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In this article, the cooperative output regulation problem of heterogeneous linear multiagent systems under jointly connected digraphs is addressed. The event-triggered control protocols based on state feedback and output feedback are proposed, respectively. It is shown that the output tracking errors of the resulting closed-loop control systems converge to 0 exponentially via the proposed protocols. One of the key advantages of the proposed event-triggering mechanism is that the information transmissions induced by event triggerings and topology switchings are independent, and data transmissions among agents are thus reduced. Furthermore, an explicit minimum interevent time is provided for all the agents so that the Zeno-behavior is excluded strictly. Finally, three numerical examples are provided to verify the effectiveness of the proposed protocols.

This article investigates the event-triggered output consensus problem for a class of unknown heterogeneous discrete-time linear multiagent systems in the presence of unmodeled dynamics. The agents have individual nominal dynamics with unknown parameters, and the unmodeled dynamics are in the form of multiplicative perturbations. A novel design framework is developed based on an event-triggered internal reference model and a distributed model reference adaptive controller. To deal with the heterogeneity of the multiagent system, the event-triggered internal reference model is designed to generate a virtual reference signal for each agent with a dynamic event-triggering mechanism being adopted to reduce the communication burden between neighboring agents. To handle the unknown parameters and unmodeled dynamics, the robust model reference adaptive controller is then designed to follow the generated virtual reference signal. It is shown that if the unmodeled dynamics satisfy certain conditions, then the boundedness of all the signals and variables in the closed-loop system and convergence of consensus errors to a residual set are guaranteed. Moreover, the consensus errors will converge to zero asymptotically in the absence of unmodeled dynamics. Compared with existing related works, the proposed framework is able to deal with the agents with individual unknown nominal dynamics and unmodeled dynamics. Moreover, the proposed framework is fully distributed in the sense that no knowledge of any global information is needed. Finally, the performance of the proposed method is validated by examples.

This paper investigates the global cooperative output regulation problem of continuous-time linear multi-agent systems (MASs), under the communication constraint of limited bandwidth. With a novel scaling function strategy, a distributed protocol based on the sampled and quantized data is designed to solve this problem. Compared with the existing works on quantized control of MASs, not only the boundedness assumption on initial conditions is removed, but also the communication graph is not restricted to be balanced or strongly connected. Moreover, the proposed quantized cooperative output regulation protocol does not rely on any global information of the communication graph. A simulation example is finally presented to illustrate the effectiveness of the proposed quantized cooperative output regulation protocol.

In this study, we present a new experiment in order to study the Lombard effect in telepresence robotics. In this experiment, one person talks with a robot controled remotely by someone in a different room. The remote pilot (R) is immersed in both environments, while the local interlocutor (L) interacts directly with the robot. In this context, the position of the noise source, in the remote or in the local room, may modify the subjects’ voice adaptations. In order to study in details this phenomenon, we propose four particular conditions: no added noise, noise in room R heard only by R, virtual noise in room L heard only by R, and noise in room L heard by both R and L. We measured the variations of maximum intensity in order to quantify the Lombard effect. Our results show that there is indeed a modification of voice intensity in all noisy conditions. However, the amplitude of this modification varies depending on the condition.

This article investigates the leader-following output consensus problem of heterogeneous linear multiagent systems, where the nonidentical followers are subject to norm bounded parameter uncertainties. Two novel distributed event-triggered consensus strategies based on state feedback and output feedback, respectively, are proposed to solve the leader-following output consensus problem. A dynamic event-triggering mechanism which involves internal state variables is proposed to determine the next triggering instant. It should be noted that the controllers can be designed without the knowledge of some global graph information, such as the eigenvalues of the Laplacian matrix associated with the corresponding communication graph and the number of agents. In addition, the interevent time can be prolonged and the Zeno behavior can be excluded with the proposed dynamic triggering mechanism. Finally, an example is presented to illustrate the effectiveness of the proposed controllers.

Radio Access Network (RAN) slicing is a promising architectural technology to address extremely diversified service demands and provide profitable business models for future mobile networks. In RAN slicing architecture, self-healing is an important functional module to minimize the impact of network failings on the performance of RAN slices. However, self-healing of burgeoning sliced RAN is vastly different from that of traditional RAN and has been rarely investigated. In this paper, we address the Self-healing of RAN Slice (SRANS) problem by modeling it as a Pareto optimization problem with the aim of maximizing the self-healing utilities of individual RAN slices. To deal with the weakness of diversity maintenance in traditional Pareto optimization methods, we propose a Generative Adversarial Network (GAN) based Pareto Optimization (GPO) framework. Specifically, we employ self-conditioned GANs to replace the offspring reproduction module in the traditional Evolutionary Algorithm (EA), where the insufficiency of diversity maintenance in EAs is effectively overcome. Furthermore, we theoretically prove that GPO framework is guaranteed to converge to the optimal Pareto solution set. Numerical results demonstrate that the convergence of proposed GPO framework can be expedited by enhancing the diversity of solution sets in solving the SRANS problem. Compared with traditional schemes, GPO can achieve significant performance gain in terms of the utilities and isolation level of repaired RAN slices.

This paper proposes a novel system identification method based on the notion of invariant subspace. It is shown that when the system input and output asymptotically converge onto an invariant subspace, a new form of regression can be obtained. New identification algorithms are then developed based on the obtained regression. The proposed method has several distinctive advantages originating from both time-domain and frequency-domain approaches. They include (i) linear continuous-time models can be identified from slowly sampled input/output data; (ii) consistency of the model parameters can be established in an error-in-variables framework; (iii) the global optimum can be found by solving two linear least-square problems; and (iv) the identification algorithms can be implemented online with explicit convergence rates. The theoretic results are tested by numerical examples to show the effectiveness of the proposed method.

When human listeners try to guess the spatial position of a speech source, they are influenced by the speaker's production level, regardless of the intensity level reaching their ears. Because the perception of distance is a very difficult task, they rely on their own experience, which tells them that a whispering talker is close to them, and that a shouting talker is far away. This study aims to test if similar results could be obtained for prosodic variations produced by a human speaker in an everyday life environment. It consists in a localization task, during which blindfolded subjects had to estimate the incoming voice direction, speaker orientation and distance of a trained female speaker, who uttered single words, following instructions concerning intensity and social-affect to be performed. This protocol was implemented in two experiments. First, a complex pretext task was used in order to distract the subjects from the strange behavior of the speaker. On the contrary, during the second experiment, the subjects were fully aware of the prosodic variations, which allowed them to adapt their perception. Results show the importance of the pretext task, and suggest that the perception of the speaker's orientation can be influenced by voice intensity.

Distributed coordination of multi-agent systems (MASs) has received considerable attention and advanced rapidly in the past years. This paper reviews the recent results on cooperative control of MASs, and in particular cooperative control under various agent dynamics, under communication transmission delays, and under event-triggered strategies. In addition, different coordination tasks, such as consensus, cooperative output regulation, formation control, and containment control, are also taken into consideration. Finally, some open problems on cooperative control of MASs are suggested.

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users. In radio access networks (RAN) slicing, service provisioning for slice users becomes much more complicated than that in traditional mobile networks, as the constraints of both user physical association with base station (BS) and logical association with NS should be considered. In other words, the user-BS-NS three layer association relationship should be addressed in provisioning tailored service for diversified use cases with various quality of service (QoS) requirements. Therefore, service provisioning in RAN slicing becomes an essential yet challenging issue for 5G and beyond systems. In this paper, we propose a unified framework for service provisioning in RAN slicing with aim of maximizing resource utilization while guaranteeing QoS of users. The framework consists of two steps. The first step is to identify a set of slice users whose QoS can be satisfied simultaneously; while the second step performs joint slice association and bandwidth allocation with aim to minimize bandwidth consumption. Numerical results show that in typical scenarios, our proposed service provisioning framework can achieve significant performance gain in terms of the number of serving users and wireless bandwidth utilization compared with traditional schemes.

This paper studies the moving-target enclosing control problem for a group of mobile agents, under the assumption that the amplitude of the target's velocity is unknown a priori. The target's velocity is time-varying and can be viewed as a signal generated by an exogenous linear system. The neighbor topology of the agents is determined by their positions relative to the target at each time instant. Based on the relative positions, a distributed observer is proposed for the agents to cooperatively estimate the velocity of the target. Then a novel distributed control law is proposed based on the obtained estimates. It is shown that under the proposed control law the agents eventually move along a common circle of the desired radius around the moving target and also achieve any desired spaced pattern along the circle. Furthermore, conditions are derived under which the collision between each agent and the target is avoided and the inter-agent collision avoidance is also guaranteed by preserving the counterclockwise order of the agents around the target. Finally, a simulation example illustrates the effectiveness of the proposed control law.

This paper studies the distributed entrapping control problem of multiple mobile agents based on bearing and velocity measurements. A time-varying entrapping formation with a prescribed shape, which is elastic and rotational with respect to the target is adopted. Such a formation allows agents to move even in restricted areas while still entrapping the target as a whole. To achieve the entrapping formation based on bearing measurements, a leader-follower structure is adopted, and a design framework integrating formation shape observers, relative position estimators and distributed controllers is proposed. The rigorous stability analysis of the overall system is also given. It is shown that the stability of the closed loop system is guaranteed if its bearing observability is satisfied. Since the desired formation is determined by the trajectories of the leaders, we characterize the sufficient conditions on the trajectories of the leaders such that the bearing observability of the closed loop system is always satisfied, and the estimation errors and the formation tracking errors converge to zero asymptotically. Simulations verify the effectiveness of the proposed framework.

For ultra-dense networks, user-centric architecture is regarded as a promising candidate to offer mobile users better quality of service. One of the main challenges of user-centric architecture is exploring efficient scheme for user association in the ultra-dense network. In this letter, we study dynamic user-centric association (UCA) problem for ultra-dense millimeter wave (mmWave) networks to provide reliable connectivity and high achievable data rate. We consider time-varying network environments and propose a deep Q-network based UCA scheme to find the optimal association policy based on the historical experience. Simulation results are presented to verify the performance gain of our proposed scheme.

This paper considers the output feedback stabilization problem of linear systems with infinite distributed delays in their inputs and outputs. A low gain observer is proposed to estimate the state of the concerned system with infinite distributed delays in its inputs and outputs. The observer based output feedback controller is then developed. It is shown that the resulting closed loop system is globally asymptotically stable. It should be pointed out that the output feedback control problem of linear systems with infinite distributed input and output delays is first time investigated to the best of our knowledge. Finally, some numerical examples are presented to demonstrate the effectiveness of the proposed controller.

Cyber–physical systems (CPSs) are complex systems that involve technologies such as control, communication, and computing. Nowadays, CPSs have a wide range of applications in smart cities, smart grids, smart manufacturing and intelligent transportation. However, with integration of industrial control systems with modern communication technologies, CPSs would be inevitably exposed to increasing security threats, which could lead to severe degradation of the system performance and even destruction of CPSs. This paper presents a survey on recent advances on security issues of industrial cyber–physical systems (ICPSs). We specifically discuss two typical kinds of attacks, i.e., Denial-of-Service (DoS) attack and Deception attack, and present recent results in terms of attack detection, estimation, and control of ICPSs. Classifications of current studies are analyzed and summarized based on different system modeling and analysis methods. In addition, advantages and disadvantage of various methodologies are also discussed. Finally, the paper concludes with some potential future research directions on secure ICPSs.

This article studies the robust output regulation problem (RORP) for a class of uncertain linear systems with unknown exosystems via event-triggered control (ETC). Compared with existing related works, the system matrix of the exosystem under consideration is allowed to contain unknown parameters. Based on an adaptive internal model, an ETC strategy composed of an event-triggered adaptive output feedback control law and an output-based event-triggering mechanism is proposed such that the RORP is solved. It is shown that under the proposed ETC strategy, the tracking error converges to zero asymptotically in spite of the unknown exosystem, and meanwhile the occurrence of Zeno behavior is prevented. Finally, two examples are provided to illustrate the effectiveness of the proposed control strategy.

Mixture production equipment is widely employed in road construction, and the quality of the produced mixture is the essential factor to ensure the quality of road construction. To detect the quality of the real-time produced mixture and solve the shortcomings of laboratory detection lag, a new fault detection method in the mixture production process is proposed, which is based on wavelet packet decomposition (WPD) and support vector machine (SVM). The proposed scheme includes feature extraction, feature selection, SVM classification, and optimization algorithm. During feature extraction, wavelet basis function is utilized to 4-layer decompose the aggregate and asphalt data mixed in real-time. The energy value calculated by wavelet packet coefficient is the extracted feature. During feature selection, a method combining the chi-square test and wrapper (CSW) is conducted to select the optimal feature subset from WPD features. Eventually, by adopting the optimal feature subset, SVM has been developed to classify various faults. Its parameters are optimized by differential evolution (DE) algorithm. In the test stage, multiple faults of different specifications of aggregates and asphalt are detected in the mixture production process. The results demonstrate that (1) accuracy produced by the CSW method with WPD features is 4.33% higher than the PCA method with statistical features; (2) SVM classification method optimized by DE algorithm brings an increase in recognition accuracy of identifying different types of mixture production faults produced by different equipment. Compared to other available methods, the proposed algorithm has a very outstanding detection performance.

This article devotes to investigating the issue of fuzzy adaptive control for a class of strict-feedback nonlinear systems with nonaffine nonlinear faults. The computational complexity is reduced by adopting the dynamic surface control technique. Under the framework of finite-time stability, a novel fault-tolerant control strategy is designed so that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set in a finite time. Finally, simulation studies for an electromechanical system are shown to verify the feasibility of the presented approach.

This article aims to address distributed optimization problems over directed and time-varying networks, where the global objective function consists of a sum of locally accessible convex objective functions subject to a feasible set constraint and coupled inequality constraints whose information is only partially accessible to each agent. For this problem, a distributed proximal-based algorithm, called distributed proximal primal-dual algorithm, is proposed based on the celebrated centralized proximal point algorithm. It is shown that the proposed algorithm can lead to the global optimal solution with a general step size, which is diminishing and nonsummable, but not necessarily square summable, and the saddle-point running evaluation error vanishes proportionally to $O(1/\sqrt{k})$ , where $k>0$ is the iteration number. Finally, a simulation example is presented to corroborate the effectiveness of the proposed algorithm.

Device-to-device (D2D) technology, which allows direct communications between proximal devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic explosion problem. In this paper, we consider an overlay D2D network, in which multiple D2D pairs coexist on several orthogonal spectrum bands, i.e., channels. Due to spectrum scarcity, the number of D2D pairs is typically more than that of available channels, and thus multiple D2D pairs may use a single channel simultaneously. This may lead to severe co-channel interference and degrade network performance. To deal with this issue, we formulate a joint channel selection and power control optimization problem, with the aim to maximize the weighted-sum-rate (WSR) of the D2D network. Unfortunately, this problem is non-convex and NP-hard. To solve this problem, we first adopt the state-of-art fractional programming (FP) technique and develop an FP-based algorithm to obtain a near-optimal solution. However, the FP-based algorithm requires instantaneous global channel state information (CSI) for centralized processing, resulting in poor scalability and prohibitively high signalling overheads. Therefore, we further propose a distributed deep reinforcement learning (DRL)-based scheme, with which D2D pairs can autonomously optimize channel selection and transmit power by only exploiting local information and outdated nonlocal information. Compared with the FP-based algorithm, the DRL-based scheme can achieve better scalability and reduce signalling overheads significantly. Simulation results demonstrate that even without instantaneous global CSI, the performance of the DRL-based scheme can approach closely to that of the FP-based algorithm.

This article investigates the cooperative output regulation problem for heterogeneous linear multiagent systems by event-triggered control, where the system matrix of the exosystem is not available to all agents. First, an event-triggered adaptive distributed observer is designed for each agent to estimate both the state and the system matrix of the exosystem. Then, a fully distributed event-triggered dynamic output feedback control law is proposed for each agent. Meanwhile, a fully distributed event-triggering mechanism is proposed such that each agent can determine when to broadcast its information to its neighbors. It is shown that with the proposed event-triggered control strategy, the cooperative output regulation problem is solved by intermittent communication in a fully distributed manner in the sense that no global information, such as the eigenvalues of the Laplacian matrix or the number of the agents, is used. It is also shown that Zeno behavior is excluded for each agent. Finally, a numerical example is given to illustrate the effectiveness of the proposed event-triggered control strategy.

To meet low latency and real-time monitoring demands of IoT-eHealth, fog computing is envisioned as a key technology to offer elastic computing resource at the edge of networks. In this context, eHealth devices can offload collected healthcare data or computational expensive tasks to a nearby fog server. However, the mobility of the eHealth devices may make the connection between them to fog servers uncertain, resulting in possible migration between fog servers. In order to evaluate the impact of this uncertainty on decision-making for offloading and resource allocation, we formulate the task offloading problem as a Multi-Stage Stochastic Programming (MSSP), with aim of minimizing the total latency of offloading to determine whether to offload or not, how much workload to offload, how much computing resource to allocate, as well as whether to migrate or not. Different from the previous MSSP based work focusing on the workload assignment only, the proposed MSSP examines joint decisions of offloading, resource allocation, and migration, advancing the understanding of the interactions among these decisions. Furthermore, to reduce the computational complexity of MSSP, we design an efficient sub-optimal offloading policy based on Sample Average Approximation, called SAA-MSSP. We conduct extensive simulation experiments to validate the effectiveness of SAA-MSSP. The results show that SAA-MSSP can converge to a near-optimal solution quickly.

Multi-agent systems (MASs) are typically composed of multiple smart entities with independent sensing, communication, computing, and decision-making capabilities. Nowadays, MASs have a wide range of applications in smart grids, smart manufacturing, sensor networks, and intelligent transportation systems. Control of the MASs are often coordinated through information interaction among agents, which is one of the most important factors affecting coordination and cooperation performance. However, unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly, and thus could lead to severe degradation of the whole system performance and even destruction of MASs. This paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks, and our goal is to present a comprehensive survey on recent results on fault estimation, detection, diagnosis and fault-tolerant control of MASs, and cyber attack detection and secure control of MASs subject to two typical cyber attacks. Finally, the paper concludes with some potential future research topics on the security issues of MASs.

This article proposes a high-order disturbance observer (HODO) and dynamic surface control (DSC) technique-based adaptive fuzzy control scheme for nonlinear systems subjected to input saturation and external time-varying disturbances. First, based on a Sigmoid function, the saturation input is tackled by utilizing a well-defined nonlinear smooth function. Furthermore, HODO and fuzzy logic systems are used to estimate the external disturbances and to handle the lumped unknown functions, respectively. Then, by using the backstepping method and DSC technique, a HODO-based adaptive fuzzy tracking control scheme is proposed for nonlinear systems with saturation nonlinearity, uncertainties, and external disturbances. The Lyapunov analysis method is used to prove that all signals in the entire system are semiglobally uniformly ultimately bounded (SGUUB). In addition, the tracking error converges to a compact set with a tunable error bound determined by some design parameters. Finally, a numerical simulation of two-stage chemical reactor shows the effectiveness of the developed tracking control strategy.

This paper investigates the leader–follower robust ${H_\infty }$ consensus of heterogeneous multiagent systems with denial of service attack, where different attack intensities are considered. A switched system model is introduced to model such an attack phenomenon. Then, sufficient conditions to guarantee the solvability of the robust output consensus problem are obtained, and the quantitative relationship between the consensus performance and attack parameter is established. It is shown that the consensus protocol design problem can be transformed into two static output feedback (SOF) control problems. It is also shown that the SOF controller gains can be determined by solving some linear matrix inequalities without the knowledge of the probability information of each attack. Finally, the effectiveness of the control protocol is demonstrated by a simulation study.

In this paper, we investigate the distributed output-feedback tracking problem of nonlinear multiagent systems (MASs) where nonlinear functions of agent dynamics depend on unmeasurable states. Most existing results on output-feedback control of nonlinear MASs are concerned with nonlinear agent dynamics in output canonical form in which the nonlinear functions of agent dynamics only depend on the measurable output. When the nonlinear functions of agent dynamics depend on unmeasurable states, the problem remains open due to the difficulties in constructing distributed observers. When the leader is only available to a small portion of followers in the directed communication topology, we establish relationship between the network topology and the design of the corresponding distributed reduced order observers. Furthermore, by developing a new distributed high-gain homogeneous domination design method, distributed observers, and controllers are designed to guarantee that the errors between the followers’ outputs and the leader’s output can be made arbitrarily small while keeping all the states of closed-loop system bounded. Finally, a numerical example is employed to illustrate the effectiveness of the proposed control scheme.

For a class of stochastic nonlinear systems in pure-feedback form with dead-zone input and multiple time-varying delays, a novel neural network (NN)-based adaptive control approach is presented in this paper through the use of backstepping approach and dynamic surface technique. By choosing proper Lyapunov–Krasovskii functionals, utilizing the characteristic of hyperbolic tangent functions and adopting the function separation technique, difficulties of controller design that introduced by the time-varying delays can be dealt with properly. Moreover, all unknown nonlinear functions are lumped together and approximated by the NN. Additionally, any information over the boundedness of dead-zone parameters is not needed in the process of controller design. The control scheme proposed in this paper ensures the boundedness in probability of all signals in the closed-loop system, besides, excellent performance of arbitrarily small tracking error will be achieved by selecting control parameters appropriately. At last, two numerical simulation examples are provided to verify the validity of the designed algorithm.

Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-the-art solutions.

Dense deployment of small cells operating on different frequency bands based on multiple technologies provides a fundamental way to face the imminent thousand-fold traffic augmentation. This heterogeneous network (HetNet) architecture enables efficient traffic offloading among different tiers and technologies. However, research on multi-tier HetNets where various tiers share the same microwave spectrum has been well-addressed over the past years. Therefore, our work is targeted towards novel multi-tier HetNets with disparate spectrum (microwave and millimeter wave). In fact, despite the huge capacity brought by millimeter-wave technology, the latter will fail to provide universal coverage, especially indoor, and so mmWave will inevitably co-exist with a traditional sub-6GHz cellular network. In this work, we propose a coordinated user association and spectrum allocation by resorting to non-cooperative game theory. In fact, in such an arduous context, efficient distributed solutions are imperative. Extensive simulation results show the precedence of our coordinated approach in comparison with state-of-the-art heuristics. Moreover, we evaluate the impact of various network parameters, such as mmWave density, cell load, and user distribution and density, offering valuable guidelines into practical 5G HetNet design. Finally, we assess the benefit brought by massive MIMO for mmWave in such a highly heterogeneous setting.

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users. In radio access networks (RAN) slicing, service provisioning for slice users becomes much more complicated than that in traditional mobile networks, as the constraints of both user physical association with base station (BS) and logical association with NS should be considered. In other words, the user‐BS‐NS three layer association relationship should be addressed in provisioning tailored service for diversified use cases with various quality of service (QoS) requirements. Therefore, user association (including handoff scheme) in RAN slicing becomes an essential yet challenging issue for 5G‐and‐beyond systems. In this chapter, we propose to solve two fundamental problems: user access control and mobile user handoff policy design for RAN slicing to achieve satisfactory QoS of users.

Network slicing is one of the most promising enabling technologies for next generation mobile network (5G), which spans over all domains of the network, including data centers, core networks (CNs), transport networks, and radio access networks (RANs). As such wide range of technologies are involved, it is difficult to ensure an end‐to‐end (network) slice working stably all the time. First, for both CN and RAN slices, bugs may accidentally occur in virtual network functions(VNFs), which can invalidate the slice and trigger a recovery process. In addition, the traffic demands to be accommodated by a CN or RAN slice can be stochastic rather than deterministic parameters, and changes of traffic demands that are too drastic may trigger slice reconfiguration. In this chapter, we investigate a robust RAN slicing mechanism to address slice recovery and reconfiguration in a unified framework. A slice is constituted by a set of VNFs and links and remapping is a process of re‐selecting VNFs and links to recover failures. As a baseline, we first formulate a recovery problem to solve remapping of a slice with deterministic traffic demands. Based on that, we propose two robust RAN slicing algorithms for slice recovery under stochastic demands. Numerical results reveal that the proposed robust RAN slicing algorithms can provide adjustable tolerance of traffic uncertainties, compared with the baseline algorithm. In addition, the tradeoff between the robustness of slices and the average load of links and the tradeoff between the robustness and the average recovery time of slices can be efficiently managed and controlled.

This article addresses stability and stabilization of infinite delay systems, which are more general but also more challenging to deal with than bounded delay systems. Based on a general model of infinite delay systems and a newly approved key technical lemma, several new Lyapunov theorems for uniform asymptotical stability and exponential stability, respectively, are established. The stability results are more general than existing stability results, and the corresponding conditions are more easily satisfied than existing ones. Therefore, the new stability results are expected to be more widely applicable. These new Lyapunov theorems are then applied to the problems of stabilizing both time-invariant and time-varying linear systems with distributed infinite input delays, and the corresponding stabilizing controllers are developed. Key to obtaining the stabilizing controllers is to construct appropriate Lyapunov functionals for these systems via introduction of a regulation term into the Lyapunov functional. A distinctive advantage of the Lyapunov-based time-domain method proposed in this article over the existing frequency domain method is that the former can be adopted to deal with more general systems, such as time-varying linear systems or even nonlinear systems. Several examples are provided to illustrate the effectiveness of our results.

The consensus problem of heterogeneous linear multi-agent systems (MASs) with communication, input and output time-delays is studied in this paper. An observer-predictor is designed to estimate the future states of each agent, and then a distributed dynamic controller based on this observer-predictor is proposed to solve the concerned problem. Switching and directed communication topologies are considered in this work. It is shown that the closed loop control systems can reach consensus for arbitrarily large yet bounded time-varying communication delays, time-varying input delays and constant output delays. A simulation example is provided to illustrate the effectiveness of the controller.

This article addresses the finite-time adaptive fuzzy control problem for a class of nonstrict-feedback uncertain nonlinear systems via an event-triggered strategy. A novel design scheme, consisting of finite-time adaptive fuzzy controller and event-triggering mechanism (ETM), is proposed to decrease the number of data transmission and the number of control actuation updates. With the proposed event-triggered adaptive fuzzy control scheme, all the solutions of the resulting closed-loop system are guaranteed to be semi-globally bounded within finite time. Moreover, the feasibility of the proposed ETM is verified by excluding Zeno behavior. In contrast to existing results on similar problems, the restrictions on nonlinearities are relaxed and the more general uncertain nonlinear systems are considered. Finally, an example is provided to illustrate our theoretical results.

This paper investigates the quantized consensus problem of general linear multiagent systems (MASs) by event-triggered control. A novel event-triggered distributed control protocol is proposed based on a new dynamic quantizer. Compared with periodical sampling for most existing works on quantized consensus, the proposed event-triggered control approach can significantly save communication and thus energy resource. It is shown that with the proposed distributed control protocol quantized consensus of the concerned MAS can be achieved, and the Zeno behavior and continuous monitoring of the neighbors’ states are avoided. Moreover, it is shown that the required number of the quantization levels of the new quantizer remains small even if the number of the agents in the MAS is large. Three simulation examples are given to illustrate the effectiveness of the proposed consensus protocol.

In both wired and wireless communication networks, power allocation is an important technique to improve system performance. This paper investigates the power allocation problem for parallel Gaussian channels from an information-theoretic perspective with the aim of maximizing the sum of mutual informations ( i.e. , an achievable data rate). If all the inputs are Gaussian, it is well-known that the waterfilling policy provides an optimal solution. For arbitrary input distributions, a generalization of waterfilling, so-called mercury/waterfilling, provides an optimal solution in terms of the minimum mean square errors (MMSEs). However, the difficulty of obtaining closed-form analytical expressions of the MMSE often makes computing the mercury/waterfilling solution challenging. This paper proposes a robust waterfilling power allocation (RPA) policy for parallel Gaussian channels when the input distributions are close to Gaussian distributions in the Kullback-Leibler (KL) divergence (relative entropy). First, it is shown that the proposed policy results in water-levels that are close to the optimal ones in a well-defined sense. Second, tight bounds for the loss in mutual information (data rate) are given. This bounded loss property makes the proposed power allocation policy robust and approximately optimal, which is illustrated by means of various simulation setups. Moreover, the RPA policy provides a general framework for solving the power allocation problem for parallel channels, with the classical waterfilling being included as a special case. Finally, the RPA policy is argued to be scalable with the number of users since it inherently uses the classical low complexity waterfilling.

This paper investigates the consensus problem of linear multi-agent systems subject to external disturbances via distributed event-triggered adaptive control. First, a distributed event-triggered adaptive output feedback control strategy is proposed for each agent. It is shown that under this control strategy, the consensus problem can be solved for any connected undirected communication graph in a fully distributed manner without using any global information. Then a distributed self-triggered adaptive output feedback control strategy is designed with which continuous monitoring of the measurement error is no longer needed. It is further shown that for the proposed event-triggered and self-triggered control strategies, no agent will exhibit Zeno behavior. Finally, the effectiveness of the proposed two control strategies is illustrated on a group of two-mass-spring systems.

This paper studies the cooperative control problem of multiple nonlinear benchmark systems perturbed by second-order moment processes. The nonlinear benchmark system consists of a moving car and a rolling ball in oscillating surroundings. When the leader is only accessible to a small part of the followers in a directed graph, a new vectorial backstepping method is proposed for the design of distributed cooperative control laws. By using stochastic analysis techniques and algebra graph theory, it is shown that the cooperative control problem under consideration is solvable. Specifically, the errors between the followers' outputs and the leader's output can be made arbitrarily small while keeping all states of the closed-loop system bounded in probability. Finally, the effectiveness of the proposed control scheme is demonstrated through a simulation example.

In this paper, we study the distributed containment control problem with multiple leaders for nonlinear multi-agent systems (MASs) with unstabilizable linearizations under the directed topology. A novel distributed control design approach is developed to simultaneously deal with the unstabilizable linearizations and nonlinear interactions among agents. The designed controller can guarantee that the followers’ outputs will eventually converge to a limit point in the convex hull spanned by the leaders’ outputs while all the states of the closed-loop system remain bounded. Finally, a mechanical system example is presented to illustrate the effectiveness of the proposed control scheme.

This paper investigates the leader-follower formation control problem of nonholonomic vehicles. An estimator is first developed to estimate the relative position in its local coordinate system with bearing measurements, to avoid directly using positions or relative positions in the global reference frame. A control law is then proposed for the follower vehicle to follow the leader based on the estimated relative position. It is shown that the estimation error converges to zero, if some conditions on the relative angular velocity between the leader and the follower are satisfied. It is also shown that the stability of the closed loop system involving both the estimator and the controller is guaranteed if some mild conditions on the trajectory of the leader are satisfied. Extension to the case of multiple followers is also considered. Simulation results of numerical examples verify the effectiveness of the proposed control scheme.

In 5G-and-beyond wireless communication systems, Network Function Virtualization (NFV) has been widely acknowledged as an important network architecture solution to meet diverse service requirements in various scenarios. However, with the increase of network functions, the introduction of NFV may significantly increase the delay of traffic flows, which is much undesired, especially for Ultra Reliable and Low Latency Communication (URLLC) service. Network Function Parallelism (NFP) architecture has been recently proposed as an effective technique to address the bottleneck of NFV technology. NFP can potentially improve the reliability and reduce the delay of service function chains (SFCs). In this paper, we propose a learning based SFC deployment strategy under NFP architecture with aim to improve the service reliability while reducing the end-to-end service delay. Specifically, service reliability is improved by deploying back-up virtual network function (VNF) nodes, while the flow delay is reduced via parallel network function processing. We formulate the VNF deployment as an integer-programming problem with objective of minimizing the reserved computing and bandwidth resources, while guaranteeing the service reliability and end-to-end delay. Considering the hardness and properties of the problem, we transform it as a Markov Decision Process (MDP), and employ a reinforcement-learning algorithm to solve it. We conduct simulations and the numerical results demonstrate that the proposed strategy can significantly improve the service reliability and delay performance, which are crucial for URLLC service.

This paper studies the problem of shape-preserving formation control for multi-agent systems where only bearing measurements are available. Most existing methods to solve the problems assume that accurate global or local position measurements of agents are available, or use estimators to get these information without analysis of estimators’ effects on the closed loop system stability. In this work, we propose a framework integrating an estimator to get relative positions using bearing-only measurements and an estimator-based controller to achieve tracking a pair of leaders in a formation with a preserved shape. The estimator is designed by exploiting orthogonality to cope with the high non-linearity of bearing measurements, based on which the controller is designed with the estimated relative positions. With rigorous theoretical analysis of the closed-loop system, we characterize the leaders which can be tracked by followers in a shape-preserving formation using bearing-only measurements, and the asymptotic stability of the closed-loop system can be guaranteed. Simulations testify the effectiveness of the proposed framework.

Emerging mobile edge techniques and applications such as Augmented Reality (AR)/Virtual Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of power and computing resource consumptions. In the meantime, the volume of data generated at the edge networks is also increasing rapidly. Under this circumstance, building energy-efficient and privacy-protected communications is imperative for 5G and beyond wireless communication systems. The recent emerging distributed learning methods such as federated learning (FL) perform well in improving resource efficiency while protecting user privacy with low communication overhead. Specifically, FL enables edge devices to learn a shared network model by aggregating local updates while keeping all the training processes on local devices. This paper investigates distributed power allocation for edge users in decentralized wireless networks with aim to maximize energy/spectrum efficiency while preventing privacy leakage based on a FL framework. Due to the dynamics and complexity of wireless networks, we adopt an on-line Actor-Critic (AC) architecture as the local training model, and FL performs cooperation for edge users by sharing the gradients and weightages generated in the Actor network. Moreover, in order to resolve the over-fitting problem caused by data leakages in Non-independent and identically distributed (Non-i.i.d) data environment, we propose a federated augmentation mechanism with Wasserstein Generative Adversarial Networks (WGANs) algorithm for data augmentation. Federated augmentation empowers each device to replenish the data buffer using a generative model of WGANs until accomplishing an i.i.d training dataset, which significantly reduces the communication overhead in distributed learning compared to direct data sample exchange method. Numerical results reveal that the proposed federated learning based cooperation and augmentation (FL-CA) algorithm possesses a good convergence property, high robustness and achieves better accuracy of power allocation strategy than other three benchmark algorithms.

The radio frequency identification (RFID) technologies, which have been widely adopted in different Internet of Things (IoT) applications, are the fundamental building block for achieving smart factories, smart logistics, smart stores, etc. Besides knowing the ID of tags, the relative location information of different tags is of great importance since it contains the spatial relationship among different tags, which is essential for many object localization applications beyond the capacity of absolute localization approaches. In this article, we propose TagSort , an RFID-based sorting system that exploits the physical layer information, i.e., the phase of RFID wireless signals to achieve the relative localization of different tags. Several novel filtering and peak detection algorithms are proposed to achieve accurate and robust detection of the order of tags. Extensive evaluation shows promising results (over 95% accuracy) and make TagSort a promising system for future RFID sorting systems, thus enabling a variety of IoT applications and services.

The resilient output consensus problem for a network of switched heterogeneous linear vehicle systems with asynchronous switching, controller gain variation, unknown disturbance, and communication delay is investigated. Based on the topology decoupling technique, the investigated problem is transformed into two subproblems: one is a robust asynchronous static output feedback (SOF) control problem and the other is an asynchronous SOF problem. Based on the piecewise Lyapunov functional method and the average dwell time switching scheme, some new sufficient conditions are proposed for the solutions to those subproblems. Moreover, some novel linear-matrix-inequality-based conditions are developed to calculate the SOF controller gains. A network of six vehicles is finally presented to show the effectiveness of the proposed resilient consensus controller.