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Cellular networks are moving towards increasing heterogeneity by deploying more small cells into macro base station (MBS) to meet rapidly growing traffic demands. To leverage the advantages of these small cells, mobile users should be offloaded onto small base stations (BSs), which will typically be lightly populated and can give a higher data rate by presenting the mobile users with many more channels than the MBS. Likewise, a more balanced cell association will lessen the pressure on the MBS, allowing it to serve its remaining users more effectively. This paper addresses the cell association challenge for Quality of Service (QoS) provisioning in terms of throughput and load-balancing for 5G and future generation networks. This problem is quite challenging because BSs have varying backhaul capacities and users have varying QoS needs. Most of the previous studies are based on reference signal received power (RSRP), signal to interference and noise ratio (SINR) or its variants and most importantly majority of them are not load-aware. Therefore, a modified load-aware biased cell association scheme based on distance is proposed to attain better QoS provisioning in terms of throughput and load-balancing. Simulation results depict that the proposed load-aware-based method outperforms conventional cell association schemes based on RSRP and its variants, and in terms of throughput and load-balancing. Furthermore, the algorithm's complexity has been assessed through a comparison and analysis of computational time, demonstrating better performance compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Detecting coordinated attacks in cybersecurity is challenging due to their sophisticated and distributed nature, making traditional Intrusion Detection Systems often ineffective, especially in heterogeneous networks with diverse devices and systems. This research introduces a novel Collaborative Intrusion Detection System (CIDS) using a Weighted Ensemble Averaging Deep Neural Network (WEA-DNN) designed to detect such attacks. The WEA-DNN combines deep learning techniques and ensemble methods to enhance detection capabilities by integrating multiple Deep Neural Network (DNN) models, each trained on different data subsets with varying architectures. Differential Evolution optimizes the model's contributions by calculating optimal weights, allowing the system to collaboratively analyze network traffic data from diverse sources. Extensive experiments on real-world datasets like CICIDS2017, CSE-CICIDS2018, CICToNIoT, and CICBotIoT show that the CIDS framework achieves an average accuracy of 93.8%, precision of 78.6%, recall of 60.4%, and an F1-score of 62.4%, surpassing traditional ensemble models and matching the performance of local DNN models. This demonstrates the practical benefits of WEA-DNN in improving detection capabilities in real-world heterogeneous network environments, offering superior adaptability and robustness in handling complex attack patterns. [ABSTRACT FROM AUTHOR]

Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of covering a variety of nodes and edges. The members of these networks usually have metadata whose analysis can lead to the discovery of knowledge. One way to analyze such data is clustered where high-quality clustering requires effective similarity calculation. Most of the existing clustering methods do not pay attention to the use of metadata or the characteristics of network members. On the other hand, they are only able to process small and medium-sized networks due to the amount of memory and execution speed. This paper presents a hybrid approach for heterogeneous network clustering to overcome these problems. The structural similarity in this approach is calculated by the graph embedding method, which we call learning-based. Attribute similarity is calculated by a scoring method that we call similarity-based. In the experimental study, we compared the proposed method with collaborative approaches based on similarity on the real-world networks. The experimental findings demonstrate the superiority of the proposed method in terms of entropy, memory consumption, execution time, and density in certain cases. [ABSTRACT FROM AUTHOR]

This paper investigates rumor propagation in a multilingual environment, taking into account language usage variations. Firstly, a 2I2S2R model is proposed within a heterogeneous network framework that incorporates both immunologic and cross-transmitted mechanisms. Secondly, the paper calculates the basic reproduction number R0 by the next-generation matrix method. Thirdly, the local asymptotic stability and the global asymptotic stability are further explored, which indicate that whether the rumor continuously spreads or becomes extinct is determined by the threshold. Finally, the numerical simulation and sensitivity analysis are given to illustrate the effectiveness of theoretical results and the influence of model parameters on rumor spreading. [ABSTRACT FROM AUTHOR]

The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for achieving fair resource allocation in such network scenario. Hence, this study formulates the fairness-aware UA problem as a utility-based combinatorial optimization problem, which is computationally hard to solve. Hence, an efficient Binary Particle Swarm Optimization (BPSO)-based UA scheme is proposed to solve the problem. Through simulations of an O-RAN based HetNet with network slicing-enabled, performance of the proposed BPSO-UA scheme is compared against two other baseline UA schemes. Results demonstrate the effectiveness of the proposed BPSO-UA scheme in achieving high fairness through equitable network slicing resource allocation, thereby leading to higher user connectivity rate and comparable average spectral efficiency. This innovative approach sheds light on the potential of metaheuristic algorithms in tackling intricate UA challenges, offering valuable insights for the future design and optimization of mobile networks. [ABSTRACT FROM AUTHOR]

Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP's effectiveness in the prediction of LDAs. [ABSTRACT FROM AUTHOR]

Restoring clear frames from rainy videos poses a significant challenge due to the swift motion of rain streaks. Traditional frame-based visual sensors, which record dense scene content synchronously, struggle to capture the fast-moving rain information. Conversely, the novel bio-inspired event camera, known for its high temporal resolution and low latency, effectively records the motion trajectories of rapidly falling rain through asynchronously generated sparse event sequences. In light of these attributes, we introduce a novel event-driven convolutional spiking network for video deraining. For video restoration, we employ a Convolutional Neural Network as the backbone, extracting dense features from video sequences to map rain-soaked frames to clear ones. To remove rain, we meticulously design a bio-inspired Spiking Neural Network that adapts to the sparse event sequences, capturing features of falling rain. We then establish a bimodal feature fusion module that combines dense convolutional features with sparse spiking features. This fusion aids the backbone in accurately pinpointing rain streaks across spatiotemporal dimensions. Thus, our diverse network extracts and collaborates information from both events and videos, enhancing deraining performance. Experiments conducted on synthetic and real-world datasets prove that our network markedly surpasses existing video deraining techniques. [ABSTRACT FROM AUTHOR]

Abstract The study of drug–target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug–target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization.

Abstract A wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP.

Abstract The escalating demand for data in wireless communication systems has posed significant challenges in recent years. This trend is predicted to continue, with explosive data usage and evolving quality of service demands from mobile users. The rapid increase in traffic demand, combined with the intricate nature of heterogeneous network (HetNet) scenarios, has significantly heightened the challenges confronting mobile network operators. These challenges encompass service quality, load distribution, coverage, and the overall user experience. Conventional approaches that prioritize maximum received power in the cell association mechanism tend to sustain network imbalances within the HetNets, making it difficult to cater for the diverse traffic requirements of mobile users. In this study, instead of focusing solely on enhancing individual user downlink rates, we maximize the number of users whose downlink needs are satisfied by integrating a cell range extension (CRE) technique with an ant colony optimization algorithm. Our proposed method considers both the workload of base stations and the signal to interference-plus-noise ratio of user devices to formulate an objective function aimed at calculating specific CRE bias values for individual small base stations. A comparative analysis of the proposed approach with existing techniques demonstrates its effectiveness. Simulation results underscore the success of our proposed strategy in meeting users’ throughput needs while reducing network imbalances and call drop rates.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study of drug–target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug–target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization. [ABSTRACT FROM AUTHOR]

A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This breakthrough has paved the way for personalized cancer therapies. With this motivation, the proposed model constructs a heterogeneous network based on tumor sample-gene relation data and gene-gene interaction data. This network also incorporates tissue-specific gene expression and primary site-based gene counts as features, enabling tissue-specific predictions. Graph neural networks (GNNs) have proven effective in modeling complex interactions and predicting links within this network. The proposed model has successfully predicted tumor-gene associations by leveraging sampling-based GNNs and link layer embedding. The model's performance metrics, such as AUC-ROC scores, reached approximately 94%, demonstrating the potential of this heterogeneous network in predicting tissue-specific tumor sample-gene links. This paper's findings highlight the importance of tissue-specific associations in cancer research. Overview of our proposed work [ABSTRACT FROM AUTHOR]

The emergence of cancers involves numerous coding and non-coding genes. Understanding the contribution of non-coding RNAs (ncRNAs) to the cancer neighborhood is crucial for interpreting the interaction between molecular markers of cancer. However, there is a lack of systematic studies on the involvement of ncRNAs in the cancer neighborhood. In this paper, we construct an interaction network which encompasses multiple genes. We focus on the fundamental topological indicator, namely connectivity, and evaluate its performance when applied to cancer-affected genes using statistical indices. Our findings reveal that ncRNAs significantly enhance the connectivity of affected genes and mediate the inclusion of more genes in the cancer module. To further explore the role of ncRNAs in the network, we propose a connectivity-based method which leverages the bridging function of ncRNAs across cancer-affected genes and reveals the non-coding RNAs extended omnigenic module (NeOModule). Topologically, this module promotes the formation of cancer patterns involving ncRNAs. Biologically, it is enriched with cancer pathways and treatment targets, providing valuable insights into disease relationships. [ABSTRACT FROM AUTHOR]

The collective Unmanned Weapon System-of-Systems (UWSOS) network represents a fundamental element in modern warfare, characterized by a diverse array of unmanned combat platforms interconnected through heterogeneous network architectures. Despite its strategic importance, the UWSOS network is highly susceptible to hostile infiltrations, which significantly impede its battlefield recovery capabilities. Existing methods to enhance network resilience predominantly focus on basic graph relationships, neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS. To address these limitations, we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network (E-MAGCN), designed to augment the adaptability of UWSOS. Our approach employs BERT for extracting semantic insights from nodes and edges, thereby refining feature representations by leveraging various node and edge categories. Additionally, E-MAGCN integrates a regularization-based multi-layer attention mechanism and a semantic node fusion algorithm within the Graph Convolutional Network (GCN) framework. Through extensive simulation experiments, our model demonstrates an enhancement in resilience performance ranging from 1.2% to 7% over existing algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of heterogeneous network technologies, such as mobile edge computing, satellite communications, self-organizing networks, and the wired Internet, satisfying users' increasingly diversified and complex communication needs in dynamic and evolving network environments has become a critical research topic. Ensuring secure and reliable information transmission is essential for stable network operation in these complex environments. Addressing this challenge, this study proposed a secure and reliable multi-objective optimized multipath transmission algorithm for heterogeneous networks to enhance security and reliability during data transmission. The core principle of this algorithm was that multipath transmission can provide additional protection through redundant paths. This redundancy ensured that even if one path is attacked or fails, alternative paths can maintain data integrity and reachability. In this study, we employed the Optimized Non-dominated Sorting Genetic Algorithm II (ONSGA-II) to determine the range of the initial population and filter suitable paths by optimizing them according to different demand objectives. In the path selection process, we introduced an innovative deletion graph method, which ensures that redundant paths do not share any common links with the original paths, except when there are unique links. This approach enhances the independence of transmission paths and improves the security of the transmission process. It effectively protects against security threats such as single points of failure and link attacks. We have verified the effectiveness of the algorithm through a series of experiments, and the proposed algorithm can provide decision-makers with high-reliability and low-latency transmission paths in heterogeneous network environments. At the same time, we verified the performance of the algorithm when encountering attacks, which is superior to other classical algorithms. Even in the face of network failures and attacks, it can maintain a high level of data integrity and security. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP. [ABSTRACT FROM AUTHOR]

Stock prediction is typically a non-linear time series task. However, few researchers attempt to systematically reveal the underlying structure of the stock market through technical data. The interactions of collaboration or conflicts among various business domains can explain the fluctuations in stock. The incorporation of this additional information aids in predicting the future trends of stocks. In order to represent the trading situation of the stock market as realistically as possible and to express the explicit or implicit relationships between stocks, this paper proposed a stock price prediction model sDHN based on a dynamic heterogeneous network, which synthesized the base of the stock and the industry and geographical information, and modeled it as a dynamic heterogeneous network. The model introduced dynamic time series capabilities to the network, and the algorithm creatively combined four different technical levels of similarity graphs of stock nodes to generate a rich information heterogeneous graph. Finally, it aggregated the semantic information hidden in different meta-paths to generate embeddings, exploring the potential correlations among stocks from the perspective of the heterogeneous graph. In addition, experiments on three real-world stock data sets show that the proposed model achieves accuracy improvements of between 5% and 34% over the overall baseline models. The F1-score is higher by approximately 11.5% ~37%. It demonstrates through graphical analysis the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Academic collaboration is fundamental to the advancement of scientific research. However, with the growing number of publications and researchers, it becomes increasingly challenging to identify suitable collaborators. Academic collaborator recommendation is a promising solution to this problem. Traditional recommendation methods based on collaborative filtering suffer serious data sparsity. In recent years, network topology-based methods have shown good recommendation performance while alleviating the data sparsity issue to some extent by exploiting the relationships between nodes and their attributes. Nevertheless, these methods are typically based on homogeneous collaboration networks that consist only of scholar nodes and collaboration relationships, leading to suboptimal performance. In reality, collaboration involves many different types of nodes and relations that accumulate multiplex information. To address this issue, we construct a heterogeneous academic information network comprising four types of nodes: scholars, papers, organizations, and publication venues. An academic collaborator recommendation model is designed to capture multi-type attribute features and network topology features of nodes through metapaths based on the network. Specifically, the attribute features of nodes are embedded by a node type-aware embedding method. The topology features are then extracted through the node type-aware aggregation and metapath instance aggregation procedure. After that, we utilize a metapath aggregation method to gather different types of metapaths, each representing a factor that affects collaboration. Thus, the topology information and attribute information are preserved, while encompassing multi-type factors of collaboration. Finally, we compute the vector similarity to determine collaborators. Through rigorous experimentation on a large-scale interdisciplinary academic dataset, we found that the proposed model exhibits outstanding performance in practical applications. Unlike traditional approaches confined to homogeneous collaboration networks, our model delves deeper by mining and leveraging diverse node attributes and multiple collaboration influencing factors. This approach significantly enhances the accuracy and effectiveness of collaborator recommendations. Ultimately, we aspire to contribute to a more efficient and accessible platform that simplifies the search for suitable collaborators. [ABSTRACT FROM AUTHOR]

The heterogeneous network formed by the deployment and interconnection of various network devices (e.g., sensors) has attracted widespread attention. P M 2.5 forecasting on the entire industrial region throughout mainland China is an important application of heterogeneous networks, which has great significance to factory management and human health travel. In recent times, Large Language Models (LLMs) have exhibited notability in terms of time series prediction. However, existing LLMs tend to forecast nationwide industry P M 2.5 , which encounters two issues. First, most LLM-based models use centralized training, which requires uploading large amounts of data from sensors to a central cloud. This entire transmission process can lead to security risks of data leakage. Second, LLMs fail to extract spatiotemporal correlations in the nationwide sensor network (heterogeneous network). To tackle these issues, we present a novel framework entitled Spatio-Temporal Large Language Model with Edge Computing Servers (STLLM-ECS) to securely predict nationwide industry P M 2.5 in China. In particular, We initially partition the entire sensor network, located in the national industrial region, into several subgraphs. Each subgraph is allocated an edge computing server (ECS) for training and inference, avoiding the security risks caused by data transmission. Additionally, a novel LLM-based approach named Spatio-Temporal Large Language Model (STLLM) is developed to extract spatiotemporal correlations and infer prediction sequences. Experimental results prove the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

In this paper, a practical scenario of multicasting through heterogeneous cellular network has been considered. The macro base station of macro cell transmits confidential information to its intended multicast macro users and multiple eavesdroppers try to decode this information and pico base station of pico cell transmits confidential information to its intended multicast pico users. Authors are interested to develop a mathematical model consists of the closed-form analytical expression for the secrecy multicast capacity (SMC) to protect that confidential information from eavesdropping. The results show that the simultaneous effects of double scatterer and correlation significantly reduce the security in multicasting but these reduction in the security can be compensated by antenna diversity at the multicast users. The results also show the effect of double scatterer on different correlation scenario (Constant Correlation and Jakes Correlation) in terms of the correlation figures of transmit, receive and scatterer correlation matrices. [ABSTRACT FROM AUTHOR]

Regeneration code technique has been widely used in the field of data storage with the advantages of high fault tolerance and low redundancy overhead, but the redundancy technique based on regeneration codes needs to download multiple coded blocks from other providers for repairing the failed data. Considering the heterogeneity of link bandwidth between nodes, the available bandwidth capacity of links varies greatly in real networks, minimization of network traffic does not necessarily imply minimization of regeneration time. Moreover, existing regeneration code repair schemes for bandwidth heterogeneity are difficult to support exact repair regeneration codes. Due to the specific mathematical structure of exact repair regeneration codes, their parallel repair is difficult to achieve. Therefore, ERC-TREE is proposed as a repair framework for exact repair regeneration codes under bandwidth heterogeneous networks. This framework efficiently takes advantage of the available bandwidth between providers by constructing an optimal tree to achieve exact repair of failed node data. The simulation experiment shows the feasibility of tree repair for exact repair regeneration codes in heterogeneous bandwidth environments. In the scenario with a significant difference in bandwidth, ERC-TREE reduces the data repair time by 78%compared to star repair. [ABSTRACT FROM AUTHOR]

The escalating demand for data in wireless communication systems has posed significant challenges in recent years. This trend is predicted to continue, with explosive data usage and evolving quality of service demands from mobile users. The rapid increase in traffic demand, combined with the intricate nature of heterogeneous network (HetNet) scenarios, has significantly heightened the challenges confronting mobile network operators. These challenges encompass service quality, load distribution, coverage, and the overall user experience. Conventional approaches that prioritize maximum received power in the cell association mechanism tend to sustain network imbalances within the HetNets, making it difficult to cater for the diverse traffic requirements of mobile users. In this study, instead of focusing solely on enhancing individual user downlink rates, we maximize the number of users whose downlink needs are satisfied by integrating a cell range extension (CRE) technique with an ant colony optimization algorithm. Our proposed method considers both the workload of base stations and the signal to interference-plus-noise ratio of user devices to formulate an objective function aimed at calculating specific CRE bias values for individual small base stations. A comparative analysis of the proposed approach with existing techniques demonstrates its effectiveness. Simulation results underscore the success of our proposed strategy in meeting users' throughput needs while reducing network imbalances and call drop rates. [ABSTRACT FROM AUTHOR]

Multiple communication applications such as high-quality video streaming, IoT, cellular vehicle to anything, augmented reality, virtual reality, and low-latency web browsing make the cellular network heterogeneous. To seamlessly support heterogeneous functionality in the upcoming 6G mobile network, the open radio access network (O-RAN) Alliance is being formed. The O-RAN Alliance is a step towards defining a standard interface between systems. It aims to reduce complexity and accelerate the deployment of 6G mobile networks. However, in deploying the 6G mobile network, the efficient sharing of resources among heterogeneous users is challenging. We considered a novel ultra-dense heterogeneous 6G O-RAN-based cellular network architecture and proposed a multi-objective particle swarm optimization at both small cell base stations (SBSs) and macrocell base stations (MBSs) for collaborative resource optimization. Analytical expressions are derived for joint energy and spectrum optimization at SBS and MBS. The simulation results show that the proposed scheme has a noticeable effect on the optimization of the energy consumption of the system in different scenarios and prove that this algorithm has a remarkable convergence. [ABSTRACT FROM AUTHOR]

Increasing research has shown that the abnormal expression of microRNA (miRNA) is associated with many complex diseases. However, biological experiments have many limitations in identifying the potential disease-miRNA associations. Therefore, we developed a computational model of Three-Layer Heterogeneous Network based on the Integration of CircRNA information for MiRNA-Disease Association prediction (TLHNICMDA). In the model, a disease-miRNA-circRNA heterogeneous network is built by known disease-miRNA associations, known miRNA-circRNA interactions, disease similarity, miRNA similarity, and circRNA similarity. Then, the potential disease-miRNA associations are identified by an update algorithm based on the global network. Finally, based on global and local leave-one-out cross validation (LOOCV), the values of AUCs in TLHNICMDA are 0.8795 and 0.7774. Moreover, the mean and standard deviation of AUC in 5-fold cross-validations is 0.8777 +/−0.0010. Especially, the two types of case studies illustrated the usefulness of TLHNICMDA in predicting disease-miRNA interactions. [ABSTRACT FROM AUTHOR]

Heterogeneous network link prediction is a hot topic in the analysis of networks. It aims to predict missing links in the network by utilizing the rich semantic information present in the heterogeneous network, thereby enhancing the effectiveness of relevant data mining tasks. Existing heterogeneous network link prediction methods utilize meta-paths or meta-graphs to extract semantic information, heavily relying on the priori knowledge. This paper proposes a heterogeneous network link prediction based on network schema and cross-neighborhood attention method (HNLP-NSCA). The heterogeneous node features are projected into a shared latent vector space using fully connected layers. To resolve the issue of prior knowledge dependence on meta-path, the semantic information is extracted by using network schema structures uniquely in heterogeneous networks. Node features are extracted based on the relevant network schema instances, avoiding the problem of meta-path selection. The neighborhood interaction information of input node pairs is sensed via cross-neighborhood attention, strengthening the nonlinear mapping capability of the link prediction. The resulting cross-neighborhood interaction vectors are combined with the node feature vectors and fed into a multilayer perceptron for link prediction. Experimental results on four real-world datasets demonstrate that the proposed HNLP-NSCA mothed outperforms the baseline models.

Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA–disease interactions. Although there have been computational methods proposed for the detection of piRNA–disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA–disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA–disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA–disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While the transition to 6G network technology has introduced significant advancements in mobile communications, it has also presented challenges to achieving optimal signalinterference-noise-ratio (SINR), especially in urban and suburban environments. This study focuses on SINR distributions within the lower-value spectrum characteristic of 6G environments, with the goal of refining coverage probability models. Through advanced simulations, the study enhanced existing models by considering factors such as antenna heights and signal frequencies. The findings showed a sharp initial increase in coverage probability in urban areas with additional base stations, contrasting with a more linear improvement in suburban areas. Specifically, urban coverage probability improved from 0.60 to 0.77, while suburban coverage saw a gradual increase from 0.50 to 0.63 with increased base station density. The study highlighted the need for tailored 6G deployment strategies to effectively address the unique challenges of urban versus suburban settings. The findings offer valuable insights for network planners and operators in optimizing 6G networks, emphasizing the significance of SINR optimization in achieving high-quality network coverage across different geographical landscapes. [ABSTRACT FROM AUTHOR]

Video anomaly detection, an important aspect of intelligent surveillance systems, is an important challenge to effectively distinguish the appearance and motion differences between normal and anomalous events. However, previous work either relies on single-scale appearance (spatial) features or motion (temporal) features or treats them indiscriminately, making the model unable to exploit information specific to both. Secondly, existing clustering methods are susceptible to high-dimensional spatial "chain" mainfold distribution, which affects the accuracy of anomaly detection. In this paper, we propose a novel unsupervised prediction network, the appearance-motion heterogeneous network (AMHN) for video anomaly detection. The AMHN consists of a spatial convolutional auto-encoder (CAE) for learning appearance normality, a temporal U-Net auto-encoder for learning motion normality, and a key-value network for alleviating the "chain" mainfold distribution of apparent-motion features in high-dimensional space. In the testing phase, normal and anomalous events are distinguished by generating a regular score for each sample. The proposed AMHN framework outperforms the state-of-the-art methods with AUCs 96.71%, 86.70% and 73.88% on UCSD Ped2, CHUK Avenue and ShanghaiTech datasets, respectively. [ABSTRACT FROM AUTHOR]

The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs. The application of deep learning methods has significantly improved the prediction performance for DTIs. Modern deep learning methods can leverage multiple sources of information, including sequence data that contains biological structural information, and interaction data. While useful, these methods cannot be effectively applied to each type of information individually (e.g., chemical structure and interaction network) and do not take into account the specificity of DTI data such as low- or zero-interaction biological entities. To overcome these limitations, we propose a method called MFA-DTI (Multi-feature Fusion Adopted framework for DTI). MFA-DTI consists of three modules: an interaction graph learning module that processes the interaction network to generate interaction vectors, a chemical structure learning module that extracts features from the chemical structure, and a fusion module that combines these features for the final prediction. To validate the performance of MFA-DTI, we conducted experiments on six public datasets under different settings. The results indicate that the proposed method is highly effective in various settings and outperforms state-of-the-art methods. • A novel DTI prediction model that explores both the graph and structural features of drugs and proteins is proposed. • A graph processing algorithm for biological networks is designed. • A module for generating sequence structure vectors is developed. • The results on 6 public datasets show it outperforms existing methods in all observed metrics. [ABSTRACT FROM AUTHOR]

Drug‐target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug‐target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low‐dimensional topology representation of drugs and targets via graph‐based convolutional neural network. NGCN achieves substantial performance improvements over other state‐of‐the‐art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) communication is a potential strategy for overcoming the limits of classic orthogonal multiple access schemes, and it can improve possible rates. In contrast, MIMO–NOMA improves them even more due to multiple antenna diversity advantages. On the other hand, future wireless communication will rely on heterogeneous networks (HetNets), which allow many wireless access networks to coexist hierarchically. However, channel reciprocity is invalid because of the shorter channel coherence time in 5G and 6G communications. As a result, channel estimation through an uplink (UL) pilot no longer holds for downlink (DL) transmission. To address this issue, a statistical beamforming approach is proposed that does not consider channel reciprocity for channel estimation. Consequently, the proposed method saves the pilot transmission overhead required for channel estimation resulting in increased spectral efficiency. To achieve this, we propose to employ the characterization of the ratio of the indefinite quadratic form (IQF) to obtain a closed-form formula for the outage probability in MIMO–NOMA HetNets. The analytical expression obtained is then utilized to design optimal beamforming weights using the genetic algorithm (GA) that solves a constrained multi-objective optimization task. In summary, advantages of our proposed method are (1) it provides spectral efficient method of beamforming, (2) it derives an exact characterization of outage probability in MIMO–NOMA HetNets in closed form, and (3) it develops a GA based optimization solution that solves a constrained multi-objective optimization task simulation results presented validate the theoretical analysis and show supremacy of the proposed method over classical statistical method. [ABSTRACT FROM AUTHOR]

The significance of community structure in complex networks, such as social, biological, and online networks, has been widely recognized. Detecting communities in social media networks typically relies on two sources of information: the network's topological structure and node attributes. Incorporating rich node content attribute information poses both flexibility and challenges for community detection. Traditional approaches either focus on mining one information source or linearly combining results from both sources, which fails to effectively fuse the information. This paper introduces a practical collaborative learning approach that explores the multi-dimensional attribute characteristics of nodes to facilitate community division. By leveraging graphical matrix decomposition, the proposed algorithm, CDGMF, improves the effectiveness and robustness of community detection. Experimental results demonstrate the method's ability to effectively utilize node attribute information for guiding community detection, resulting in higher-quality community divisions. [ABSTRACT FROM AUTHOR]

Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages meta- paths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network. [ABSTRACT FROM AUTHOR]

In wireless networks, the effectiveness of beamforming and power allocation strategies is crucial in meeting the increasing data demands of users and ensuring rapid data transmission. Graph learning approaches have been developed to tackle complex challenges in wireless communications and have shown promising results. However, most existing graph learning methods primarily focus on node features, neglecting the potential benefits of leveraging rich information from edge features. This study addresses this limitation and proposes a novel framework called Heterogeneous Node and Edge Graph Neural Network (HNENN). Specifically designed for heterogeneous networks, HNENN incorporates node-level and edge-level attention layers to learn and aggregate node and edge embeddings. The alternating stacking of these two layers facilitates the mutual enhancement of node and edge embeddings. Simulations show that the proposed framework works better than state-of-the-art approaches, getting a higher sum rate in different scenarios with different numbers of D2D pairs, training samples, interference levels, and transmit power budgets. [ABSTRACT FROM AUTHOR]

Almost blank subframe (ABS) is a classic and efficient technology to cancel interference in cellular networks and could be applied in future networks such as ultra‐density network. However, the existence of ABS will result in the deterioration of the communication capacity of the macro base station (BS), particularly for applications with large number of edge users. To solve this problem, an improved ABS technology with an interference cancelation scheme is proposed. For the considered scenario, the macro BS and the micro BS work in a cooperative manner, where the signal transmitted by the macro BS interferes with the communication between the micro BS and its edge terminals. At the terminal side, both the nonlinearity of the macro BS transmitter and the propagation model between the edge user and the macro BS are estimated using neural networks (NNs), which are then sent to the micro BS for interference elimination through the ABS. At the micro BS side, firstly, the nonlinearity and channel parameters from the ABS frame are used to generate a cancelation signal. Secondly, the cancelation signal is sent from the micro BS to the edge user to eliminate the disturbance from the macro BS. This enhanced technology can alleviate the disturbance for the edge user. Meanwhile, since it massively decreases the occupancy of ABS in the macro BS, the capacity of the macro cell can be improved. [ABSTRACT FROM AUTHOR]

The effectiveness of developing reconfigurable intelligent surfaces (RIS) for heterogeneous network (HetNet) systems has resulted in significant spectral efficiency (SE) gains. The majority of current research has not addressed effectively whether a hybrid metaheuristic technique may be used to create the hybrid RIS phase changes in HetNet. In this paper, we study a heterogeneous network (HetNet) assisted by a hybrid reconfigurable intelligent surface (H-RIS). Compared with the passive RIS, a hybrid RIS that has only 8% active elements is proposed to enhance by reflecting and amplifying incident signals. Hybrid RIS phase shift and SBS transmit beamforming are optimised. At the SBS, transmit beamforming based on zero-forcing is employed, whereas for hybrid RIS phase shift optimisation, the hybrid PSO-GWO (HPSOGWO) method is employed. The exploitation power of particle swarm optimisation (PSO) and the exploration power of the grey wolf optimizer (GWO) are combined in this hybrid approach. Simulation findings show that the suggested method, which uses only modest active RIS elements, can achieve a significant spectral efficiency improvement over both RIS-aided HetNet with totally passive RIS elements but with the SDR method to optimise the RIS phase changes and RIS-aided HetNet with entirely active RIS elements but with the Lagrange multiplier (LM) scheme to design the phase shift at the active RIS.

Clustering algorithms have a key role in decreasing energy consumption and increasing network longevity in wireless sensor networks. This work advances on previous homogeneous and heterogeneous algorithms, including low-energy adaptive clustering hierarchical routing protocol (LEACH), distributed residual energy LEACH (DIS-RES-EL), residual energy LEACH (RES-EL), energy efficient LEACH (EEL), and stable election protocol (SEP), by introducing novel clustering methodologies. It introduces novel improved residual energy LEACH (IMP-RES-EL) and energy efficient stable election protocol (EE-SEP) to improve the efficiency of clustering algorithms in energy savings for homogeneous and heterogeneous wireless sensor networks. The simulation result shows that, in addition to prolonging network lifetime and optimal routing, these methods transported more data packets from the cluster to sensor nodes and then to base stations than other techniques. When compared to the stable election protocol (SEP), the proposed energy-efficient stable election protocol (EE-SEP) influences the number of bunch heads formed over their lifetime, the organization’s stability, the number of nodes shipped off the base station from each cluster head, and the organization’s overall lifetime. When comparing the two current algorithms, EE-SEP and LEACH, for various topologies, the findings demonstrate that EE-SEP is the most energy efficient directing convention for extending the previously described qualities. This attribute has not been discussed thus far. The results also show that the IMP-RES-EL algorithm successfully increases network lifespan while minimizing energy dissipation and transmissions between sensor nodes and base stations or cluster heads (CHs). For all of the suggested homogeneous and heterogeneous algorithms, network lifetime in rounds rose by 36%, aggregated data packets from CHs to BS increased by 44%, and total data packets to BSs improved by 20%.

The quality of service (QoS) in public transportation is not at an acceptable level in high-mobility scenarios as penetration loss severely degrades signal quality and reduces the achievable data rate. Also, high mobility requirement is included in 5G as an essential part of its design. In this paper, a background of mobility management in high-speed scenarios and a review of underlay mobility management are presented. After a discussion of the usual high mobility challenges, major techniques for dealing with mobility management are reviewed, including architectural support, handover optimization, and employing new techniques like non-orthogonal multiple access (NOMA), and energy efficiency maximization. Also, challenges and prospective research directions for each technique are provided. The primary objective of this research study is to enhance knowledge of mobility management in high-speed vehicular scenarios, specifically by employing network architectural support in an upcoming heterogeneous mobile network. The primary objective of this research study is to enhance knowledge of mobility management in high-speed vehicular scenarios, specifically by employing network architectural support in an upcoming heterogeneous mobile network.

In order to logically prioritize the urgent risks in the heterogeneous mobile network operating environment, we derive environmental factors that can reflect the characteristics of the heterogeneous network operating environment and present them as an improved security risk assessment formula. The prioritized risks derived through this improved risk assessment formula can visually express the severity of the risk by using computer vision. The purpose of this study was to derive environmental factors that can reflect the security control characteristics of various heterogeneous network operating environments and to apply them to security risk evaluation formulas to prioritize urgent risks and easily identify the degree of security risks. In the existing risk assessment formula, risk is calculated based on three indices: the importance of the asset, the vulnerability score, and the threat score. However, two problems were derived from the existing risk assessment. First, the existing risk assessment formula is insufficient to reflect the controlled environment characteristics of each network because the risk level is calculated based on individual assets. Second, if the same systems with the same purpose (same settings) are operated in different heterogeneous network operating environments, they are counted at the same risk level, and action cannot be prioritized quickly. To solve these problems, we propose an indicator called environmental factor (E), which is a combination of three indices. The three indices are "Network Diversity Index (NDI), network Zone Separation Index (ZSI) and Control Level Index (CLI)". NDI expressed the diversity of networks numerically. ZSI is a numerical expression of the complexity of the network zone. CLI is a numerical expression of the degree of network control level. Results of the study showed that the risk assessment formula applying the proposed risk assessment factors can quickly identify urgent risks and act quickly. In heterogeneous mobile network operating environment in which numerous systems are operated, really urgent risks among the risks calculated through the proposed risk assessment will be handled quickly and logically.