mobility, etc. are tested in the field, and the test results are given.

With the development of communication technology. Most 5G systems are TDD systems. Due to the existence of the atmospheric duct phenomenon, the uplink signal of the local 5G base station will be interfered by the downlink signal of the remote 5G base station. This interference has the characteristics of wide interference range, high frequency, and serious degree, which affects the uplink random access of users, handover process and uplink service time slot. This article will study how to identify the remote 5G base station causing interference and how to suppress interference.

Compared with the fourth generation (4G) technology, the fifth generation (5G) network possesses higher transmission rate, larger system capacity and lower transmission delay, which requires lager radio frequency (RF) bandwidth and more transport and receive channels to cater for the target requirement proposed by International Telecommunication Union (ITU). The power consumption of the base station has increased quickly and the operators worldwide are in sore need of corresponding energy saving solutions. In order to avoid the bad influences on network coverage or user experience, it is necessary to look for the balance between power saving effects and network performance when implementing any schemes. This article analyzes the power consumption model of 5G base stations firstly, and then introduces four basic power consumption strategies. Based on the field trial results, power conservation performances of each scheme are shown clearly. Finally, the technology development trends and challenges of 5G power conservation from the perspective of network operations are analysed.

Ultra dense networks are a promising technique to achieve increasing 1000 times data rate requirements of the fifth generation (5G) wireless communications. However, due to the ‘tidal effect’ of mobile Internet traffic, the energy efficiency of the 5G communication system decreases dramatically, especially in off-peak hour, such as midnight. The software-defined wireless networks (SDWN) architecture provides a solution to reduce the energy consumption via cells management. To facilitate wide usage of cells management in SDWN, we consider in this paper the problem of joint ultra dense cells management and resource allocation, with the objective to minimize the network power consumption while guaranteeing the quality of service requirements of users and the coverage rate requirement. The problem is formulated as a mixed-integer nonlinear programming problem. To deal with this problem, we utilize the sparse characteristics of the beamforming vector and the method of reweighted l1 norm to approximate l0 norm. Because the coverage rate requirement constraint is still non-convex, we propose an iterative algorithm to derive the lower bound of the problem, and then propose a heuristic iterative algorithm to obtain a practical solution. Simulation results confirm that minimizing the total network power consumption results in sparse network topologies, and some of the small cells are inactivated when possible. The performance of the proposed heuristic algorithm is only increased by 13.09% compared with the lower bound, while the target signal-to-interference-plus-noise ratio grow from 0 to 8 dB. And the proposed heuristic algorithm can achieve good performance compared with the conventional sparsity-based algorithm.

Previous work has reported that vertical sectorization can efficiently improve the capacity of LTE network using Active Antenna System (AAS) based on simulation results, however, its performance in real-world scenario still needs to be evaluated and verified for future deployment. Thus, this study conducts an evaluation of vertical sectorization in LTE field trial with single user. The field trial results show that the average downlink throughput of the single user is reduced compared to the one of traditional network, which results in the deterioration of user experience. Furthermore, we examine field data and antenna parameters, and verify that the co-frequency interference between the inner and outer cells is the main reason for average downlink throughput decrease in field trial. Obviously, without proper interference suppression or can-celation mechanisms, the vertical sectorization can not improve the user experience. Therefore, some practical suggestions on the deployment of vertical sectorization in LTE network are proposed.

Atmospheric optical wireless channel is a time-varying optical channel because of the effect of scintillation, the paper introduces rate compatible punctured turbo code (RCPTC) scheme to provide unequal error protection (UEP) for optical wireless communication. In order to use RCPTC over optical wireless channel, the paper firstly proposed a turbo decoder with iterative optical channel estimation for optical wireless channel. Simulations show that the proposed turbo decoder significantly improves the performance of the system. Based on the proposed turbo decoder, the paper compares several RCPTC schemes via simulations. In these RCPTC schemes, the non-uniformly punctured code schemes are compared with uniformly punctured code scheme. Moreover, the systematic and non-systematic puncturing schemes are both discussed.