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This study addresses the stability and stabilization problems of discrete-time semi-Markov jump linear systems (S-MJLSs) with unavailable sojourn-time information. The sojourn-time probability mass functions (S-TPMFs) of discrete-time semi-Markov chains are no longer confined to the geometric distribution, and it is difficult to obtain accurate and comprehensive information for S-TPMFs in practice. This is because S-TPMFs are usually deduced from the statistical characteristics according to the sampled-data, while adequate samples are often costly and time consuming. In this study, when the S-TPMFs for semi-Markov chains are assumed to be unavailable, the σ-error mean square stability is investigated for discrete-time S-MJLSs with some widely used assumptions; for semi-Markov chains, only the transition probability matrix of the embedded chain is used. In addition, the existence conditions of the effective controller are provided for closed-loop systems without using the information of S-TPMFs. Numerical examples are presented to illustrate the validity of the obtained theoretical results.

In this paper, nonsingular prescribed-time control is studied based on periodic delayed sliding mode surfaces. Different from the existing sliding mode control, where either singularity problems may appear or the convergence time depends on the initial state, the proposed sliding mode control approaches can achieve prescribed-time convergence without singularity. The proposed nonsingular sliding mode control approaches can be applied to both second-order and high-order nonlinear systems with prescribed-time convergence. As applications of the proposed sliding mode control approaches, the control of hypersonic vehicle systems is revisited. Numerical simulations on the nonlinear model of the hypersonic vehicle system show the effectiveness of the proposed methods.

We develop a comprehensive and accurate channel model forMI-WUSNs by taking into account the impact of multiple conductive objects located in different positions. The mathematical expressions for mutual inductance and received power are rigorously derived for each case. Extensive experimental results have verified our theory. Hence, this paper provides a solid foundation for subsequent system design for MI-WUSNs.

Emerging applications such as smart city infrastructures and virtual reality landscapes are setting rigorous benchmarks for 6G mobile networks, requiring elevated levels of confidentiality, integrity, non-repudiation, authentication, and stringent access controls. Blockchain technology is heralded as a transformative enabler for meeting 6G standards, owing to its intrinsic attributes. However, a gap exists in the holistic investigation of blockchain’s applicability in 6G realms, particularly addressing the “whether”, “when”, and “how” of its deployment. Present research trails in developing robust methodologies to gauge blockchain’s efficacy within 6G use cases. Addressing this, our study introduces a novel confluence of blockchain with 6G networks, where data resides in distributed Hash tables (DHTs) while their hashes are secured in distributed ledger technology (DLT), harnessing blockchain’s core strengths-immutability, traceability, and fortified security. We delineate seven specific 6G use cases poised for enhancement through blockchain integration, and scrutinize the rationale, nature, and timing of this convergence. Furthermore, we devise a comprehensive methodology for assessing blockchain’s performance metrics and scalability in 6G environments. Our extensive experimental analyses evaluate the synergistic performance of this integration, revealing that the Quorum blockchain satisfactorily supports 80% of 6G scenarios. The findings suggest that, with appropriate configurations, consortium blockchains are well-equipped to fulfill the demanding performance and scalability requisites of 6G networks.

In this work, a novel design method of DP-LDPC codes for the JSCC system via the ACE-PEG algorithm has been proposed. The designed base matrix contains more higher-degree VNs than the conventional ones, and thus increases the amount of message between the cycles and/or stopping sets and the remaining part of the Tanner graph via the ACE-PEG algorithm. Simulation results have demonstrated that the proposed design approach of DP-LDPC codes obtains significant performance enhancement in both waterfall and error-floor regions compared to the selected benchmark schemes from the latest literature.

Mobile telemedicine systems based on the next-generation communication will significantly enhance deep fusion of network automation and federated learning (FL), but data privacy is a paramount issue in sectors like healthcare. This work hence considers FL augments 5G-and-beyond networks by training deep learning (DL) models without the need to exchange raw data. The substantial communication loads imposed on by extensive parameters involved in DL models are managed through adaptive scheduling mechanisms effectively. To address the opaque nature of DL models and to improve the interpretability of FL models, we introduce a convolutional fuzzy rough neural network specifically designed for medical image processing. We also develop a multiobjective memetic evolutionary algorithm to streamline and optimize the neural network architectures. Our comprehensive FL framework integrates smart scheduling, interpretable fuzzy rough logic, and neuroevolution. This framework is shown to improve communication efficiency, increase interpretability of diagnosis with protected privacy, and generate low-complexity neural architectures.

Content delivery networks (CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning (DRL) offers a promising solution for intelligent zero-touch network governance. However, the black-box nature of DRL models poses challenges in understanding and making trusting decisions. In this paper, we propose an explainable reinforcement learning (XRL)-based intelligent edge service caching approach, namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence (XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent’s Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental inputs. The results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service (QoS), and space utilization.

As an extension of networked control systems, cloud control systems (CCSs) have emerged as a new control paradigm to improve the service quality of emerging control missions, such as data-driven modeling and automated vehicles. Existing studies have used the workflow-based restructured method to optimize the computation-intensive algorithms in the CCSs. However, the challenges here are how to define and submit these algorithms’ workflows as cloud services and execute these algorithms’ workflows in a containerized manner. Based on these challenges, we propose a containerized solution for the control-algorithm-as-a-service (C3aS) in the CCSs, namely ControlService. It offers the control algorithm as a cloud workflow service and uses a customized workflow engine to realize the containerized execution. First, we employ a cloud workflow representation method to define a control algorithm into an abstract cloud workflow form. Afterward, we provide a cloud service representation of the abstract cloud workflow. Next, we design a workflow engine and submit the cloud service to this workflow engine to implement containerized execution of this cloud service in the CCSs. In the experiment, we discuss the cloud service form and containerized implementation of the subspace identification method. Experimental results show that the proposed ControlService has significant performance advantages in computational time, reduction percentage, and speedup ratio compared with the baseline method.

In this paper, we consider the distributed generalized Nash equilibrium (GNE) seeking problem in strongly monotone games. The transmission among players is implemented through a digital communication network with limited bandwidth. For improving communication efficiency or/and security, an event-triggered coding-decoding-based communication is first proposed, where the data (decision variable) are first mapped to a series of finite-level codewords and, only when an event condition is satisfied, then sent to the neighboring agents. Moreover, a distributed communication-efficient GNE seeking algorithm is constructed accordingly, and the overrelaxation scheme is further taken into consideration. Through primal-dual analysis, the proposed algorithm is proven to converge to a variational GNE with fixed step-sizes by recasting it as an inexact forward-backward iteration. Finally, numerical simulations illustrate the benefit of the proposed algorithms in terms of saving communication resources.

Classifying network time series (NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks, and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification methods. These include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature (RecurSig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning (DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems (NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that RecurSig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.