To overcome the 180° coverage limitation of conventional reflective Reconfigurable Intelligent Surfaces (RIS) in challenging Non-Line-of-Sight (NLoS) environments like underground coal mines, this paper proposes the deployment of a Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The STAR-RIS achieves full 360° signal coverage, effectively addressing the spatial constraints of complex tunnel topologies. Furthermore, we introduce a “Position-Flexible” approach, where the entire panel’s location is dynamically adjusted to maximize the average sum-rate across wideband OFDM subcarriers. By exploiting frequency diversity, the proposed system effectively combats the severe frequency-selective fading inherent in multipath-rich mine tunnels. This holistic movement strategy is specifically designed to enhance hardware reliability in harsh, dust-prone mining conditions by mitigating failure risks associated with complex element-wise mechanical actuation. We formulate a joint optimization problem involving broadband active beamforming, passive phase shifts, and the STAR-RIS coordinates. To solve this non-convex problem, an Alternating Optimization (AO) algorithm is developed. Specifically, the STAR-RIS location is optimized via Projected Gradient Ascent (PGA), while the beamforming and phase-shift coefficients are refined using Successive Convex Approximation (SCA) and Semidefinite Relaxation (SDR). Simulation results confirm that the proposed system significantly improves the sum rate, validating its effectiveness for robust underground wireless connectivity.

This paper addresses the challenges of image transmission in satellite communication networks, where bandwidth constraints, high interference, and latency issues significantly limit conventional transmission methods. We propose a novel semantic communication framework that adapts to various computational capabilities of receiving terminals in Internet of Everything (IoE). Our approach leverages the Swin Transformer V2 architecture to extract and transmit task-relevant semantic features rather than raw image data, significantly reducing bandwidth requirements while maintaining high reconstruction quality. The proposed system dynamically adjusts its encoding and decoding processes based on receiver computational capacities, enabling efficient image transmission to heterogeneous terminals ranging from high-performance stations to resource-constrained devices. Extensive experiments on various datasets demonstrate that our framework outperforms conventional JPEG+LDPC schemes and state-of-the-art deep learning-based approaches in terms of both PSNR performance and semantic communication utility across various signal-to-noise ratios. The framework shows particular robustness in low-SNR and low-CBR environments, addressing the “efficiency-compatibility” dilemma in resource-constrained satellite communications.

Federated learning (FL) has been considered as a promising way to train distributed wireless systems in a privacy-preserving manner. However, the significant communications overheads caused by uploading local parameters and the potential unreliability of wireless links emerged as one of the bottlenecks of FL. To address this challenge, this paper investigates a reconfigurable intelligent surface (RIS)-assisted sparse FL network, where the RIS is utilized for wireless transmission reliability enhancement, and the sparsification operation is used to reduce the communications overheads. Considering that the wireless transmissions of the FL uploads are carried by finite blocklength (FBL) codes, wefor the first timeinvestigate the convergence of sparse FL while taking into account both the FBL decoding errors and FL sparsification errors. Following such a model, a novel joint learning and communication design framework is provided. In particular, an optimization problem is formulated to minimize the impacts of the above errors on the convergence via jointly determining the coding rate, transmit power, and RIS phase shift. To tackle the formulated non-convex problem, a block coordinate descent (BCD)-based algorithm is proposed, which decomposes the problem into two sub-ones and solves them alternately. On the one hand, for the resource allocation sub-problem, we derive a closed-form expression of optimal coding rate with respect to power that drastically reduces the optimization problem dimension, and shows the convexity of the resulting power allocation problem. For the RIS phase shift design sub-problem, on the other hand, a trust-region based linear approximation is used, along with problem transformations and tight successive convex approximations, to derive a highly effective iterative algorithm based on the closed-form expression for each variable. The entire proposed iterative algorithm converges efficiently to a suboptimal solution. Then, we extend the proposed algorithm to the imperfect channel state information (CSI) scenarios by using second-order Taylor approximation. Numerical results demonstrate that the proposed design significantly improves the FL performance in comparison to benchmark schemes.

High-altitude airships (HAS) and unmanned aerial vehicles (UAVs) equipped with Multiaccess Edge Computing (MEC) servers have emerged as promising aerial MEC nodes for providing task offloading (TO) services to intelligent mobile devices (IMDs) in post-disaster scenarios. HAS offers robust computing and energy resources, while UAVs provide flexible, low-altitude coverage for rapid deployment. However, direct task offloading from IMDs to HAS often leads to task failures due to high transmission delays. UAVs with limited onboard resources require to minimize resource waste. Additionally, IMDs in sparse areas face insufficient TO services due to unfair UAV coverage. This paper defines these challenges as a joint optimization problem involving TO, RA, and UAV coverage fairness. It proposes a cooperative aerial Multiaccess Edge Computing (AMEC) framework integrating HAS and UAVs to address the issue. Within this framework, a hybrid TO scheme is first developed to mitigate the high transmission delay between IMDs and HAS. Second, a Distance, Resource, Urgency-based Decision Mechanism (DRUDM) is designed to enhance the accuracy of UAVs in selecting target IMDs for TO services. Third, a Coverage Fairness Guarantee (CFG) strategy is proposed to optimize UAV flight trajectories, ensuring IMDs in sparse areas receive fair TO services. Finally, the joint optimization problem is modeled as a Multi-Agent Partially Observable Markov Decision Process (MA-POMDP), and a DRUDM–CFG algorithm is presented to efficiently solve this complex non convex optimization problem. Experimental results demonstrate that the proposed algorithm outperforms other compared algorithms in task completion rate and average delay, benefiting from the DRUDM mechanism. Meanwhile, the CFG strategy effectively improves TO service fairness for IMDs in sparse areas.

In the Internet of Autonomous Vehicles (IoAV), task offloading is crucial for managing tasks that require extensive computing power to guarantee vehicle safety under different weather scenarios. However, extreme weather events can lead to infrastructure damage and network disruptions, significantly increasing the computational demands of autonomous vehicles. These vehicles require additional computing resources to navigate complex road conditions and risks, all while facing a high degree of uncertainty, such as fluctuations in vehicle resource utilization and task workloads. To address these challenges, a new and lightweight task offloading decision framework, named 2FDP-BRL, has been first proposed in this paper. This framework not only considers the fast response time required for autonomous driving, but also considers the resource shortage and offloading uncertainty caused by extreme weather. Therefore, we introduce the dynamic pricing idea and the Interval Type-2 Fuzzy Inference System (IT2FIS) utilizing broad reinforcement learning to deal with various dynamic uncertainties in the IoAV under extreme weather. For the authenticity of experimental results, we utilize the VISSIM platform to collect experimental data and conduct simulations. Moreover, to accurately simulate extreme weather scenarios, we also account for the variability of infrastructure and road elements, including reduced transmission rates and decreased efficiency in executing tasks. Furthermore, to enhance the realism of the simulation, we incorporate historical weather data from NOAA for Shenyang in 2024 to model dynamic uncertainties under extreme weather conditions and conduct comparative experimental analyses focusing on task completion rates. Finally, the proposed framework was implemented on both a local setup and the Huawei Atlas 200I DK A2 device, illustrating its efficacy design.

In intelligent transportation systems, the ability to make precise and efficient lane-changing overtaking decisions is essential for improving traffic flow, safety, and overall efficiency. However, the coexistence of both connected and non-connected vehicles, driven by the high cost of full deployment and the incomplete global adoption of standardized communication standards, has led to the emergence of Mixed Connected and Connectionless Vehicles (MCCV) scenarios. These scenarios complicate lane-changing overtaking decisions, as the unpredictability of connectionless vehicles, particularly in dense traffic, poses significant challenges and increases safety risks. Furthermore, incorporating fairness into decision-making is vital to ensure equitable treatment of all vehicles, which is key to improving road safety and traffic efficiency. To address these challenges, we propose a fairness-aware overtaking decision optimization method for MCCV scenarios, which aims to enhance fairness while improving safety and efficiency in vehicle decision-making. First, a Bayesian network-based fairness assessment method is introduced to quantify fairness under limited data conditions by modeling the probabilistic relationships between vehicle behaviors and fairness outcomes. Second, we develop a left-lane availability detection mechanism based on adaptive maneuver tree search and a fast-lane speed recommendation mechanism grounded in traffic flow analysis. These mechanisms enhance compliance with traffic regulations and improve efficiency by dynamically assessing lane conditions and providing a real-time speed recommendation based on traffic density and flow. Finally, we incorporate a K-Nearest Neighbor (KNN)-enhanced deep reinforcement learning approach, which integrates a parameterized dueling deep recurrent Q-network with KNN-enhanced experience replay. This approach effectively copes with rare but critical driving conditions, such as unpredictable vehicle behaviors or sudden traffic dynamics, improving decision-making reliability in various traffic scenarios. Extensive simulations demonstrate that the proposed method significantly enhances the fairness, efficiency, and safety of lane-changing overtaking decisions in MCCV scenarios, effectively addressing the unpredictability of mixed-vehicle interactions.

Large language models (LLMs) are revolutionizing various fields due to their powerful generation capabilities. However, their immense computational complexity poses significant challenges in resource consumption, inference latency, and data privacy for traditional cloud-centric deployments. Edge artificial intelligence (Edge-AI) offers promising LLMs deployment solutions by leveraging distributed resources at the network edge. However, existing approaches struggle to adapt to dynamic workloads and efficiently utilize heterogeneous resources in Mobile Edge Computing (MEC) environments. This paper proposes aDynamicBatching andAdaptivePartitioning (DyBAP) scheme for LLMs deployment, which utilizes ubiquitous geo-distributed resources via end-edge-cloud collaboration. Firstly, we formulate a collaboration deployment optimization problem to minimize inference latency and resource usage under heterogeneous resource and user requirements for latency and accuracy constraints, which is NP-hard. Secondly, to solve this, we develop a dynamic batch fusion optimization algorithm that optimizes the batch size of inference by utilizing the parallel processing power of computing units to balance the latency and resource usage. A block-aware partition optimization algorithm based on multi-agent reinforcement learning (MARL) is proposed for efficient transformer block allocation, integrating mobility awareness for optimal partitioning across dynamic network environments. Simulation results demonstrate the superiority of DyBAP over other benchmarks, reducing inference latency by 17.94% and saving 11.12% in memory resource consumption compared to the end-edge-cloud collaboration approaches.

Person re-identification (Re-ID) aims to match individuals across different cameras, a task complicated by variations in camera positions, resolutions, and lighting conditions. While supervised training improves Re-ID model accuracy, it requires significant annotation efforts. Unsupervised domain adaptation (UDA) methods address this by leveraging unlabeled target domain data but often fail to fully utilize multiple source domains and are constrained by computational resources. This article introduces a lightweight multi-source domain adaptation method for person Re-ID that combines meta-learning with pseudo-label-based UDA. By employing Self-paced Meta-Learning (SpML) and style enhancement techniques, the model learns domain-invariant knowledge from easy to difficult source domains, enhancing pseudo-label quality during adaptation. Our approach, based on an omni-scale feature extraction network using deep separable convolution, combines global and partial feature branches to capture richer pedestrian features. Experiments on public and real-world datasets demonstrate that our method achieves competitive performance with significantly fewer parameters and Floating Point Operations (FLOPs) compared to state-of-the-art models, proving its effectiveness and practicality.

With the rapid development of artificial intelligence and the dramatic growth of communication services, the sixth-generation (6G) wireless network needs to handle communication tasks more flexibly and efficiently, significantly exacerbating the challenge of resource allocation. For the access network scenarios in 6G networks, the existing single-layer reinforcement learning resource allocation algorithms are hard to satisfy the diverse demands of users due to the complex and variable state space. Therefore, we propose a reinforcement learning-based two-timescale resource allocation scheme, aiming to jointly enhance the quality of service and system resource utilization. The proposed method comprises an upper-layer controller that allocates network resources to lower-layer controllers on a large time scale. Then, lower-layer controllers refine the resources based on user service types on a smaller time scale. To implement the proposed two-timescale allocation scheme, we propose a two-layer reinforcement learning framework consisting of a deep deterministic policy gradient (DDPG) and a dueling deep Q network (Dueling-DQN). Furthermore, recognizing that coupling multiple reinforcement learning processes may slow down algorithm convergence, we employ asynchronous training, transfer learning, and prediction-based action space simplification to expedite the model’s convergence speed. Finally, we build a prototyping network to verify the performance of the proposed small-timescale and the large-timescale allocation algorithms. Our proposed scheme demonstrates significant improvements in both resource utilization and quality of service compared to existing schemes.

Vehicular Edge Computing (VEC) leverages promising technologies, namely the vehicle-to-vehicle (V2V) computation offloading approach and edge service caching, to address latency-sensitive tasks. The V2V offloading method efficiently harnesses idle resources from neighboring vehicles. Edge service caching facilitates the offloading task through pre-caching pertinent service data. However, formulating an efficient caching mechanism to support V2V offloading poses significant challenges, given the dynamic vehicle environment, varying computational resources, and limited caching resources of Roadside Units (RSUs). This paper introduces a collaborative caching and offloading (CACO) scheme. First, to mitigate resource wastage caused by inter-vehicle communication interruptions, we employ Generative Adversarial Network (GAN) for trajectory prediction. This process generates a relationship matrix, predicting the stability of inter-vehicle link connections to assist in V2V offloading decisions. Second, to circumvent redundant uploads and computations for recurring offloading tasks, we analyze the popularity of historical offloading tasks using the Page-Hinkley test (PHT) technique, caching frequently offloaded tasks to reduce the processing latency of offloading tasks. Subsequently, a matching scheme for caching and offloading contents is devised. Finally, the Deep Reinforcement Learning (DRL) algorithm is employed to train the offloading strategy. Results from extensive experiments substantiate that CACO attains superior performance in both system computational latency and offloading success rate.

Integrated sensing and communication (ISAC) is a key technology for future 6G networks. Most existing studies focus on monostatic and/or bistatic setups with limited coverage and capabilities. Networked ISAC systems with distributed base stations (BSs) can overcome these limitations. Moreover, movable antenna (MA) architectures offer improved ISAC performance over fixed-position antennas (FPAs) by enabling adaptable antenna movement. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and position configuration of transmit/receive MA towards maximizing the sum rate for both downlink (DL) and uplink (UL) users. The optimization problem is highly difficult due to the unique channel model derived from the position coefficient of the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying the MA framework in the networked ISAC system.

This paper investigates the two-timescale design for access point (AP) mode selection and power allocation to realize the full potential of the cooperative bi-static ISAC network with low system overhead, where the beamforming at the APs is adapted to the rapidly-changing instantaneous channel state information (CSI), while the AP mode selection and power allocation are adapted to the slowly-changing statistical CSI. Firstly, the minimum mean square error (MMSE) estimator is applied to estimate the channels between the APs and the channels between the APs and the user equipments (UEs). Then we adopt the low-complexity maximum ratio transmission (MRT) beamforming and maximum ratio combining (MRC) detector, and derive the closed-form expressions of the ergodic rate of the UEs and the sensing signal-to-interference-plus-noise-ratio (SINR). A non-convex mix integer optimization problem is formulated to maximize the minimum sensing SINR under the communication quality of service (QoS) constraints. McCormick envelope relaxation and successive convex approximation (SCA) techniques are applied to solve the challenging non-convex mix integer optimization problem. Extensive simulation results demonstrate the analytical accuracy of the closed-form expressions and validate the convergence and effectiveness of the proposed AP mode selection and power allocation scheme.

In this paper, we model the minimum achievable throughput within a transmission block of restricted duration and aim to maximize it in movable antenna (MA)-enabled multiuser downlink communications. Particularly, we account for the antenna movement delay caused by mechanical movement, which has not been fully considered in previous studies, and reveal the trade-off between the delay and signal-to-interference-plus-noise ratio at users. To this end, we first consider a single-user setup to analyze the necessity of antenna movement. By quantizing the virtual angles of arrival, we derive the requisite region size for antenna moving, design the initial MA position, and elucidate the relationship between quantization resolution and moving region size. Furthermore, an efficient algorithm is developed to optimize MA position via successive convex approximation, which is subsequently extended to the general multiuser setup. Numerical results demonstrate that the proposed algorithms outperform fixed-position antenna schemes and existing ones without consideration of movement delay. Additionally, our algorithms exhibit excellent adaptability and stability across various transmission block durations and moving region sizes, and are robust to different antenna moving speeds. This allows the hardware cost of MA-aided systems to be reduced by employing low rotational speed motors.

The long propagation delay makes the delay characteristics of data transmission in low Earth orbit (LEO) satellite networks limited by retransmission. Therefore, this paper focuses on the retransmission delay characteristics through analyzing the local delay, defined as the mean times required for the serving satellite successfully transmitting the message to the ground user. We propose a general analytical framework to evaluate the local delay in massive LEO satellite-to-ground downlink networks. Specifically, binomial point process is used to model the locations of satellites. Considering Nakagami fading and directional transmission, we derive the conditional success probability under a given network topology. On this basis, we first give an exact expression for the local delay and further provide an asymptotic analysis when the signal-to-interference-plus-noise ratio tends to zero and infinity. Additionally, we analyze the local delay in three special cases: noise-limited, Rayleigh fading and infinite antenna array of satellite. Numerical results verify our analysis and show that Rayleigh fading model and the asymptotic analysis can simplify and effectively approximate the exact result of the local delay under Nakagami fading.

Orthogonal Time Frequency Space (OTFS) enables reliable communication in high-speed mobility scenarios, making it ideal for Low Earth Orbit (LEO) satellite communication. Furthermore sparse Code Multiple Access (SCMA) supports massive connection in uplink mobile communications. This paper proposes an OTFS-SCMA scheme for LEO satellite communications and the corresponding cross domain detection algorithm. At the transmitter, users are grouped, and a practical codebook is employed. At the receiver, cross-domain detection is utilized to obtain initial estimates, which are then refined using the Message Passing Algorithm (MPA) for optimized detection. Comparative analysis with other baseline schemes demonstrates the performance gain.

For the front-end, large-scale data acquisition is usually realized through paging request mechanism, the efficiency of this mechanism is limited by the browser mechanism, server processing capability and network conditions. In order to achieve efficient processing and display of a large number of geographic data nodes in the front-end big data visualization platform, the scheme proposes a data loading optimization mechanism based on paging request mechanism, which comprises three modules: a detector, a record library and a recommender. The detector is used to detect request conditions in specific environments, the record library stores the detection results, and the recommender returns appropriate paging request results. The scheme not only improves the data processing efficiency, but also enhances the user interaction experience. The scheme can be used in the field of data visualization in various industries, especially for the rapid visualization of geographic data.

During the During the National People's Congress and the Chinese Political Consultative Conference in 2024, the ‘low-altitude economy’ was included in the government work report as a significant factor driving new quality productive forces. In the New Radio (NR) network, when a terminal is accessed, the base station uses SSB (Synchronization Signal Block) beam sweeping to detect the optimal beam for the terminal. After the terminal accesses and obtains the configuration information of the reference signal, it feeds back the channel state information (CSI), and the base station uses the optimal beam from CSI-RS (Channel State Information Reference Signal) beam sweeping. In low-altitude communications, 5G antennas flexibly configure the number of beams, considering horizontal and vertical dimensions. Combining SSB frequency offset technology, the SSB frequency points of the low-altitude network can be staggered with the configuration of the ground network, forming a virtual airground heterogeneous frequency network. This approach enhances performance by reducing handover times and interference.

Concept reduction that preserves binary relations is an emerging reduction theory in the field of Formal Concept Analysis. Its core lies in reducing the number of concepts while ensuring that the original information is not lost, thereby significantly improving the efficiency of data processing. Based on Object Pictorial Diagram (OPD), this paper proposes a novel attribute-oriented concept reduction method that preserves complementary binary relations. First, this paper clarifies the definition of attribute-oriented concept reduction and presents a specific method for addressing it from the perspective of OPD. Against the backdrop of smart education's growing emphasis on data-driven decision-making, accurately diagnosing learners' knowledge states has become a core requirement for instructional reform and personalized tutoring. Practically, by integrating learners' response data to exercises, cognitive diagnosis is conducted by using the obtained attribute-oriented concept reduction results, enabling an in-depth analysis of learners' knowledge states and cognitive structures. Experimental results demonstrate that the proposed method exhibits high efficiency in both solving attribute-oriented concept reduction and performing cognitive diagnosis. The proposed method provides robust support for assessing learners' learning states and enhances the interpretability of various personalized learning applications.

This paper investigates the workflow and requirement of telecommunications operators in handling 4G/5G user network quality complaints and proposes a solution that combines large and small models to achieve more intelligent complaint handling. The large model is responsible for comprehensively analyzing unstructured data such as user complaint texts, extracting key information, and understanding user intentions. Small models are used for indepth processing of structured data related to network performance indicators, conducting root cause analysis, and providing targeted solutions. The models and systems are applied to current network operations, significantly reducing network maintenance optimization work orders, saving labor costs, and improving work efficiency.

With the rapid development in 6G networks, traditional network operation and maintenance (O&M) approaches are insufficient to handle the scale and real-time demands. This paper presents a novel O&M system, applying a multiagent scheduling algorithm to autonomously detect, diagnose, and resolve network faults. The system is structured in five layers: Data, Data Model, Agent, Application, and Interaction layers. And five classes of agents are integrated. Experimental results show the system's ability to outperform manual processes, which demonstrates the efficiency for the demanding needs of nextgeneration network management.

Influence Maximization (IM) is a critical task in social network analysis, yet its application to large-scale networks is often hampered by computational complexity and the challenge of effectively identifying influential communities. Existing community-based IM algorithms frequently rely on traditional detection methods that may not scale well or adequately capture influence propagation dynamics. While deep learning has advanced community detection, the explicit integration of influence properties into these models remains a significant gap. This paper introduces the Deep Learning-based Community-aware Influence Maximization (DLCIM) algorithm, a novel approach that synergistically combines deep learning for community detection with a tailored focus on influence diffusion characteristics. DLCIM employs an autoencoder framework with a novel influence-aware modularity maximization objective to learn node representations that are sensitive to information propagation. Subsequently, it filters important communities and allocates seed quotas proportionally, followed by efficient seed selection using established techniques within these communities. Comprehensive experiments on real-world datasets demonstrate that DLCIM achieves superior influence spread and competitive computational efficiency compared to state-of-the-art baseline algorithms.

This paper analyzes the typical service requirements of low-altitude scenarios and proposes an intelligent weight optimization scheme for 5G beams in low-altitude scenarios using artificial bee colonies and genetic algorithms. Based on key indicators such as coverage quality, interference level, and service perception, joint optimization was conducted and validated in low-altitude networking pilot areas, resulting in significant improvements in computational efficiency and optimization results. This scheme achieved the optimal solution for the weight of contiguous areas, resulting in sound application effects.

Knowledge Graphs (KGs) have emerged as powerful tools for organizing and representing structured knowledge, enabling advanced reasoning and intelligent applications across diverse domains. Traditional approaches to Knowledge Graph Construction (KGC) rely on rule-based systems or supervised learning methods that require extensive feature engineering and labeled data, often limiting their generalization capabilities. Recently, large language models (LLMs) have shown great promise in improving KGC tasks by offering contextualized representations that enhance entity and relation extraction from unstructured text. However, one major limitation of LLMs is their tendency to generate hallucinated or factually incorrect information, which undermines the reliability of the resulting knowledge graphs. To address this challenge, we propose RLRSKGC (Reinforcement Learning with Reward Shaping for Knowledge Graph Construction), a novel framework that integrates reinforcement learning (RL) with Transformer-based language models to improve factual consistency and reduce hallucinations in the knowledge extraction process. Our approach formulates KGC as a sequential decision-making problem and introduces reward shaping mechanisms that explicitly evaluate the accuracy, completeness, and structural coherence of the generated knowledge. We conduct comprehensive experiments on benchmark KGC datasets to validate the effectiveness of our framework, demonstrating that RLRS-KGC achieves superior performance in extracting high-quality, graph-structured knowledge from textual sources.

Telecom fraud in mobile communication networks has become a serious threat to user security and network integrity. Traditional graph neural networks (GNNs) struggle to effectively detect fraudulent activities due to the pervasive noise in real-world fraud data, where genuine fraud signals are often obscured by spurious interactions and feature corruption. To address this challenge, we propose a novel framework combining a Top-p Neighbor Sampler and an adaptive graph neural network module, which selectively aggregates reliable neighbor features while suppressing noise propagation. Experiments on a real-world telecom fraud dataset demonstrate that our model outperforms state-of-the-art methods in macro-F1, AUC, and recall for fraud detection. This work not only provides a practical solution for telecom fraud detection but also offers insights into handling noise contamination in graph-structured data.

The next generation of mobile communication enables the realization of emerging technologies such as smart cities, smart industries and Internet of vehicles. However, the realization of these technologies requires the mobile communication network to have sensing capability of high precision. Starting from the model of integrated sensing and communication (ISAC) system, the processing of wireless signal sending and receiving and frequency domain radar are discussed, including interpolation, target detection, distance and velocity estimation. Then, according to the characteristics of integrated sensing and communication technology, the application scenarios of ISAC are explored. Next, starting from the application scenario requirements of ISAC, the key indicators of sensing are analyzed, and the performance of sensing capabilities is systematically depicted. Finally, based on 5G-A millimeter wave, the remote detection and accurate tracking capabilities of integrated sensing and communication technology in the low-altitude UAV scene are tested and verified. Through the on-site test, technical support is provided for future integrated communication-sensing applications, and the development of communication technology is promoted towards a more efficient and intelligent direction.

In mobile communication networks, host classification plays a critical role in constructing user profiles and ensuring network security. Traditional approaches, which rely on rule-based matching and shallow feature engineering, face significant limitations in coping with the high-frequency dynamic variations of hostnames and the labor-intensive maintenance of manual rules. To address these challenges, this paper proposes a novel frequency-aware hybrid-granularity tokenization method, specifically designed to capture both the semantic structure and statistical patterns of hostnames. By leveraging semi-supervised learning on large-scale host sequence data collected from real-world network environments, the proposed method enables effective service classification through vectorized host representations. This work not only offers an efficient and scalable solution for host analysis in personalized recommendation systems and mobile network security but also provides valuable insights into the design of pretrained tokenizers tailored for dynamic data scenarios.

Temporal action proposal generation plays a vital role in the analysis of untrimmed videos and has garnered growing interest from researchers. Nevertheless, the presence of long-term temporal dependencies and the large variation in action durations within untrimmed videos pose significant challenges for accurately localizing action boundaries. To overcome the aforementioned issues, we design a novel Transformer-based Temporal Feature Pyramid Network (TTFPN) tailored for generating action proposals. Specifically, we introduce a local transformer to capture longterm temporal information while reducing computational complexity through the substitution of conventional selfattention with a localized variant. Subsequently, a temporal feature pyramid is built to produce multi-scale representations, enabling the model to effectively handle action instances of varying durations. Based on this temporal feature pyramid, we employ a convolutional network-based predictor to generate action proposals in an anchor-free manner. We evaluate TTFPN on THUMOS14, a standard benchmark for temporal action detection, to validate its effectiveness. The results show that TTFPN achieves competitive performance and significantly outperforms previous methods.

With unmanned aerial vehicles (UAVs) widely applied in diverse fields, their potential safety risks are more prominent. Accurate UAV identification is crucial. This paper presents a method combining wavelet decomposition and the channel-enhanced attention mechanism. Two-dimensional discrete wavelet transform (2D-DWT) analyzes UAV radio frequency (RF) signal spectrograms for multi-resolution, extracting key information while decomposition data volume and computational complexity. The efficient channel attention (ECA) mechanism boosts the model’s expressiveness. Together with attention-based multi-scale convolution network (AMSCNet), it extracts multi-scale features, reducing information loss and enhancing identification. Experimental results show an average accuracy of 97.00%, outperforming residual network (ResNet) and efficient neural network (EfficientNet). It also has low computational complexity and stable performance across various scenarios, offering an efficient and reliable UAV identification solution.

Multidimensional resources management enhances the efficiency of low-Earth orbit (LEO) satellite systems by dynamically allocating wireless resources. However, the majority of existing studies have overlooked the evolutionary trend of terrestrial terminals (TTs) from homogeneous to heterogeneous types. For instance, energy efficiency (EE) requirements for TTs inherently vary due to differences in their operating environments and deployment costs. Thus, this article investigates the design of beam hopping (BH) patterns, frequency allocation, and power control for LEO satellite uplinks, considering the diverse EE sensitivity (EES) among TTs. Specifically, a resource management problem is formulated to maximize the weighted sum EE, which captures the differences in EES among TTs. Next, an undirected graph is constructed based on EES and spatial isolation, and matching algorithms are proposed according to the many-to-one matching theory to obtain BH patterns. After that, to solve the joint frequency allocation and power control problem, a penalty function is applied to handle the binary variable utilized for expressing frequency allocation. Finally, quadratic transform and minorize–maximization are employed to concave the original problem, which guarantees to obtain a suboptimal solution. Simulation results demonstrate that proposed BH design methods provide a higher optimization ceiling for joint resources management than relevant benchmarks. Besides, the proposed uplink resources management significantly improves the EE by 34% in LEO satellite uplink compared with ignoring EES scheme. This work establishes a novel paradigm for energy-efficient uplink resource management in heterogeneous LEO systems.

Beam hopping (BH) is a widely adopted technique in multi-beam satellite communication systems, and it can effectively improve the system capacity. However, conventional BH with full frequency reuse requires spatial isolation to avoid inter-beam interference, and it will impose restrictions on the flexibility of BH and achievable system capacity. By combining the BH and multi-beam precoding, it is beneficial to satisfy the uneven traffic demands and keep the flexibility of beam management. Different from the existing works that focus on the joint BH and precoding for a single satellite, we investigate the joint multi-satellite cell association, BH pattern design and multi-beam precoding problem for dense low earth orbit (LEO) satellite communication systems. To tackle the complex problem, we decompose it into two subproblems. First, a many-to-one matching based multi-satellite cell association algorithm is proposed, which balances the load among satellites and mitigates interference among activated cells. Second, many-to-many matching based BH pattern design and quadratic transform based precoding algorithms are proposed and alternately optimized to solve the joint BH and precoding problem. Lastly, a multi-satellite joint BH and precoding algorithm based on alternating optimization (MJBHPAO) is proposed to solve the entire problem. The effectiveness of the proposed algorithm is verified with various parameters, and simulation results show that the proposed algorithm performs better than the referred schemes.

Along with the development of Internet of Things systems, numerous edge intelligent devices can obtain a large amount of user data, and the analysis of this user data can be applied to business scenarios, such as user click-through rate (CTR) prediction. In the recommendation, advertising and other scenarios, the users at the edge have high response requirements for CTR prediction model training and inference. In the current edge scenario of CTR prediction, there are problems of overly complex model structure and highly sparse original features, which makes it difficult to deploy CTR prediction models at the edge. Therefore, we propose KD-based graph attention FI model (KD-GAFIM), a lightweight recommendation algorithm that combines graph neural networks (GNNs) with knowledge distillation (KD). The approach uses graph attention networks to flexibly capture feature dependencies in a way that maintains a small model size while augmenting the feature vector with feature dependencies. And by sharing the embedding layer of the teacher model, KD-GAFIM improves the efficiency of user CTR prediction. On top of that, we also propose a feature compression strategy guided by model interpretability, which identifies high-contributing features for inference and model refinement based on their performance in model interpretability. This strategy improves efficiency, making KD-GAFIM suitable for training and inference on edge devices. We conducted extensive experiments on multiple datasets. The experimental results show that KD-GAFIM outperforms various state-of-the-art CTR prediction models, demonstrating that GNN-based KD models can improve model performance while reducing model size and feature dimensionality, and have significant potential for application at the edge.

In modern vehicular networks, the absence of infrastructure support, such as roadside units (RSUs), presents significant challenges for efficient task offloading and allocation. Limited computational capabilities of individual vehicles, combined with task allocation imbalances caused by varying task complexity and vehicle capacities, further complicate the process. Additionally, the formation of vehicular platoons requires accurate future route and destination information to ensure stable collaboration and effective coordination. However, such information can be challenging to obtain due to dynamic and unpredictable road environments, hindering the reliability of platoon formation. To address these challenges, we propose a platoon-based offloading strategy that integrates deep reinforcement learning (DRL) and long short-term memory (LSTM) networks to enhance task allocation efficiency. This approach also leverages the convoy formation algorithm considering future positions (CFA-FPs) to manage platoon constraints effectively. Experimental results demonstrate that our method significantly improves key performance metrics, including total computation cost, latency, and offloading success rate, compared to other task offloading strategies.

Satellite Edge Computing (SEC) enhances real-time data processing by deploying computational resources at the edge of satellite networks, reducing Task Completion Delay (TCD). To minimize TCD, the characteristics of satellites with large coverage, strong collaborative capabilities, and limited resources must be fully considered. This paper proposes a novel three-stage task offloading framework to optimize task execution in dynamic and resource-constrained satellite environments. First, to efficiently offload computational tasks among large-scale, heterogeneous users, we introduce a region division-based offloading strategy and develop the Adaptive Division Offloading Region (ADOR) algorithm, which dynamically partitions satellite coverage areas to improve offloading efficiency. Second, to enhance the collaborative computing capabilities of Low Earth Orbit (LEO) satellite constellations, we propose a Particle Swarm Optimization Genetic (PSOG) algorithm to optimize TS under dynamic conditions. Finally, to tackle the limited and interdependent computing resources of satellites, we design an Intelligent Parameter Adjustment (IPA) algorithm based on Q-learning, which dynamically adjusts computational parameters to maximize processing speed while ensuring system stability. Simulation results demonstrate that our proposed framework outperforms existing methods. Compared with the baseline algorithm, it achieves higher task offloading efficiency and better resource allocation. Additionally, it maintains stable satellite operations while reaching the highest processing speed.

Multi-modal semantic communication (MSC) has gained great attention due to its multi-modal processing ability. However, the existing MSC systems are mainly built on multi-modal large models that lead to inefficient computation on non-essential tokens, potentially restricting MSC from achieving more advanced levels of intelligence. To address this challenge, we propose a large model-empowered MSC system with a cross-modal attention-based token selection mechanism, denoted as LMECM-SC, which effectively utilizes the attention score across multi-modal tokens to filter out noisy or unuseful tokens, selectively learning the tokens that best benefit downstream applications. Meanwhile, we introduce the multi-modal adaptive semantic encoder and decoder that dynamically assign weights to encode multi-modal semantic information extracted from the selected tokens based on their modality and integrate semantic information with cross-modal attention scores at the receiver, optimizing the performance on downstream tasks. Experiment results indicate that LMECM-SC effectively reduces the number of tokens used for training, outperforming four baseline methods in terms of bilingual evaluation understudy score for text, learned perceptual image patch similarity for image, and perceptual evaluation of speech quality score for speech.

This paper investigates the energy-efficient hybrid beamforming design for a multi-functional integrated sensing, communications, and powering (ISCAP) system. In this system, a base station (BS) with a hybrid analog-digital (HAD) architecture sends unified wireless signals to communicate with multiple information receivers (IRs), sense multiple point targets, and wirelessly charge multiple energy receivers (ERs) at the same time. To facilitate the energy-efficient design, we present a novel HAD architecture for the BS transmitter, which allows dynamic on-off control of its radio frequency (RF) chains and analog phase shifters (PSs) through a switch network. We also consider a practical and comprehensive power consumption model for the BS, by taking into account the power-dependent non-linear power amplifier (PA) efficiency, and the on-off non-transmission power consumption model of RF chains and PSs. We jointly design the hybrid beamforming and dynamic on-off control at the BS, aiming to minimize its total power consumption, while guaranteeing the performance requirements on communication rates, sensing Cramér-Rao bound (CRB), and harvested power levels. The formulation also takes into consideration the per-antenna transmit power constraint and the constant modulus constraints for the analog beamformer at the BS. The resulting optimization problem for ISCAP is highly non-convex due to the binary on-off non-transmission power consumption of RF chains and PSs, the non-linear PA efficiency, and the coupling between analog and digital beamformers. To tackle this problem, we first approximate the binary on-off non-transmission power consumption into a continuous form, and accordingly propose an iterative algorithm to find a high-quality approximate solution with ensured convergence, by employing techniques from alternating optimization (AO), sequential convex approximation (SCA), and semi-definite relaxation (SDR). Then, based on the optimized beamforming weights, we develop an efficient method to determine the binary on-off control of RF chains and PSs, as well as the associated hybrid beamforming solution. Numerical results show that the proposed design achieves an improved energy efficiency for ISCAP than other benchmark schemes without joint design of hybrid beamforming and dynamic on-off control. This validates the benefit of dynamic on-off control in energy reduction, especially when the multi-functional performance requirements become less stringent.

Unmanned aerial vehicle (UAV) assisted communication is becoming a promising technology for future networks. Leveraging this benefit, the ambient backscatter communication can utilize the UAV’s emitted signal as the radio frequency carrier to transmit its own information. However, this transmission behavior is easily to be detected by the UAV due to the high possibility of line-of-sight (LoS) air-ground channel. Thus, in this paper, we propose a covert ambient backscatter communication scheme by exploiting the UAV relay as the radio frequency source. Specifically, the UAV relays the information for two legitimate ground nodes, and monitors the potential ambient backscatter communication. Our goal is to maximize the covert ambient backscatter communication rate under the worst case that the UAV performs with the optimal detection threshold, transmit power and hovering location. First, the UAV’s optimal detection threshold is analyzed, and the corresponding closed-form expression of error detection probability is derived. Then, we propose an iterative algorithm to achieve the minimum error detection probability by optimizing the transmit power and hovering location of UAV. To fight against the detection of UAV, we formulate a convex optimization problem to maximize the worst-case covert ambient backscatter communication rate by adjusting the reflection coefficient. Simulation results show that the proposed scheme can effectively improve the covert ambient backscatter communication rate.

As a new paradigm focusing on transmitting the meaning of information, semantic communications (SCs) have been revealed significant potential in alleviating network congestion and improving energy efficiency. By extracting a small-size semantic feature from the large-size raw-data, the communication loads can be reduced at a price of more computing power, i.e., the computational resource used in semantic extraction. In this paper, we explore the computation dimension to improve the communication performance in an uplink SC system. The SC-oriented user first compresses its original data via local computing during other users’ transmission time and then transmits it to the base station within the assigned time. To achieve a balanced tradeoff between communications and computing, we formulate an optimization problem to minimize the energy consumption of all users by jointly considering the compression ratio and time allocation. We first propose an efficient general algorithm that can be applied to different SC models. To gain more insights, we then derive the closed-form solutions for two special cases: equal-time allocation and two-user transmission. The obtained analytical results reveal a pronounced energy saving of adopting SC, especially for large transmitted data size, high transmission energy coefficient, scarce time resources, and poor channel conditions. Simulation results reveal that our proposed SC-based scheme achieves excellent performance in terms of saving energy consumption compared to the conventional communication.

This paper studies the joint optimization of multi-objective dependent task scheduling, resource allocation, and service caching in an aerial-ground integrated mobile edge computing system that includes multiple uncrewed aerial vehicles (UAVs). These UAVs, in coordination with a high-altitude platform, work together to process numerous dependent tasks collected by the UAVs. The optimization problem involves two conflicting objectives that need to be minimized simultaneously: the average execution delay of all dependent tasks and the average energy consumption of all UAVs. The conflict between the two objectives makes the problem quite challenging. Recently, some multi-objective approaches, such as multi-objective evolutionary algorithms (MOEAs), have been introduced to address dependent task scheduling. However, these approaches often suffer from premature convergence and tend to fall into local optima. To address these issues, we propose a modified MOEA based on decomposition that incorporates two performance-improving strategies. The first one is a probability-based neighborhood search strategy that selects two individuals to update neighborhood individuals based on the neighborhoods and external population, thereby improving population updating efficiency. The second one is a dynamic voltage and frequency scaling-based energy reduction strategy that further enhances the quality of solutions by adjusting the computing frequencies. Experimental results verify that the proposed algorithm obtains a number of outstanding nondominated solutions and achieves a better balance between objectives compared with several algorithms.

The rapid advancement of Software-Defined Networks (SDN) and Network Function Virtualization (NFV) has popularized the adoption of the Service Function Chain (SFC) paradigm for efficient network service delivery. This paradigm leverages the flexibility and cost-effectiveness of deploying Virtual Network Functions (VNFs) as software entities or virtual machines on off-the-shelf servers. Chaining VNFs together allows traffic to be directed through the network as required. However, existing algorithms for traffic steering and routing path computation in SFC suffer from many challenges, including complexity, lack of scalability, and low time efficiency. This paper focuses on addressing the challenges associated with VNF placement and SFC chaining in SDN/NFV-enabled networks. Our objective is to identify an optimal solution for VNF placement that maximizes the utilization of network resources. We formulate the problem as a Binary Integer Programming (BIP) model to accomplish this. Additionally, we propose a novel algorithm called DeepSelector, which incorporates deep learning techniques and an intelligent node selection network to determine the optimal placement of VNFs for SFC requests. Through performance evaluation, we demonstrate that DeepSelector achieves high network resource utilization and offers efficient VNF placement computation, significantly improving overall network performance.

Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data collected by different satellites and the problems of efficient inter-satellite collaborative computation pose significant obstacles to realizing the potential of these constellations. Existing approaches struggle with data heterogeneity, varing image resolutions, and the need for efficient on-orbit model training. To address these challenges, we propose a novel decentralized PFL framework, namely,ANovel DecentraLized PersonAlized Federated Learning for HeterogeNeous LEO SatellIte CoNstEllation (ALANINE). ALANINE incorporates decentralized FL (DFL) for satellite image Super Resolution (SR), which enhances input data quality. Then it utilizes PFL to implement a personalized approach that accounts for unique characteristics of satellite data. In addition, the framework employs advanced model pruning to optimize model complexity and transmission efficiency. The framework enables efficient data acquisition and processing while improving the accuracy of PFL image processing models. Simulation results demonstrate that ALANINE exhibits superior performance in on-orbit training of SR and PFL image processing models compared to traditional centralized approaches. This novel method shows significant improvements in data acquisition efficiency, process accuracy, and model adaptability to local satellite conditions.

Although some studies in recent years have focused on the coexistence of service and task dependencies in the collaborative optimization of service caching and task offloading in Vehicle Edge Computing, the challenges brought by dual dependencies have not been fully addressed. Therefore, this paper proposes a more comprehensive joint optimization method for service caching and task offloading under dual dependencies. First, this paper proposes a service criticality prediction method based on the Gated Graph Recurrent Network to perceive complex task dependencies and accurately capture the service requirements of critical task types. Based on this, a hierarchical active-passive hybrid caching strategy is designed, which aims to satisfy diverse service demands while reducing the additional overhead caused by remote service requests. Second, a global task priority computation method based on application heterogeneity has been developed to prevent cascading delays in task chains. Finally, this paper formulates a joint optimization problem for service caching and task offloading in a three-layer VEC system, models it as a Markov Decision Process, and applies a Proximal Policy Optimization-driven collaborative optimization algorithm named COHCTO. Simulation results show that COHCTO achieves multi-objective optimization across metrics such as delay, energy consumption, caching hit rate, and application success rate under conditions different from those of other algorithms.

Recently, numerous deep learning algorithms have addressed wearable human activity recognition (HAR), but they often struggle with efficient knowledge transfer to lightweight models for mobile devices. Knowledge distillation (KD) is a popular technique for model compression, transferring knowledge from a complex teacher to a compact student. Most existing KD algorithms consider homogeneous architectures, hindering performance in heterogeneous setups. This is an under-explored area in wearable HAR. To bridge this gap, we propose a heterogeneous mutual KD (HMKD) framework for wearable HAR. HMKD establishes mutual learning within the intermediate and output layers of both teacher and student models. To accommodate substantial structural differences between teacher and student, we employ a weighted ensemble feature approach to merge the features from their intermediate layers, enhancing knowledge exchange within them. Experimental results on the HAPT, WISDM, and UCI_HAR datasets show HMKD outperforms ten state-of-the-art KD algorithms in terms of classification accuracy. Notably, with ResNetLSTMaN as the teacher and MLP as the student, HMKD increases by 9.19% in MLP's $F_{1}$ score on the HAPT dataset.

Approximate membership query (AMQ) data structures can approximately determine whether an element exists in a given dataset. They are widely used in parallel and distributed systems (e.g., high-performance databases, distributed cache systems, and bioinformatics systems) to avoid unnecessary dataset accesses, thereby accelerating massive data processing. For AMQ data structures used in the above systems, achieving high throughput, low false positive rate, and large capacity objectives simultaneously is critical but challenging. Porting AMQ data structures from DRAM to persistent memory makes it possible to achieve the above three objectives simultaneously, but this porting is not a trivial task. Specifically, existing AMQ data structures generate numerous random accesses and/or sequential writes on persistent memory, resulting in poor throughput. Therefore, in the conference version of this paper, we proposed a novel AMQ data structure called wormhole filter, which achieves high throughput on persistent memory, thereby achieving the above three objectives simultaneously. In this journal version, we extend our prior work by introducing parallel wormhole filters to enhance parallel performance. Additionally, we integrate parallel wormhole filters into the LevelDB database system to show that porting AMQ data structures to persistent memory significantly improves system endto-end throughput. Theoretical analysis and experimental results show that wormhole filters significantly outperform state-of-theart AMQ data structures. For example, wormhole filters achieve 12.06× insertion throughput, 1.98× positive lookup throughput, and 8.82× deletion throughput of the best competing baseline.

In non-orthogonal multiple access (NOMA) networks, intelligent reflecting surface (IRS) and artificial noise (AN) can provide a double guarantee to achieve the secure transmission, especially essential for the far user with poor channel. However, AN is often eliminated via successive interference cancellation (SIC), which severely mitigates the secrecy energy efficiency. To tackle this issue, we propose a constructive interference precoding (CIP) enabled secure IRS-NOMA scheme, leveraging both the inter-user interference and AN to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. In the CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and SIC for the near user and the IRS unit modulus constraint. To handle this non-convex problem, we propose an alternating optimization algorithm. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Furthermore, to ensure the robustness, we also extend the scheme to a more practical case of imperfect CSI, where we utilize the S-procedure to deal with the channel uncertainty. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA in both cases.

This paper considers a multi-functional orthogonal frequency division multiplexing (OFDM) system with integrated sensing, communication, and powering (ISCAP), in which a multi-antenna base station (BS) transmits OFDM signals to simultaneously deliver information to multiple information receivers (IRs), provide energy supply to multiple energy receivers (ERs), and sense potential targets based on the echo signals. To facilitate ISCAP, the BS employs the joint transmit beamforming design by sending dedicated sensing/energy beams jointly with information beams. Furthermore, we consider the beam scanning for sensing, in which the joint beams scan in different directions over time to sense potential targets. In order to ensure the sensing beam scanning performance and meet the communication and powering requirements, it is essential to properly schedule IRs and ERs and design the resource allocation over time, frequency, and space. More specifically, we optimize the joint transmit beamforming over multiple OFDM symbols and subcarriers, with the objective of minimizing the average beampattern matching error of beam scanning for sensing, subject to the constraints on the average communication rates at IRs and the average harvested power at ERs. We find converged high-quality solutions to the formulated problem by proposing efficient iterative algorithms based on advanced optimization techniques. We also develop various heuristic designs based on the principles of zero-forcing (ZF) beamforming, round-robin user scheduling, and time switching, respectively. Numerical results show that our proposed algorithms adaptively generate information and sensing/energy beams at each time-frequency slot to match the scheduled IRs/ERs with the desired scanning beam, significantly outperforming the heuristic designs.

This paper studies a distributed semantic communication (SC) problem for multivariate time series forecasting tasks in edge environments, with heterogeneous clients considered. At the client side, a semantic encoder is composed of a number of federated blocks and this number is subject to local resource availability. Each federated block consists of a patch-wise attention module (PAM) and a federated adapter, extracting semantic information for efficient transmission across wireless channels. Based on the federated adapters, this paper proposes an SC-oriented heterogeneous federated learning architecture, named SC-FedAda. SC-FedAda adopts self-distillation to facilitate cross-client and cross-layer knowledge sharing, enabling efficient collaborative inference. At the edge server, semantic signals are fed into a channel decoder and then a semantic decoder. The semantic decoder consists of a PAM and a fully connected network for forecasting tasks. Simulation results demonstrate that SC-FedAda outperforms four state-of-the-art federated learning-based structures under three types of wireless channels, i.e. SC-FedAda achieves much lower forecasting loss on three widely-used time series datasets, particularly in low signal-to-noise ratio scenarios.

Mobile edge computing (MEC) decentralizes the computational and storage capabilities of the network to edge nodes, providing support for the dynamic deployment and rapid response of mobile services. However, the large-scale distributed deployment of edge nodes, their widespread geographic distribution, multi-dimensional and complexly dependent service characteristics, and the dynamically changing network environment pose challenges to energy-efficient service deployment. In this paper, we consider multi-dimensional features for service deployment, including dynamic traffic demand, geography information, service semantics, and service popularity, with the aim of improving the availability of edge services and reducing network energy consumption. Firstly, to handle the large volume of edge service data, we design a multi-dimensional feature extraction approach based on the Transformer model, which does not rely on the sequential order of data and can enhance computational efficiency of edge models through parallel processing. Then, to adapt to the dynamically changing edge network environment, we propose an Energy-Efficient Service Deployment algorithm (EESD) based on the improved Dueling Deep Q-Network, which makes service deployment and base station switching decisions in a learning-based manner. Finally, simulation results demonstrate that EESD outperforms comparison algorithms in terms of model convergence, system total cost, and energy consumption.

Network function virtualization (NFV) enables the provision of different quality of service (QoS) levels through service function chains (SFCs), where NFV outsources big data tasks of end users to nearby edge servers. In multi-access edge computing (MEC), its dynamic and uncertainty nature poses great challenges to the SFC placement problem, which requires optimizing multiple potentially-conflicting objectives, such as network latency and load balancing. Moreover, user preferences may vary along with time, adding another layer of complexity to the problem. To address the problem above, we propose a novel distributed deep reinforcement learning (DRL) architecture based on a spatio-temporal encoder (STE), denoted as DDRL-STE. DDRL-STE is featured with equal-weight pre-training and transformer-based STE. Experimental results show that DDRL-STE outperforms three state-of-the-art DRL algorithms regarding latency and load balancing under three well-known network topologies, exhibiting its excellent potential in exploration and generalization.

Poor coverage quality is a common cause of poor wireless communication network quality, which seriously affects the user experience in mobile communication. Currently, the front line mainly adopts a manual trial-and-error method, which has problems such as low efficiency and high human cost. How to use artificial intelligence algorithms to quickly and accurately identify and solve the problem of poor coverage quality based on existing data is one of the important research directions in the field of wireless networks. The data of wireless networks is essentially spatio-temporal data, but most of the existing methods are based on time-domain and space-domain data for analysis and modeling, and the information mining in the spatio domain is not sufficient. In the spatio domain, the distribution of base stations is not uniform in Euclidean space, which increases the difficulty of spatio-temporal modeling. In view of the natural advantages of graph mining technology for modeling and processing unstructured data, this paper proposes a model named Spatio-Temporal Graph Message Propagation (STGMP) based on graph technology. This method uses spatio-temporal graphs to represent the historical states of related service cells, proposes a processing layer that combines the time and spatio domains, and maps the actual problem to a multi-classification task, thereby achieving the identification of the causes of poor coverage quality. This paper also conducts experiments on real data sets, and the results show that the proposed method STGMP is very effective.

The fragmented architecture, cloud-based infrastructure, and functionally virtualized network elements within the 5 G core network have significantly surged the volume and diversity of alarms generated on cloud network service platforms that it supports. Given the inherently cross-layered nature of failure scenarios on these platforms, identifying the root causes presents a significant challenge. Alarm association rule mining has become an effective means to address the problems of alarm correlation and root cause localization. In this paper, an explainable alarm association rule mining approach based on knowledge graph, referred to as ARK-G, is proposed. Initially, a cloud-network cross-layer alarm association knowledge graph (CA2KG) is constructed. Subsequently, the knowledge embedding based graph convolutional network is employed to perform knowledge graph embedding on CA2KG. This embedding is then utilized to enhance the RNNLogic algorithm, thereby facilitating cross-layer alarm association rule mining with interpretable paths. Finally, a weighted rule tree is derived from a subset of CA2KG and the generated explainable rules, enabling the deduction of the root alarm. Experimental results demonstrate that the proposed ARK-G approach for association rule mining yields a higher hit rate compared to the baseline model, which provides valuable assistance in the faults analysis of 5 G cloud-network platforms.

Integrated sensing and communication (ISAC) and space-air-ground integrated networks (SAGIN) have been considered as key technologies of 6G. The challenge of achieving ISAC in uplink massive access scenarios within the SAGIN has become a major research topic. This paper introduces the Resilient Massive Access (RMA) protocol, which deeply integrates S-ALOHA and NOMA, effectively enhancing the system's access success probability. Additionally, a cascaded uplink detection algorithm based on LS and MUSIC (MUSIC-NOMA-TSA) is proposed, enabling signal decoding and target sensing even in the presence of collisions at the receiver. Simulation results demonstrate that, compared to traditional algorithms, the proposed algorithm not only achieves more accurate signal decoding and target sensing but also significantly improves the access success probability.

Although intelligent reflecting surface (IRS) can reconfigure the propagation environment to enhance the performance of both non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), the security remains a key challenge. We design a secure beamforming scheme for IRS-assisted NOMA SWIPT networks in this paper, where the artificial jamming is inserted into NOMA signals by the base station to ensure the network security with the aid of IRS. Specifically, we jointly optimize the transmit beamforming and jamming vectors, the IRS reflecting matrix and the power splitting ratio to maximize the sum rate, satisfying the rate requirement and energy harvesting threshold for each user. The optimization problem is difficult to be solved directly due to its non-convexity with coupled variables. Thus, we first apply auxiliary variables to reformulate it into a more tractable form, and then decompose it into three subproblems that can be converted into convex ones via successive convex approximation. Finally, we solve them iteratively using an alternating optimization algorithm. Simulation results validate that the proposed scheme can yield significant improvement in both secrecy performance and energy harvesting efficiency in comparison with benchmarks.

This paper investigates semantic energy efficiency in a non-orthogonal multiple access (NOMA) enabled integrated sensing and semantic communication (ISSC) system. The model involves the base station (BS) transmitting information to multiple users while performing target sensing using dedicated beamforming. In the considered model, the BS needs to transmit substantial text data to each user using text semantic communication techniques while sensing the targets with certain constraints. Our goal is to maximize semantic energy efficiency and meet semantic communication and sensing accuracy requirements. We formulate an optimization problem for the beamforming matrix and semantic parameter, employing the Dinkelbach's algorithm for simplification and proposing an iterative solution. Numerical results validate the efficacy of the NOMA-ISSC scheme.

Sparse code multiple access (SCMA) is a new nonorthogonal multiple access scheme, which effectively exploits the constellation shaping gain of multi-dimensional codebook. In this paper, we propose a new SCMA architecture of uplink satellite communication system. At the transmitter, we group the users and design a practical spherical codebook. At the receiver, a low-complexity multi-user detection algorithm, namely logarithm domain message passing algorithm (Log-MPA) is implemented. The results show that the reliability of the proposed codebook outperforms the existing SCMA codebook schemes in both AWGN and Rayleigh channels.

This paper studies the over-the-air computation (AirComp) in an orthogonal frequency division multiplexing (OFDM) system with imperfect channel state information (CSI), in which multiple single-antenna wireless devices (WDs) simultaneously send uncoded signals to a multi-antenna access point (AP) for distributed functional computation over multiple subcarriers. In particular, we consider two scenarios with best-effort and error-constrained computation tasks, with the objectives of minimizing the average computation mean squared error (MSE) and the computation outage probability over the multiple subcarriers, respectively. Towards this end, we jointly optimize the transmit coefficients at the WDs and the receive beamforming vectors at the AP over subcarriers, subject to the maximum transmit power constraints at individual WDs. First, for the special case with a single receive antenna at the AP, we propose the semi-closed-form globally optimal solutions to the two problems using the Lagrange-duality method. It is shown that at each subcarrier, the WDs’ optimized power control policy for average MSE minimization follows a regularized channel inversion structure, while that for computation outage probability minimization follows an on-off regularized channel inversion, with the regularization dependent on the transmit power budget and channel estimation error. Next, for the general case with multiple receive antennas at the AP, we present efficient algorithms based on alternating optimization and convex optimization to find converged solutions to both problems. It is shown that with finite receive antennas at the AP, a non-zero computation MSE for AirComp is inevitable due to the channel estimation errors even when the transmit powers at WDs tend to infinity, while with massive receive antennas, the average MSE and outage probability vanish when the channel vectors are independent and identically distributed. Finally, numerical results are provided to demonstrate the effectiveness of the proposed designs.

Although intelligent reflecting surface (IRS) can reconfigure the propagation environment to enhance the performance of both non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), the security remains a key challenge. We design a secure beamforming scheme for IRS-assisted NOMA SWIPT networks in this paper, where the artificial jamming is inserted into NOMA signals by the base station to ensure the network security with the aid of IRS. Specifically, we jointly optimize the transmit beamforming and jamming vectors, the IRS reflecting matrix and the power splitting ratio to maximize the sum rate, satisfying the rate requirement and energy harvesting threshold for each user. The optimization problem is difficult to be solved directly due to its non-convexity with coupled variables. Thus, we first apply auxiliary variables to reformulate it into a more tractable form, and then decompose it into three subproblems that can be converted into convex ones via successive convex approximation. Finally, we solve them iteratively using an alternating optimization algorithm. Simulation results validate that the proposed scheme can yield significant improvement in both secrecy performance and energy harvesting efficiency in comparison with benchmarks.

Despite its potential for reducing the detection probability at the warden, the effectiveness of covert communication in practical situations is often hindered by harsh wireless signal propagation environments. Fortunately, intelligent reflecting surface (IRS) can establish programmable wireless channels to tackle this issue. In this paper, we propose two IRS-assisted finite-blocklength covert communication schemes to maximize the effective covert throughput (ECT) with equal and unequal transmit prior probabilities, respectively. First, we analyze the warden’s detection performance with its optimal detection threshold derived, which is the worst situation for the covert transmission. We jointly optimize the transmit power, transmission blocklength, prior transmission probability and IRS’s phase shifts to maximize ECT in the common scenario and packet-generation scenario, respectively, which covers a wide range of practical applications. The designed optimal phase shifts not only maximize the signal-to-noise ratio at the receiver, but also introduce uncertainty to the warden for covertness provisioning. The closed-form expressions of solutions indicate that there exists a non-trivial trade-off between ECT and covertness, and adopting unequal transmit prior probabilities is proved to perform better than its counterpart of equal probabilities. Finally, numerical results demonstrate the superior performance achieved by the proposed covert communication schemes.

Multi-user semantic communication (MUSC) has emerged as a promising paradigm for future 6G networks and applications, where massive clients (e.g., mobile devices) collaboratively construct a global semantic decoder without sharing their local data. However, due to the lack of direct access to clients’ data, MUSC is vulnerable to data poisoning attacks (DPAs), wherein malicious participants send updates derived from poisoned training samples. Current defense techniques against DPAs are designed for traditional networks and are not directly applicable to MUSC. In this paper, we propose an effective attack-defense game framework, denoted as DPAD-MUSC, tailored to defend against DPAs during image transmission for MUSC. First, we determine each attack-type's optimal attack policy based on reinforcement learning, with the aim of strengthening the attack while avoiding detection. To generate adversarial samples accordingly, we devise an adversarial samples generator (ADV-Generator) based on conditional generative adversarial network (CGAN). Then, we introduce an attack defender (DPA-Defender) to detect data poisoning attacks and exclude poisoned samples from the target model's learning process, with the adversarial samples generated under the guidance of the optimal attack policy to enhance the detector's robustness. Simulation results demonstrate that the DPAD-MUSC can find optimal attack policies that cause a greater accuracy drop in the target model while maintaining a higher evasion rate. The ADV-Generator can generate effective adversarial samples and the DPA-Defender outperforms five state-of-the-art methods on three widely used image datasets under additive white Gaussian noise (AWGN) channel in terms of Top-1 accuracy.

This paper studies the age of information (AoI) and energy tradeoff (AET) problem in an aerial-ground collaborative mobile edge computing system, where a high-altitude platform and an unmanned aerial vehicle (UAV) work together to offer computing services for ground devices (GDs). The AET problem is formulated as a multi-objective optimization problem (MOP) that aims at simultaneously minimizing the total AoI of GDs and total energy consumption of the UAV by optimizing its flight paths and task offloading ratios. Addressing the AET problem poses a significant challenge due to the inherent conflict between the two objectives. The existing methods cannot well address the MOP because they adopt the linear combination to transform an MOP into a single-objective optimization problem using fixed weights (i.e., preferences), ignoring the conflict between objectives. Moreover, user preferences may change over time in dynamic MEC systems. To overcome these challenges, we first build a multi-objective Markov decision process model with a vectorial reward for the AET problem. There are one-to-one relationships between each component of the reward and one of the two objectives. Then, we propose a multi-objective learning algorithm based on proximal policy optimization (PPO), which primarily comprises a training phase and an evolutionary phase. The former adopts multi-objective PPO to iteratively optimize multiple learning individuals, aiming to obtain a nondominated policy set. The latter employs a genetic operator to further improve the quality of each policy in the set. Specifically, the crossover and mutation operators operate at the parameter level of policy networks, avoiding stagnation and premature convergence. The experiment results validate that the proposed approach obtains a set of excellent nondominated policies and a favorable balance between objectives. Moreover, the proposed approach achieves improvements of at least 39.8%, 2.1%, and 15.3% regarding AoI, energy consumption, and cost compared with several algorithms.

Mobile edge computing (MEC) brings computational resources to the edge of network that triggers the paradigm shift of centralized machine learning towards federated learning. Federated learning enables edge nodes to collaboratively train a shared prediction model without sharing data. In MEC, heterogeneous edge nodes may join or leave the training phase during the federated learning process, resulting in slow convergence of dynamic communities and federated learning. In this paper, we propose a fine-grained training strategy for federated learning to accelerate its convergence rate in MEC with dynamic community. Based on multi-agent reinforcement learning, the proposed scheme enables each edge node to adaptively adjust its training strategy (aggregation timing and frequency) according to the network dynamics, while compromising with each other to improve the convergence of federated learning. To further adapt to the dynamic community in MEC, we propose a meta-learning-based scheme where new nodes can learn from other nodes and quickly perform scene migration to further accelerate the convergence of federated learning. Numerical results show that the proposed framework outperforms the benchmarks in terms of convergence speed, learning accuracy, and resource consumption.

Time series data widely exist in public services, industrial environments, and military applications. Traditionally, the transmission of a huge volume of data for analytic tasks poses challenges, particularly in mobile environments with limited computing and communication resources. Semantic communication emerges as a solution for intelligently extracting various features from source data and efficiently transmitting task-related information to receivers, thereby reducing bandwidth consumption significantly. In this paper, we introduce a novel federated semantic communication system tailored for forecasting-oriented time series transmission tasks. The correlation of source data collected from terminal devices is mined and the corresponding semantic information is transmitted to an edge server for collaborative inference. To optimize the semantic analysis process, we devise a deep decomposition block at the transmitter side, decomposing time series into trend and multiple period components. This reduces noise interference from wireless channels, enhancing the overall transmission quality. For effective training and collaborative inference, we propose a Federated Mixture of period Routers (FedMoR) architecture. Within each channel encoder, period routers are divided into private and public ones. Private routers extract specialized features from individually collected data, mitigating accuracy degradation. Public routers share knowledge across all transmitters, enhancing temporal analysis robustness. Simulation results demonstrate that the proposed system outperforms two traditional technique-based and two semantic communication-based baselines under three common channels. The system achieves low mean square errors on five widely-used real-world time series forecasting datasets, particularly in the low signal-to-noise ratio regime.

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MB-RMR within a single area, eliminating the need for multi-region datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multi-band data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework’s adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT’s enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT’s effectiveness and practicality for MB-RMR in environments with limited data availability.

Recently, intelligent reflecting surface (IRS) emerges as an effective technique for saving power consumption by customizing the wireless propagation environment. On the other hand, the symbol level precoding (SLP) technique provides a clever solution to interference exploitation by converting the multiuser interference (MUI) into a beneficial part of the desired signal. In this paper, we propose to jointly exploit IRS and SLP to cope with the power control and interference management issues in a heterogeneous network (HetNet). Considering the possible coordination between the macro base station (MBS) and the pico base station (PBS), we propose two corresponding schemes to manage the inter-cell and intra-cell interference. For both proposed schemes, the power minimization problems are studied by jointly optimizing the precoding matrices at the MBS and PBS as well as reflecting coefficients at the IRS. Due to the non-convexity of these problems, the precoding matrices and reflecting coefficients are optimized alternately. We propose two Lagrangian based algorithms to obtain the optimal solutions of the precoding matrices, where the precoding matrix of the MBS always yields a closed-form. A multiple-gradient descent algorithm based on the Riemannian manifold (MGD-RM) is proposed as well to enhance the received signal quality of each MUE and PUE for the reflecting design. Simulation results manifest a significant performance gain achieved by our proposed HetNet over benchmarks.

Covert communication assisted by intelligent reflecting surface (IRS) has been widely investigated. Specifically, IRS can reconfigure wireless propagation environment to introduce uncertainty to the warden for covertness provisioning. In this paper, we propose an IRS-assisted finite-blocklength covert communication scheme with unequal transmit prior probabilities (UTPP) resulting from random packet generation at the transmitter. First, we analyze the warden's detection performance with its optimal detection threshold derived, which is the worst case for covert transmission. Then, we jointly optimize the transmit power, the blocklength, the phase shifts of IRS, and the transmit prior probabilities to maximize the effective covert throughput (ECT). Theoretical analysis reveal that UTPP can perform better tradeoff between ECT and covertness than equal transmit prior probabilities. Finally, numerical results demonstrate the superiority of the proposed covert communication scheme with UTPP.

Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independent, meaning that the GPSSMs cannot exploit the inductive biases between different outputs and lose certain model capacities. To address this issue, this paper proposes an output-dependent and more realistic GPSSM by utilizing the well-known, simple yet practical linear model of coregionalization (LMC) framework to represent the output dependency. To jointly learn the output-dependent GPSSM and infer the latent states, we propose a variational sparse GP-based learning method that only gently increases the computational complexity. Experiments on both synthetic and real datasets demonstrate the superiority of the output-dependent GPSSM in terms of learning and inference performance.

In this work, we consider a downlink millimeter-wave (mmWave) communication scenario where a UAV base station (UAV-BS) equipped with a large-scale antenna array serves several ground users via hybrid beamforming. In order to save the power consumption of aerial UAV-BS, we consider the hybrid beamforming structure with fewer RF chains, and design the multi-user hybrid beamforming algorithm with the goal of minimizing the total transmit power. In order to ensure the quality of service (QoS) of ground users, the fairness transmission rate is considered. Furthermore, we take the resolution of analog phase shifter (PS) into account, and the beamforming problem turns into a mixed-integer nonlinear programming (MINLP). We also propose an improved Riemann gradient descent algorithm to solve the MINLP. Numerical results on the transmission power, beam pattern, and energy efficiency of the proposed algorithms are presented, showing that it allows hybrid systems to approach the performance of optimal fully-digital systems even PS resolution is constrained.

5G non-public network (NPN) can provide customized and dedicated network services for various vertical industries. The operation of 5G NPN is a crucial aspect for the deployment and application of 5G NPN. This paper studies the development of 5G NPN operation. Furthermore, this paper proposes a three-stage evolution path, framework and the guaranteed requirements for 5G NPN operation. Some examples are also provided to achieve NPN optimization goal by the framework. The paper provides insights and guidance for the vertical industries of 5G NPN operation, as well as suggests potential directions for future work on 5G NPN operation.

The construction of a smart campus has become an important part of educational informatization and a significant indicator of the educational modernization. Campuses across China are moving from traditional digital campus construction to smart campus construction. Chinese telecom operators, with their innate advantages in network infrastructure and resource, have become important service providers in the construction of smart campus. Telecom operators have effectively explored the application platform and application scenarios of smart campuses by leveraging existing educational service products and big data resources at their disposal.

Due to the implementation of national security-related laws and regulations, data privacy protection and ownership issues have attracted much attention, meanwhile, the rise of technologies such as 5G, IOT, big data, and edge computing has promoted the digital transformation of data as a factor of production to empower social governance. At present, traditional machine learning still uses the method of data-centered large models for training and reasoning. This method brings about data fragmentation and island distribution and other problems, which have become the key problems restricting the popularization of Artificial Intelligence (AI) technology applications. In this paper, we explore a federated learning model for user complaint warning algorithm based on big data and other enterprise side data. The experimental result also shows that the prediction accuracy of the federated learning model and the traditional logistic regression model are within an acceptable range on the premise of ensuring user privacy and data security.

The vigorous development of the digital economy brings new opportunities for enterprise digital transformation. This article proposes a knowledge-sharing-based digital consulting capacity platform, focusing on important digital transformation concerns in the consulting profession. By sorting out the practical problems and challenges, the optimization path of constructing the digital consulting capability-sharing platform for the intelligent city field is explored. Seven business centers are built to gather digital resources such as policies, industry trends, and market information. Knowledge precipitation is more convenient and comprehensive, and business management is more scientific. This paper analyzes the framework of the digital consulting capability platform in detail. Furthermore, this paper carries out the actual deployment and verifies the effectiveness of the digital consulting capability platform scheme based on knowledge sharing.

With AI and big data technologies, telecom operators are looking to change the traditional O&M model from reactive problem handling to proactive prevention and prediction. This paper proposes a model framework trained on multiple data sources for the 5G wireless network to support proactive O&M tasks based on complaint prediction. By grouping user complaints into base station complaint prediction, the model enhanced precision scores while maintaining high recall scores. The model has been integrated into the operator’s work order system to support intelligent operational optimization workflow.

When the data service latency of mobile network is too large, it will cause problems such as slow page opening, game stuck, video stuck and seriously affect user perception. Therefore, optimizing the network and reducing latency become one of the main tasks in mobile network. This paper researches on the analysis method of 5G data service latency problem. A set of analysis methods, which are for problem demarcation and localization, are provided to support network operation and maintenance personnel in improving user perception, focusing on the key performance of wireless and core network networks that affect the service.

With the improvement of the mobile communication market, the smart home business of telecom operators is showing unprecedented development and contribution. With the full-service competition in the domestic telecommunications industry, operators are facing increasing competitive pressure. The home service has become one of the important competitions for operators. In order to solve the user’s relationship recognition problem, this paper compares five commonly used machine learning methods: logistic regression, decision tree, random forest, LightGBM and multi-layer perceptron. Based the experiments on the data set, the results demonstrate the efficient of the LightGBM method. Finally, this paper selects the LightGBM method as the technical method for user relationship recognition in the business.

Visual Question Answering (VQA) is a recent hot topic that involves multimedia analysis, computer vision (CV), natural language processing (NLP), and even a broad perspective of artificial intelligence, which is challenging and has obtained increasing attention. VQA needs a complete understanding of the spatial relationship, textual clues, as well as the common sense for an actual image. However, most existing approaches simply embed and concatenate the features of questions and images to predict answers. Treating all embeddings equally without consideration of relation consistency hinders the model performance. In this paper, we propose an explicit Multi-Granularity Cross-Attention network (MGCAN) that mutually learns the multi-modal branches. MGCAN jointly matches word-level representation with whole image, and patch-level representation with the whole question that infers the high-order vision-semantic relationship. Experiments conducted on VQA datasets demonstrate that the proposed MGCAN outperforms previous baselines. The cross-attention mechanism explicitly exploits the relevant visual and textual clues that lead to superior prediction.

Efficiently utilizing beam resources in multi-beam satellite systems is crucial for improving system performance. Existing beam-hopping allocation algorithms focus solely on Quality of Service (QoS) objectives, such as time delay, transmission rate, and system capacity, while overlooking the interference caused by large Low Earth Orbit (LEO) constellations on Geostationary Earth Orbit (GEO) satellite systems. Although beam shutdown and power reduction are common methods to mitigate interference, they can adversely impact the quality of service and communication coverage in areas. To address this issue, we propose a deep reinforcement learning based interference avoidance beam-hopping allocation algorithm(DRL-IABHA). The article proposes an IABHA optimization approach to maximize system throughput in a multi-beam satellite system under interference avoidance conditions. To handle the time-dependent nature of this problem, it is modeled as a Markov decision process (MDP), which is commonly used in deep reinforcement learning (DRL) analysis. The MDP state is transformed into an image and features are extracted using convolutional neural networks. Simulation results indicate that the DRL-IABHA algorithm can enhance system capacity by 22.94% and prevent interference with GEO ground stations more effectively than other beam assignment techniques

This paper proposes a task-oriented semantic communication system for electrocardiogram (ECG) signal classification, called ECG-SC-DARTS. Based on deep learning, this system adopts the differentiable neural architecture search (DARTS) to automatically design the neural architecture of the semantic encoder under various channels. This paper improves the performance of the original DARTS by introducing a new recurrent neural network (RNN) cell with residual structure and a noise adding scheme for skip-connections. The RNN cell enhances the temporal semantic information extraction ability while the added noise reduces the risk of performance collapse caused by skip-connections. Experimental results demonstrate that ECGSC-DARTS generates appropriate neural architectures for the semantic encoder under AWGN, Rayleigh and Rician channels and these architectures outperform a number of baseline models, such as the original DARTS, fully convolutional network, multi-layer perception, and ResNet, regarding F1-score. Moreover, ECGSC-DARTS is more reliable than the traditional communication system in harsh channel environment.

Software products have brought convenience to modern society but also pose significant security risks due to various types of vulnerabilities. Identifying vulnerability names is vital for program repair and software maintenance, but the lack of training data presents a challenge. Big data analytics and machine learning can help overcome this challenge by processing large amounts of data and improving the accuracy of vulnerability name prediction. Considering that the data is often from datasets composed of multiple sources, a feature-based or attention-based multi-source domain adaptation (MSDA) approach is required. In this paper, we propose an MSDA method based on both feature and attention to accomplish the task of predicting vulnerability names, called Multi-Source Domain Adaptation for Vulnerability Name Prediction (MSDA-VNP). First, MSDA-VNP reduces domain divergence by adversarial training and then uses domain-invariant features to obtain feature correlations between individual source and target domains. In combination with the obtained domain correlations, Weighted multi-kernel Maximum Mean Discrepancy (WMK-MMD) is proposed as the attention mechanism. Second, a data augmentation strategy is employed to enhance MSDA-VNP to identify privacy-related vulnerabilities. To evaluate our approach, we conducted experiments on eight Java real-world projects in the Software Assurance Reference Dataset (SARD). The experimental results show that the proposed method MSDA-VNP performed efficiently and stably for the 44 types of vulnerabilities involved. The data augmentation strategy has also been proved to be effective as an enhancement for the proposed method MSDA-VNP.

In recent years, 5G/5G-A technology has fast developed and found widespread deployment, meeting the diverse requirements of application scenarios across various industries. In this paper, we introduce the principle and advantages of 5G/5G-A private network. Then, we introduce the construction of 5G/5G-A private network. Furthermore, we design an intelligent operation system of 5G/5G-A private network, which includes six key modules with over twenty functionalities. This intelligent operation system can effectively support the operation of 5G/5G-A private network. Lastly, this paper introduces the 5G/5G-A private network applications in a realistic vehicle factory.

Customer churn prediction is critical to an enterprise. Therefore, the improvement of the churn prediction model can directly help enterprises to better manage customers to obtain more profit. Meanwhile, the explanation of the decision-making mechanism of the churn prediction model can guide enterprises to accurately understand and evaluate the likelihood of customer churn, and then make more targeted measures to prevent customer churn. However, most existing deep learning approaches have poor model interpretability due to the black-box characteristics of neural networks. In addition, there are few previous studies on the model interpretability of customer churn predictions. Accordingly, in this paper, we propose a novel model named Interpretable DeepFM (iDeepFM) to study the model interpretability. The Proposed iDeepFM model introduces multi-head self-attention mechanism to the embedding layer, the linear interaction component, and the deep component. Especially, the deep component computes the high-order feature interactions by stacking multiple attention blocks. Furthermore, we conduct extensive experiments using two real-world telecom customer churn datasets. We calculate attention scores which are used to interpret the prediction results for each component. The results show that the proposed approach not only improves the performance of DeepFM but also offers good model interpretability.

In quantitative trading, stock prediction plays a crucial role in portfolio optimization as it directly impacts the actual level of return. However, the trading market is complex, making return prediction a challenging task. To address this issue, existing works have utilized various technical indicators as inputs to enhance predictive accuracy. However, these indicators are often proprietary and kept confidential by quantitative funds and researchers, limiting their accessibility. In this paper, we propose a federated learning-based method that leverages multiple parties’ technical indicators for stock return prediction without disclosing them. The results demonstrate that the proposed method outperforms traditional methods in terms of prediction accuracy. Additionally, the proposed method achieves higher portfolio return through portfolio optimization using the Mean-variance Optimization model compared to traditional approaches. The proposed method offers a promising solution for stock return prediction while maintaining the confidentiality of technical indicators.

The growth analysis of enterprises is an important basis for predicting the future development trend of enterprises. For an enterprise itself, the enterprise growth analysis can help the enterprise to understand its own business situation. It can also assist the enterprise to accurately customize the development strategy. As far as the investment market is concerned, the enterprise growth analysis can help investors comprehensively understand the investment target and reduce the investment risk as well as improve the investment benefit. This paper makes a comparative analysis on the growth of 4937 enterprises with all A-shares in different industries from seven dimensions, including competitiveness, profitability, operation ability, debt paying ability, R & D ability, scale expansion ability, enterprise supply chain ability. This paper reveals that there are significant differences in the growth of enterprises in different industries in the post-epidemic era.

Virtual Network Function (VNF) placement in NTNs is challenging because Non-Terrestrial Networks (NTNs), such as satellite networks, have limited resources regarding computational power and rate. However, existing solutions do not consider satellites’ resource constraints and the bandwidth constraints of links, which are essential metrics for designing VNF placement strategies in NTNs. Utilizing Network Function Virtualization (NFV) technology to deploy related network services on satellites in VNFs is a reasonable way. This paper focuses on delay-aware VNF placement in 6G NTNs to meet the ultra-low delay requirements of different applications. In addition, we also consider how to improve the resource utilization of servers to eliminate the resource bottlenecks of resource-constrained 6G NTN facilities. Then we formulate the VNF placement problem as a weighted graph-matching problem, aiming to maximize resource utilization. We propose the Linear Programming based algorithm and the Hungarian-based algorithm to solve the VNF placement problem. Evaluation results show that our proposed solutions outperform the benchmarks regarding resource utilization and execution time.

Benefiting from the development of satellite on-board processing capability, the orbital computing can be realized by deploying edge computing servers on satellites to reduce the task processing latency. However, edge computing based on geostationary Earth orbit (GEO) or low-Earth orbit (LEO) alone can hardly meet the latency requirements of Satellite-assisted Internet of Things (SIoT) services. Moreover, the uneven distribution of tasks generated by SIoT devices will also cause the load unbalancing among different satellites. In this article, hybrid GEO–LEO SIoT networks is investigated with joint computing and communication resource allocation. To tackle the load unbalancing problem, tasks generated by SIoT devices can be processed by collaborative LEO satellites or forwarded to gateways on ground via GEO satellite. Thus, the joint task offloading, communication and computing resources allocation for the hybrid SIoT network can be formulated as a mixed integer dynamic programming problem with satellites-ground cooperation and intersatellite cooperation via the intersatellite links. Then, an intelligent task offloading and multidimensional resources allocation algorithm (TOMRA) is proposed to minimize the latency of task offloading and processing. First, a method base on deep reinforcement learning is utilized to solve the subproblem of task offloading and channel allocation. And then, convex optimization is adopted to solve the subproblem of computing resource allocation under fixed offloading and channel allocation decisions. Simulation results show that the proposed TOMRA can achieve better performance than the reference schemes.

How to achieve reliable communication and safety monitoring is very important in coal mines. However, most of the existing transmission strategies and sensing-based monitoring approaches assume a single objective and neglect non-line-of-sight (NLOS) problems brought by winding tunnels or mine collapses. To this end, we first propose a multihop reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) approach to maximize the energy efficiency of the JCAS access point and the sum sensing rates in order to improve the sensing accuracy. Specifically, we formulate an energy-efficient optimization problem by jointly designing both the phase-shift matrix and the switches status of the RISs as well as the transmit power of the access point. The problem is solved by adopting the successive convex approximation-based alternating optimization algorithm, the second-order optimization method, the Lambert-$w$function, and Newton’s method. Moreover, a sensing-based rate optimization problem is also solved via the Lagrange relaxation method and the Gradient descent method. Simulation results demonstrate that the proposed algorithm has better robustness and higher energy efficiency.

Reconfigurable intelligent surface (RIS)-aided symbiotic active/passive transmission is a promising communication paradigm, which is able to improve the propagation environment while transmitting additional information. In this article, a novel scheme, termed RIS-aided number modulation (RIS-NM), is proposed for symbiotic active/passive communications. In RIS-NM, the RIS elements are divided into in-phase (I-) and quadrature (Q-) subsets depending on their phase shift configurations, and the number of elements in the I-subset (or Q-subset, equivalently) is used to convey the RIS’s private information. A low-complexity yet near-optimal detector is designed for RIS-NM by shrinking the search space of constellation points. We then investigate a special case of RIS-NM, termed RIS-aided number shift keying (RIS-NSK), in which the radio-frequency source transmits unmodulated carrier signals. Statistic channel state information (CSI)-based maximum-likelihood (ML) detection is developed for RIS-NSK. We analyze the bit error rate (BER) performance of RIS-NM/NSK over Rician fading channels. BER upper bounds are derived in closed form for RIS-NM by assuming instantaneous CSI-based ML detection, while an approximate BER expression is obtained for RIS-NSK by assuming statistic CSI-based ML detection. Furthermore, we extend RIS-NM to multiple-input multiple-output scenarios. Our simulation results in terms of BER corroborate the performance analysis and the superiority of RIS-NM over the state-of-the-art RIS-aided symbiotic active/passive transmission scheme.

Existing localization methods that intensively leverage the environment-specific received signal strength (RSS) or channel state information (CSI) of wireless signals are rather accurate in certain environments. However, these methods, whether based on pure statistical signal processing or data-driven approaches, often struggle to generalize to new environments, which results in considerable time and effort being wasted. To address this challenge, we propose MetaLoc, which is the first fingerprinting-based localization framework that leverages the Model-Agnostic Meta-Learning (MAML). Specifically, built on a deep neural network with strong representation capabilities, MetaLoc is trained on historical data sourced from well-calibrated environments, employing a two-loop optimization mechanism to obtain the meta-parameters. These meta-parameters act as the initialization for quick adaptation in new environments, reducing the need for much human effort. The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately. Furthermore, the advanced distributed paradigm modifies the vanilla MAML loss function to ensure that the reduction of loss occurs in a consistent direction across various training domains, thus facilitating faster convergence during training. Our experiments on both synthetic and real datasets demonstrate that MetaLoc outperforms baseline methods in terms of localization accuracy, robustness, and cost-effectiveness. The code and datasets used in this study are publicly available at:https://github.com/WU-Dongze/MetaLoc.

We present a many-to-one distributed semantic communication system for multivariate time series classification. The system adopts a federated learning-based architecture to achieve low-redundancy collaborative inference, where an unsupervised auxiliary task is designed to coordinate the feature vectors at different dimensions between semantic encoders and the classifier. For each transmitter, we design a scale-adaptive semantic encoder by applying weighted sum to a number of predefined convolutional layers. The scale-adaptive semantic encoder can extract multi-scale features from time series following various distributions. A dynamic channel encoder is developed to adapt to the scale-adaptive semantic encoder, converting semantic features to complex symbols appropriate for wireless transmission. For the receiver, we apply the same scale-adaptive structure to the semantic decoder to extract multi-scale semantic features from all transmitters for accurate classification. Simulation results show that the proposed distributed semantic communication system outperforms two baseline systems under AWGN, Rician, and Rayleigh channels and achieves excellent Top-1 accuracy performance on three UEA2018 datasets, especially when the signal-to-noise ratio is low.

The past few years have witnessed an exponential growth of diverse Internet of Things (IoT) devices as well as compelling applications ranging from industrial production to medical care. Dramatic advances in IoT technology not only brought enormous economic opportunities but also challenges (e.g., privacy and security vulnerabilities). Recently, with the appearance of blockchain technology, the integration of IoT and blockchain (BCoT) is considered a promising solution to address these issues. Blockchain provides a secure and scalable data management framework for IoT devices. However, the huge computation and energy cost of the consensus process in blockchain prevents it from being directly applied as a generic platform. To overcome this challenge, in this article, we propose a cloud mining pool-aided BCoT architecture, where the IoT devices can rent the computing resources from the cloud mining pools to offload the mining process. Based on this architecture, we study the mining pool selection problem and analyze the colony behaviors of IoT devices with different pooling strategies. We propose a centralized evolutionary game-based pool selection algorithm for the sake of maximizing the system utility. Considering the non-cooperative relationship among multiple miners, we also propose a lightweight distributed reinforcement learning algorithm, named the ‘WoLF-PHC’ algorithm.

The ground-to-satellite communication network (G2SN) has highlighted the significance of constructing ubiquitous and seamless networks for the next-generation communication system. However, in the presence of secret eavesdroppers, securing massive transmission links is posing tremendous challenges for G2SNs. In this paper, we propose an aerial assistant scheme to safeguard legitimate transmissions in G2SNs, where multiple unmanned aerial vehicles (UAVs) are deployed between the ground users and the satellite. With the assistance of flexible UAVs and the directivity of directional antennas, the constructed link can significantly reduce the risk of wiretapping, resulting in the improvement of security. Furthermore, to evaluate the performance of G2SNs, we introduce the eavesdropping probability and link connectivity as metrics. With the comparison of the non-protection scheme, we validate the effectiveness of our aerial assistant scheme. Finally, we present useful insights into practical deployment by revealing the relationship between the performance and other parameters, such as antenna beamwidth, deployment height and density of UAVs.

The existing indoor fingerprinting methods based on received signal strength (RSS) are rather accurate after intensive offline calibration for a specific scenario, but the well-calibrated localization model (can be a pure statistical one or a data-driven one) will present poor generalization ability in a new scenario, which results in big loss in knowledge and human effort. To break the scenario-specific localization bottleneck, we propose a new-fashioned data-driven fingerprinting method for localization based on meta-learning, named by MetaLoc, that can adapt itself rapidly to a new, possibly unseen, scenario with very little calibration work. Specifically, the underlying localization model is taken to be a deep neural network (NN), and we train an optimal set of group-specific meta-parameters by leveraging historical data collected from diverse well-calibrated indoor scenarios and the maximum mean discrepancy criterion. Simulation results confirm that the meta-parameters obtained for MetaLoc achieves very rapid adaptation to new scenarios, competitive localization accuracy, and high resistance to significantly reduced reference points (RPs), saving a lot of calibration effort.

Massive MIMO (Multiple Input and Multiple Output) is a key technology for improving 5G (the 5thGeneration) system capacity and spectrum utilization. This paper introduces the basic principles of Massive MIMO. Then, this paper proposes a novel LSTM-PD (LSTM and Periodic Decision) algorithm. The proposed LSTM-PD algorithm belongs to the category of periodic decision method with predictive properties. In addition, we also design a monitoring exit mechanism to improve the entire algorithm. The current network data results show that when the physical resource block (PRB) utilization rate is greater than 30%, the spectral efficiency of the LSTM-PD algorithm is significantly higher than that of the traditional algorithm. In addition, when the PRB utilization reaches 60%, the CPU utilization of the LSTM-PD algorithm can be reduced by nearly 30%, compared with the traditional algorithm.

Due to inadequate management of Vehicular Ad hoc Networks (VANETs), malicious nodes could participate in communications along with misbehavior, e.g., dropping packets and spreading fake information. Therefore, it is essential to detect misbehavior of internal attackers that will cause network performance degradation (e.g., taking longer time to receive messages or reaching destinations with detours). Apart from the capture of dynamic network topology of VANETs, the social relationship among nodes can also be applied as a relatively stable metric to qualify nodes. This paper proposes a misbehavior detection mechanism based on social relationships, from which nodes determine trust for the receiver or transmitter. Based on the proposed mechanism, road traffic control applications can avoid the interference from malicious nodes. The construction of social relationships depends on the geographic information reflected by the movement of nodes, including contact frequency and trajectory similarity, since the geographic information can accurately indicate the relevance among nodes. In addition to the social relationship, the proposed mechanism also evaluates the data trust from time and spatial factors to reduce the interference of fake data. Finally, the proposed mechanism integrates data trust and social relationships to enable misbehavior detection decisions. Extensive results of simulations show that the proposed mechanism has outstanding malicious nodes detection rates under various proportions of malicious nodes and movement patterns.

With the gradual commercialization of 5G VoNR, VoLTE and VoNR will become the main methods of voice services. How to efficiently evaluate the quality of voice service is the focus of telecom operators. This paper proposes an intelligent combined evaluation method of VoLTE and VoNR voice quality based on artificial neural network. In the proposed method, the artificial neural network model is fitted by the call level time slice sample data of voice, and then the prediction model is established. The prediction results of voice quality of mobile networks are obtained by using the prediction model at call level, grid level and area level. Meanwhile, the proposed method can address the shortcomings of traditional evaluation method based on road test, such as high cost, low timeliness and limited area. Finally, through theoretical verification and comparison with the real test results, the effectiveness of the prediction method is verified.

With the rapid development of mobile network, the use of mobile phones has become popular. People use mobile phones every day to surf the Internet, shop, socialize, work, etc. In the process of using mobile web services, users may be dissatisfied with the service perception, such as voice connectivity, Internet access, Slow Internet access and other common problems. If the customer is not satisfied with the communication service, the customer can usually complain about the quality of the communication service, so the frequency of the customer complaint has become an important evaluation index for the management of the operator. The quantity and frequency of customers ‘complaints about telecommunication service are increasing gradually, which brings challenges to the service quality and efficiency of telecommunication operators. This paper presents a methodology for customer complaints. The analysis system is based on the data of Horizontal pull- through, combined with big data analysis model, focus on the user’s response to the Internet slow, Internet access, voice access issues such as real-time positioning analysis, to provide customers with the first time solutions.

In order to meet the needs of data analysis and visualization to assist mobile network operation decision-making, telecom operators have established several mobile network index analysis tools or platforms. However, the network data analysis efficiency of planning, construction, maintenance and optimization is still low, and demand-oriented visualization means are still insufficient. This paper designs a mobile network data analysis and visualization system. The designed system aims at addressing the problems that mobile network has various types of data. The designed system can make data easy to manage, improve the data analysis efficiency and the flexibility of data visualization for telecom operators.

Future networks are towards autonomous, with a high level of automatic and intelligent abilities. There are several domains and layers in operator networks, malfunctions can be transmitted from lower layers to upper layers, and from one domain to another domain. At present, cross-domain network malfunctions are mainly relied on the operation and maintenance staff of each professional network to analyze and dispatch orders, resulting in repeated orders and increased human cost. The first and important step of malfunction diagnosis is the construction of network topology. However, cross-domain network topology cannot be associated automatically at present. Based on the performance data, a new method using AI technologies is proposed in this paper, which can associate the connecting cross-domain network ports automatically. The principle is that a same time sequence similarity is shared by the connected ports. Taking the data from real networks and comparing with the existing topology, the connecting relations can be 100% correctly recognized. This method can be widely used to any cross-domain networks, without changing current network equipment.

The 6G communication system is expected to achieve seamless global coverage. The orthogonal time-frequency space (OTFS) is generally considered as the main candidate waveform for 6G communication. Especially, in the air-space-ground integrated communication system, OTFS is more suitable for air interface modulation waveforms for high mobility communication scenarios than OFDM system. This paper focuses on OTFS technology, which conveniently adapts to the channel constantly changing via modulating information. In the paper, comparative analysis under different rate scenarios is performed, and potential future application scenarios are proposed, such as applying artificial intelligence based on vehicle network.

Aircraft Communications Addressing and Reporting System (ACARS) has been widely used in aviation datalink. However, for lack of security designs, ACARS faces increasing security threats such as eavesdropping and message injection. Although several security solutions has been proposed on aviation surveillance message, such as Automatic Dependent Surveillance-Broadcast, those on ACARS have received far less attention. To further improve the session security and privacy of civil aviation users, we put forwards a compatible protocol for ACARS datalink to protect message security as well as aircraft identity privacy. The proposed solution provides communication confidentiality, and supports data integrity and user identity verification. Meanwhile, by replacing the aircraft’s identity transmitted in plaintext with a variable anonymity, the privacy of an aircraft is protected from the disclosure of aircraft identity. Moreover, our protocol is compatible with current ACARS standards, making the proposed solution easy-to-deploy and practical. Formal analysis and simulations are carried out to make sure the security of proposed protocol.

With the trend of 5G-A (5G-Advanced) harmonized network communications and sensing harmonized communication and sensing, network capability exposure technology will help operators, business providers and 3rd business parties to realize harmonized network communications and sensing harmonized communication and sensing business and applications. This paper will focus on capability exposure technology based on 5G-A harmonized communication and sensing. Initially, this paper discusses the capability exposure hierarchical architecture based on harmonized communication and sensing, secondly proposes the harmonized communication and sensing network architecture and basic network signaling process combined with capability exposure technology. Then, this paper discusses capability exposure application scenarios based on communication sensing. This paper can provide relevant reference for the technological evolution, network deployment and application discussion of the harmonized communication and sensing capability in the operator network.

MDT data have been widely used for 4G/5G wireless network coverage estimation. Whereas the sparsity of the MDT data makes coverage rate bias when it applied into realistic network coverage analysis. To achieve a more precise coverage estimation, this paper proposes an approach that adding geographical and landform information to network coverage estimation in order to refine the coverage rate. An attention U-Net model was applied to landforms recognition from online satellite map with low cost. It can effectively assists telecom operators to filter out areas, which are users inaccessible or do not require signal coverage.

Recently, vehicle classification is becoming increasingly important with the development of automated driving technology. In particular, it can provide the basis and prerequisites for autonomous vehicles to make decisions in terms of improving driving safety. However, the current mainstream vehicle classification methods are deep learning algorithms based on Convolutional Neural Networks (CNN), which are mainly focused on the cloud, and these algorithms have complex models and large training parameters. In addition, for computationally intensive and urgent tasks, the poor computational power and low storage capabilities of edge nodes cannot support CNN-based vehicle classification algorithms for model updating. In this paper, we propose a lightweight vehicle classification method with mobile edge computing based on Broad Learning System (BLS). On the one hand, the vehicle can serve as a mobile edge computing node to provide computing and storage resources to ensure that classification tasks are performed locally and quickly, avoiding the bandwidth congestion caused by uploading to the cloud. On the other hand, we use broad learning method to perform incremental training on the data, which is more suitable for computing at the edge, because it can support incremental updates to the model on the vehicular edge nodes without retraining the whole model. Experiments are conducted on a Raspberry Pi system to simulate edge nodes, the results show with a similar performance, the training speed of our vehicle classification system can be increased by 10 times compared with the other CNN-based algorithms.

The feedback of service quality comes from customers is an important information for mobile operators, and the value of different user contacts varies greatly. This paper tries to integrate this contact information with telecom operator’s business and signaling data, and then realize the digital mapping from user experience to operation and network problems. Our aim is diagnose the root cause of the problem and then provide the systematic solution. This paper proposes a systematic and package solution for collaborative improvement of user experience and network quality, driven by user’s contact information with operators. Meanwhile, this paper also proposes three method to help telecom operators to repair user’s stickiness step by step, and improve the mobile network quality synchronously.

With the development of 5G and AI technology, the infectious virus detection framework system based on the combination of 5G MEC and medical sensors can effectively assist in the intelligent detection and control of influenza viruses such as COVID-19. Employing the edge computing and 5G+MEC model, the virus AI model is trained for the collected influenza virus data. Then the virus AI model can be used to evaluate the virus patients on the local edge computing service platform. Therefore, this paper introduces an algorithm and resource allocation, which uses 5G functions (especially, low latency, high bandwidth, wide connectivity, and other functions) to achieve local chest X-ray or CT scan images to detect COVID-19. Meanwhile, this paper also compares the computational efficiency of different algorithms in the 5G edge AI-based infectious virus detection framework, in this way to select the best algorithm and resource allocation.

This paper investigates on the remote interference problem in the TDD network and proposes an intelligent 5G Remote Interference Avoidance and Clustering Scheme (RIAC), on the basis of RIM-RS (remote interference management-reference signal) and clustering algorithm. This paper adopts the GBLA-DBSACN (the grid-based local adaptive DBSCAN) algorithm based on the traditional DBSCAN algorithm (Density—Based Spatial Clustering of Application with Noise) to improve the accuracy of interference base station (BS) clustering, which considers the dispersion of interference sources. This scheme helps to locate interference problems and potential sources through testing in the existing network quickly and effectively. By taking corresponding optimization means for these problems, network operators can effectively reduce the interference level in the target area and improve the quality of network construction.

With the continuous expansion of network construction, 4G/5G networks have gradually developed into hybrid multi-frequency heterogeneous networks, while the difficulty of inter-RAT mobility assurance is gradually increasing. Traditional interoperability optimization requires enormous labor costs, and the accuracy is low. This paper proposes an AI-based collaborative optimization scheme under multi-frequency heterogeneous 4G/5G networks based on the XGBoost prediction model and DNN algorithm. It aims to comprehensively improve the performance of different users in multi-frequency heterogeneous 4G/5G networks in terms of 4G/5G neighborhood re-organization and intelligent optimization of 4G/5G interoperability parameters. The results show that the proposed scheme has high accuracy and strong generalization, which is critical in improving user mobility perception under complex network structures. The scheme contributes to the network operators’ efficiency improvement and intelligent transformation process.

Enterprises loss is a growth strategy, in which enterprises migrate across regions/cities to adapt to the changes of internal and external environment, in this way to seek new development space and further reach the growth again. As the carrier of local economic development, the transfer of enterprises from one region to another undoubtedly means the loss of regional resources for the region. This paper takes large- scale enterprises as the research object. Then, this paper uses questionnaire data and statistical data, and adopts the combination of PCA algorithm and extreme value standardization method to comprehensively evaluate the loss probability of enterprises. This method will reflect the loss tendency of enterprises in the region, and make an empirical analysis on the large-scale enterprises in region, in this way to help regional managers have an early insight into the loss tendency of enterprises in the region. Finally, it will provide a reference for stabilizing the regional economy and help reduce the loss risk of large-scale enterprises in the region.

With the rapid progress of the telecom industry and fierce competition among telecom operators, telecom companies pay more attention to customer retention. Telecom companies developed multiple solutions to predict churn customers before customers move to another telecom operator. However, the existing prediction solutions have some disadvantages in the real-world use cases. For example, churn definition is limited to moving from one telecom operator to another, which is too late for preventing customer churn. The main contribution of the paper is to introduce the new definition of customer chum for the telecom industry, and to propose a Half Termination Dynamic Label (HTDL) that improves the churn prediction solution with XGBoost. Experiment results showed that the proposed solution improved the model performance, which significantly outperforms traditional solution, in terms of churn prediction on F1-score. The new solution also sidelines more active customers for retention.

In the 5G era, mobile networks gradually become complex, and there are also high requirements for network operation and maintenance. As log data is important information to reflect the status of network devices, the monitoring of log data generated by network devices has become an important part of network operation and maintenance. But the massive amount of log data generated by large-scale network devices has already exceeded the range of human processing capabilities. And the introduction of artificial intelligence algorithms can optimize the detection of log anomalies and reduce network operation and maintenance costs under the challenges of high complexity of 5G networks. This paper proposes a Mahalanobis distance and Pauta criterion based log anomaly detection (MPLAD) algorithm for 5G mobile network. On the basis of solving the shortcomings of the existing log anomaly detection algorithms, it innovatively integrates the Mahalanobis distance algorithm and the Pauta criterion. Meanwhile, it also introduces the negative sample mechanism and the principal component analysis (PCA) method to achieve high accuracy, high efficiency and high compatibility towards 5G mobile network log anomaly detection.

The high popularity of 5G has spawned a large number of emerging application scenarios and diversified business models, meanwhile, it also leads to the increase in network capacity. The research on 5G network capacity has become an important topic to improve the user perception. This paper analyzes the future capacity trend and development characteristic model of 5G, and then determines the four dimensions for evaluating 5G network capacity. Based on each dimension, this paper locates key indicators, and creatively puts forward the concept of experience satisfaction. Furthermore, this paper researches and recommends the capacity expansion thresholds for 3.5G and 2.1G respectively, using the big data fitting method. In addition, this paper also finds the internal relationship between these key indicators, and give the recommended capacity expansion threshold for each type of cell. A reasonable and accurate capacity expansion threshold is can effectively use the limited capacity expansion investment as well as improve user perception of 5G network.

Unmanned aerial vehicle (UAV) assisted emergency communication is an important technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to forward information from the macro base station (MBS) to the users in emergency area. In this paper, the MBS power allocation, the UAV service zone selection, and the user scheduling are jointly investigated to enhance the sum spectrum efficiency. We formulate the MBS power allocation and UAV service zone selection problem as an Markov Decision Process (MDP) in the delay ignored system (DIS) and propose a deep reinforcement learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN). Then the proposed DRL-based scheme is extended in time delay system (TDS) to estimate the current optimal action with the outdated channel information. We also formulate the user scheduling as a 0-1 optimization problem and solve it by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme can effectively improve the spectrum efficiency compared with the existing schemes.

Due to the complicated and changing urban traffic conditions and the dynamic mobility of vehicles, the network topology can rapidly change which causes the communication links between vehicles disconnected frequently, and further affects the performance of vehicular networking. To overcome this problem, we propose a intelligent multi-attribute routing scheme (MARS) for two-layered software-defined vehicle networks (SDVNs). The proposed scheme is divided into two phases, the routing path calculation and the multi-attribute vehicle autonomous routing decision-making. In this paper, we construct the topology diagram in SDVNs for finding the efficient routing paths. To increase the packet arrival rate and reduce the end-to-end delay, an intelligent multi-attribute routing scheme is proposed by employing fuzzy logic and design a technique of order preference by similarity to ideal solution (TOPSIS) algorithm to find the next-hop forwarder. To solve the uncertainty problem of multiple attributes, we apply the fuzzy logic to identify the weight of each attribute in TOPSIS algorithm. Simulation results demonstrate that MARS can effectively improve packet delivery ratio and reduce average end-to-end delay in urban environments compared with its counterparts.

Network slicing is a key technology in 5G. Generally, 5G networks employ slicing technology to provide the isolated and customizable network services for different scenarios (e.g., different vertical industries, different customers, different businesses etc.) in the form of virtual industry private networks. 5G slicing has the potential to meet the individual requirements of users and services, in terms of bandwidth, delay, reliability, and mobility. This paper gives an overall introduction to network slicing management, end-to-end processes, and wireless slicing capabilities. Then, this paper carries on algorithm research for wireless RB resource reservation and QoS scheduling. On one hand, the proposed algorithm clarifies the specific scheme of wireless RB resource reservation. On the other hand, the algorithm provides QoS scheduling parameter configuration. This lays a solid foundation for the implementation of slice differentiation capabilities in 5G wireless networks.

Social network marketing is a very promising topic in the data operation work of telecom operators. Based on the big data collection and analysis of telecom operators, this paper presents a content recommendation scheme which considering both users' social relationships and users' personal preferences. Regarding users' personal preference analysis, this scheme uses DPI (Deep Packet Inspection) technology to obtain the user's personal preference tag and evaluate the user's preference index. In terms of user social relations, it integrates the analysis of mobile users' communication behaviors, temporal and spatial relationships, interaction circles and other related indicators. Logistic regression algorithm is used to illustrate the influence from a user to another. The preference recommendation scheme based on the mobile network user social circle proposed in this paper expands the value scenarios of operators' big data, integrates resources and channels, improves operators' data insight capabilities, and realizes the value mining and enhancement of operators' big data.

Psychological health of students has become a widespread social problem, while the management and assessment of college students' psychological health is still stay in passive and manual mode based on the traditional method. In this paper, we design a novel architecture for the prediction of college students' psychological health based on Multi-Source big data including Operation Support System big data, educational data and psychological health questionnaire data. Then we propose the Optimized Decision Tree using Multiple-Target Particle Swarm Optimization (DT-MTPSO) algorithm. Experiment shows that the proposed algorithm can solve the Multiple-Target problems effectively and has better performance in F1-score than traditional Decision Tree. In addition, the result of the features selection of DT-MTPSO for different targets shows the relationship between the psychological health level and behavioural characteristics of students for different evaluation indicators, providing guidance to the school managers and educational psychologist.

Data expansion is one of the commonly used steps in big data analysis applications. This paper proposes a data expansion method, which is based on operator data and considers multiple scenarios, multiple operating systems, and multiple operators in the target area. Factors such as the proportion of share and the difference in the proportion of users in the consumption power portrait are comprehensively expanded to obtain the full amount of user data of each target group in the target area. This method can be prepared to reflect changes in user data in time, and is applied to industries such as scene-based marketing and business planning.

With the rapid development of Internet industry, people are facing increasing challenge of information overload. Under this background, personalized recommendation has been comprehensively researched in order to provide a more time-saving and accurate way for information retrieval. In this paper, a novel hybrid recommendation scheme based on collaborative filtering and association rules is put forward to compensate the weaknesses of individual algorithms. This scheme is implemented through several steps. Firstly, it solves the problem of data sparsity with the help to association rules, and then employs the revised collaborative filtering to calculate the similarity among the items. Finally, it predicts user ratings for the unknown items based on item similarity and generates recommendation lists according to the prediction ratings. Experimental results show that the recommendation accuracy of this hybrid scheme has been dramatically improved compared to other traditional algorithms.

The economic expenditure of mobile network operators includes two parts, namely CAPEX and OPEX. CAPEX mainly includes the huge amount of capital invested in network infrastructure construction, while operating expenditure mainly includes expenditure for daily operation and maintenance. In order to achieve continuous coverage of wireless network, CAPEX needed for base station procurement is indispensable. Operators need to adopt more intelligent and scaled means to optimize the maintenance process of wireless network so as to better achieve the goal of cost reduction and efficiency increase. In this paper, a scheme of cell boundary prediction and base station location information verification based on machine learning is proposed, which innovatively introduces the machine learning algorithm into network optimization analysis and improve the verification efficiency and reduce the input of manpower.

5G mobile edge computing (MEC) can be used in intelligent manufacturing. In complex industrial application scenarios, this paper tries to address the problem of energy consumption optimization of customer task unloading and resource rescheduling. Specifically, we employ 5G wireless private network and MEC computing resources between 5G private network and MEC. Then, we use game theory algorithm to optimize the user task unloading and resource rescheduling allocation, which is mainly measured by the minimum total time required to complete the task and energy consumption. The problem is a combinatorial nonlinear programming algorithm, involving joint optimization of task offloading decision, user side's uplink transmission energy consumption, MEC server's resource allocation. The solution includes the resource allocation of fixed task unloading decision, and the resource allocation optimization of task unloading. Results verify the proposed solution can improve the efficiency of task scheduling.

The stability of passive antenna feed operation is an important indicator to measure the quality of wireless network. On the basis of big data of antenna and feed fault, this paper proposes a support vector machine (SVM) based fault classifier of antenna and feed, in order to quickly classify the faults of antenna and feed system (AFS). In addition, the improved Cascaded Pyramid Network (CPN) learning algorithm is employed to establish a fault diagnosis device of antenna and feed to quickly diagnose various categories of faults. For the fault model of antenna and feed, we continue to learn and train to optimize the fault classifier model, as well as the fault diagnosis model. For the fault diagnosis information, the antenna and feed fault classifier is used to update the classified faults, which empower the antenna and feed fault classification more accurate.

5G network consumes huge investment cost, including 5G network construction, 5G network operation and maintenance etc. Therefore, China Unicom and China Telecom take the initiative to jointly build the 5G network (wireless network sharing, independent construction mode of core network). Its aim is to reduce 5G overall investment cost, and rapidly realize the continuous and wide-area 5G service capability, as well as improve the network efficiency and asset operation efficiency. This paper focuses on the automatic data configuration model of 5G co-construction and shared base stations. By interacting with the core network and wireless network, this model can identify and match different 5G network modes such as SA and NSA (including dual-anchor scenarios and single-anchor scenarios). On this basis, the data required for automatic activation of the shared base stations is obtained from the wireless side and automatically configured on the core network side. This technology meets the practical needs of both sides for fast and efficient construction of 5G network. It can realize the automatic opening and fast on-line of co-construction and shared BS on the side of the core network.

Aiming at the current high energy consumption problem of the Internet of Things data center, and the static state of the traditional data center network architecture is not convenient to the design and deployment of energy consumption optimization strategies, this paper specifically proposes a new energy consumption optimization strategy under the Software Defined Network (SDN) architecture, the control plane is separated from the data plane. The control plane is divided into two layers, bottom layer and top layer. The bottom layer controllers are uniformly scheduled by the upper layer controller. In order to reduce the computational pressure of the upper-level controller and make full use of the computing power of the bottom level controller, the relationship between the bottom layer controllers in this article is non-cooperative. On this basis, two energy efficiency strategies are designed in this paper. The first one is Strategy For Nash Equilibrium Point (SFNEP). The second one is Strategy For Energy Optimal(SFEO). The target of SFNEP is to search for the Nash Equilibrium Point. The target of SFEO is to gain energy efficiency via running SFNEP. Through extensive simulations, we show that SFEO has a better energy consumption optimization effect than other benchmark strategies. Under ideal condition, energy saving ratio is able to reach 24%.

This paper studies the Internet characteristics of Internet media news based on three factors, including journalism, communication, statistical physics. By obtaining the indicators (e.g., news release time, title, text, media, media type, media weight, etc.), the news transmission speed evaluation system is constructed by employing clustering model, and the news transmission speed is further obtained. Through this indicator and system, it can effectively reflect the transmission speed and changing trend of news events, and monitor the spread situation of news events in real time. In addition, this paper provides a reference basis for the governance of network public opinion and the early warning as well as handling for crisis events.

This article studies the joint optimization problem of computation offloading and resource allocation (JCORA) in mobile-edge computing (MEC). Deep reinforcement learning (DRL) is one of the ideal techniques for addressing the dynamic JCORA problem. However, it is still challenging to adapt traditional DRL methods for the problem since they usually lead to slow and unstable convergence in model training. To this end, we propose a temporal attentional deterministic policy gradient (TADPG) to tackle JCORA. Based on the deep deterministic policy gradient (DDPG), TADPG has two significant features. First, a temporal feature extraction network consisting of a 1-D convolution (Conv1D) residual block and an attentional long short-term memory (LSTM) network is designed, which is beneficial to high-quality state representation and function approximation. Second, a rank-based prioritized experience replay (rPER) method is devised to accelerate and stabilize the convergence of model training. Experimental results demonstrate that the decentralized TADPG-based mechanism can achieve more efficient JCORA performance than the centralized one, and the proposed TADPG outperforms a number of state-of-the-art DRL agents in terms of the task completion time and energy consumption.

Beam hopping (BH) is the key technology to improve the system throughput and decrease the transmission delay in multibeam satellite systems. The objective of this study is to find a policy to maximise the expected long‐term resource utilisation. The BH illumination plan (BHIP) optimisation problem aimed at minimising the transmission delay is formulated and modelled as a partially observable Markov decision process. To tackle the issue of unknown dynamics and prohibitive computation, an artificial intelligence method named deep reinforcement learning (DRL) is first proposed to solve the BHIP problem in multibeam satellite systems. The proposed DRL‐BHIP algorithm considers a series of realistic conditions, including the traffic demands in spatial distribution and temporal variation, ModCod constraints, antenna radiation pattern and inter‐beam interference. The state reformulation concept is adopted to characterise the traffic spatial and temporal features. Simulation results show that the proposed DRL‐BHIP algorithm can decrease the transmission delay and improve the system throughput compared with existing algorithms.

Mobility load balancing MLB redistributes the traffic load across the networks to improve the spectrum utilisation. This paper proposes a self-organising cluster-based cooperative load balancing scheme to overcome the problems faced by MLB. The proposed scheme is composed of a cell clustering stage and a cooperative traffic shifting stage. In the cell clustering stage, a user-vote model is proposed to address the virtual partner problem. In the cooperative traffic shifting stage, both inter-cluster and intra-cluster cooperations are developed. A relative load response model is designed as the inter-cluster cooperation mechanism to mitigate the aggravating load problem. Within each cluster, a traffic offloading optimisation algorithm is designed to reduce the hot-spot cell's load and also to minimise its partners' average call blocking probability. Simulation results show that the user-vote-assisted clustering algorithm can select two suitable partners to effectively reduce call blocking probability and decrease the number of handover offset adjustments. The relative load response model can address public partner being heavily loaded through cooperation between clusters. The effectiveness of the traffic offloading optimisation algorithm is both mathematically proven and validated by simulation. Results show that the performance of the proposed cluster-based cooperative load balancing scheme outperforms the conventional MLB. Copyright © 2013 John Wiley & Sons, Ltd.

Traffic shifting is an efficient load balancing method to offload traffic from a hot-spot cell to neighbouring cells. This paper proposes a channel-aware optimised traffic shifting (COTS) scheme in LTE-Advanced relay networks. The COTS scheme employs a channel-aware assistant cell selection mechanism, which considers users' channel condition, received from the relay station (RS) in neighbouring cells, to select assistant cells and address the weak assistant cell problem. The optimal traffic offloading algorithm analyses and calculates the shifted traffic from the hot-spot cell to its assistant cells. Simulation results show that the COTS scheme can select a small number of neighbouring cells as assistant cells to effectively offload users, and can efficiently reduce the call blocking probability as well as the call dropping probability.

In order to deal with uneven load distribution, mobility load balancing adjusts the handover region to shift edge users from a hot-spot cell to the less-loaded neighbouring cells. However, shifted users receive the reduced signal power from neighbouring cells, which may result in link quality degradation. This paper employs a user relaying model and proposes a user relay assisted traffic shifting (URTS) scheme to address this problem. In URTS scheme, a shifted user selects a suitable non-active user as relay user to forward signal, thus enhancing the link quality of the shifted user. Since the user relaying model consumes relay user's energy, a utility function is designed in relay selection to reach a trade-off between the shifted user's link quality improvement and the relay user's energy consumption. Simulation results show that the URTS scheme can improve SINR and capacity of shifted users. Also, URTS scheme keeps the cost of relay user's energy consumption at an acceptable level.

Load balancing (LB) is an important function of the self-organizing network (SON) for coping with the uneven load distribution to achieve higher spectrum efficiency and lower operational expenditure. This paper proposes a cluster based self-organizing LB scheme, which employs a user-vote mechanism to avoid the ‘virtual partner’ problem experienced by current LB schemes with the load-based partner selection. The user-vote can assist the hot-spot base station (BS) to efficiently select partner BSs for constructing its cluster, and then shift the traffic to the partners within the cluster. Simulation results show that the proposed scheme can effectively solve the ‘virtual partner’ problem. Furthermore, it can reduce the call blocking rate via a small number of partner BSs.

Mitigating Inter-cell Interference (ICI) and ensuring seamless high-quality communication are two challenging issues for OFDMA systems. Cell-level coordinated resource allocation and handover (HO) are the two key technologies for achieving these goals. They have been investigated intensively, however, mainly separately. In this paper, a novel combined Handover Guarantee and Interference Mitigation (HGIM) cell-level resource allocation scheme is proposed. HGIM defines the Handover User Set (HUS) and grants higher allocation priority to handover users. Other active users are prioritized based on a unified cell division model which divides a cell into different ICI sensitive areas. Meanwhile, HGIM defines a Sub-carrier Preferred List (SPL) to optimize allocation. Simulation results show that HGIM achieves greater ICI mitigation and improved handover performance compared with the conventional soft frequency reuse scheme.