Bandwidth Resources Research Articles

Multi-Domain Network Slicing in Satellite–Terrestrial Integrated Networks: A Multi-Sided Ascending-Price Auction Approach

With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.

Joint optimization of IoT devices access and bandwidth resource allocation for network slicing in edge-enabled radio access networks

Joint optimization of IoT devices access and bandwidth resource allocation for network slicing in edge-enabled radio access networks

Adaptive Self-Triggered Control for Multi-Agent Systems with Actuator Failures and Time-Varying State Constraints

This work focuses on the consensus problem for multi-agent systems (MASs) with actuator failures and time-varying state constraints, and presents a fixed-time self-triggered consensus control protocol. The use of time-varying asymmetrical barrier Lyapunov functions (BLF) avoids the violation of time-varying state constraints in MASs, ensuring stability and safety. Meanwhile, the system’s performance is further enhanced by leveraging the proposed adaptive neural networks (NNs) control method to mitigate the effects of actuator failures and nonlinear disturbances. Moreover, a self-triggered mechanism based on a fixed-time strategy is proposed to reach rapid convergence and conserve bandwidth resources in MASs. The mechanism achieves consensus within a predefined fixed time, irrespective of the system’s initial states, while conserving communication resources. Finally, the proposed method’s effectiveness is confirmed through two simulation examples, encompassing diverse actuator failure scenarios.

Blockchain‐assisted twin migration for vehicular metaverses: A game theory approach

AbstractAs the fusion of automotive industry and metaverse, vehicular metaverses establish a bridge between the physical space and virtual space, providing intelligent transportation services through the integration of various technologies, such as extended reality and real‐time rendering technologies, to offer immersive metaverse services for vehicular metaverse users (VMUs). In vehicular metaverses, VMUs update vehicle twins (VTs) deployed in RoadSide Units (RSUs) to obtain metaverse services. However, due to the mobility of vehicles and the limited service coverage of RSUs, VT migration is necessary to ensure continuous immersive experiences for VMUs. This process requires RSUs to contribute resources for enabling efficient migration, which leads to a resource‐trading problem between RSUs and VMUs. Moreover, a single RSU cannot support large‐scale VT migration. To this end, we propose a blockchain‐assisted game approach framework for reliable VT migration in vehicular metaverses. Based on the subject logic model, we first calculate the reputation values of RSUs considering the freshness of interaction between RSUs and VMUs. Then, a coalition game based on the reputation values of RSUs is formulated, and RSU coalitions are formed to jointly provide bandwidth resources for reliable and large‐scale VT migration. Subsequently, the RSU coalition with the highest utility is selected. Finally, to incentivize VMUs to participate in VT migration, we propose a Stackelberg model between the selected coalition and VMUs. Numerical results demonstrate the reliability and effectiveness of the proposed schemes.

Switching ETM-based neural adaptive output feedback control for nonaffine stochastic MIMO nonlinear systems subject to deferred constraint

This article focuses on the neural adaptive output feedback control study related to nonaffine stochastic multiple-input, multiple-output nonlinear plants. First, a K-filter state observer based on a radial basis function neural network is designed to estimate the remaining unavailable states. Then, a novel adaptive command-filtered backstepping output feedback control framework is established, where an improved command filter with a fractional-order parameter is applied to conquer the calculation size problem. Specifically, the highlight of this work is that it designs a modified error compensation signal and incorporates the concept of deferred constraint to eradicate the negative effect caused by the filter errors. In addition, the network bandwidth resources, control impulse, and control accuracy are synthesized using an amended switching event-triggered mechanism. The theoretical analysis proved that the proposed control approach guarantees that the tracking error can converge to a preassigned region within a user-defined time while the violation of the deferred output constraint can be excluded. Two illustrative studies are provided to demonstrate the validity and superiority of the developed control method.

Adaptive fault-tolerant control on complex networks with semi-Markov jump topology under random packet loss

ABSTRACT In this paper, semi-Markov process and adaptive fault-tolerant law are used to study the synchronization of complex networks in the presence of topology mutations and actuator failures. Firstly, the semi-Markov jump process, which is more practical than the traditional Markov jump process, is used to simulate the topology change of complex networks. Secondly, by designing adaptive fault-tolerant controller, the influence of actuator fault and interference on the system is eliminated. Then, considering the random packet loss caused by network channel congestion and limited bandwidth resources, a control strategy is developed to achieve convergence of the synchronisation error system. Finally, a numerical example and a practical example of Chua's circuit are given to verify the validity of the proposed theory.

Cost-efficient security-aware scheduling for dependent tasks with endpoint contention in edge computing

Edge computing empowers latency-sensitive and computation-intensive applications composed of multiple dependent tasks of mobile devices to execute on nearby edge servers. Despite the advantages, intermediate data across edge servers faces external security threats. Moreover, most existing works on task scheduling are designed on the idealized system model where communications are performed in parallel, they neglect the endpoint contention problem during data transmission, and thereby can hardly meet the user-specified deadline. To tackle these issues, this paper studies the endpoint contention-aware scheduling problem for dependent tasks to minimize the execution cost under the security and deadline constraints in the edge computing environment. We propose a heuristic algorithm named CSSDE that first distributes the deadline to each task and sorts tasks according to probabilistic endpoint contention-aware upward rank, and then allocates bandwidth resources and computation resources to intermediate data and tasks, respectively, finally performs rescheduling to improve the initial scheduling scheme. In addition, based on CSSDE and genetic algorithm, we present a metaheuristic algorithm called C-GA that considers the impact of task ordering and incorporates a multi-population co-evolution mechanism to enhance population diversity and avoid falling into local optimum for a better scheduling scheme. Simulation experiments are conducted on synthetic and real-world applications. The results demonstrate that our proposed algorithms achieve a higher success rate, and C-GA reduces the execution cost by 43.92, 40.52, and 8.56 percent compared with SCAS-E, ELSH, and CSSDE, respectively.

Service Function Chaining and Embedding With Heterogeneous Faults Tolerance in Edge Networks

In the 5G-and-beyond era, ultra-reliable low latency communication (URLLC) services are ubiquitous in edge networks. To enhance the performance metrics and the quality of service (QoS), URLLC services are delivered via a sequence of software-based network functions, also known as a service function chain (SFC). Towards reliable SFC delivery, it is imperative to incorporate fault-tolerance during SFC deployments. However, deploying an SFC with fault-tolerance is challenging because the protection mechanism needs to jointly consider multiple concurrent physical/virtual network failures and hardware/software failures. Considering these concurrent heterogeneous failures, this work investigates how to effectively deliver an SFC in edge networks with the objective of minimizing bandwidth resource consumption. First, we introduce the concept of k-heterogeneous-faults-tolerance and propose an augmented protection graph, called k-connected service function slices layered graph (KC-SLG). Based on the KC-SLG, we formulate a novel problem called k-heterogeneous-faults-tolerant SFC embedding and propose an effective algorithm, called fault-tolerant service function graph embedding (FT-SFGE). FT-SFGE employs two proposed techniques: k-connected network slicing (KC-NS) and k-connected function slicing (KC-FS). Via thorough mathematical proofs, we show that KC-NS is 2-approximate. Extensive simulations show that KC-FS has the best average cost-efficiency when k = 2, and FT-SFGE outperforms the schemes directly extended from the state-of-the-art.

An Optimized AMS Based Cloud Downloading Service with Advanced Caching and Intelligent Data Distribution Mechanism

The popularity of peer-to-peer video content downloading has surged due to diverse content availability and convenient sharing among users. However, scaling systems to accommodate the growing number of users and content items poses a challenge. This research aims to optimize video content downloading in peer-to-peer systems. The objective is to improve performance by developing advanced caching mechanisms, an intelligent data distribution algorithm, and efficient bandwidth resource management. The proposed approach involves implementing innovative caching mechanisms that store frequently accessed content closer to users, reducing download time. An intelligent data distribution algorithm minimizes bottlenecks and maximizes download speeds. Efficient bandwidth resource management ensures fair allocation. Results demonstrate significant enhancements in download time and overall system performance, leading to improved user experience. This research addresses the need for an optimized video content downloading system to handle increasing user and content volumes. The findings hold the potential to enhance user experiences, facilitate seamless video sharing, and advance peer-to-peer video content downloading.

ABLA: Application-Based Load-Balanced Approach for Adaptive Mapping of Datacenter Networks

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers’ side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA’s methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node’s resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates.

LEO satellite assisted UAV distribution using combinatorial bandit with fairness and budget constraints.

In this paper, an integration between a low earth orbit satellite (LEO-Sat) and unmanned aerial vehicle (UAV) is proposed to assist users in post-disaster areas. In this scenario, multiple UAVs will be distributed to fully cover the victims and provide rescue services, while LEO-Sat provides backhaul links for UAVs to the ground base station (GBS). In this regard, we consider the problem of efficient UAVs distribution to maximize the total sum rate of the victims while assuring fairness in their coverage within the limited resources of UAVs batteries and LEO-Sat bandwidth. In this paper, UAV distribution problem is considered as a combinatorial multi-armed bandit (MAB) with arms' fairness and limited UAVs battery budget (CMAB-FB) constraints. Additionally, the utilization of LEO-Sat bandwidth resources is optimized based on the average traffic demands of the LEO-UAV links by means of gradient decent algorithm. The results of numerical analysis indicate that the proposed approach outperforms other naïve ben chmarks.

A Review of High-Power Semiconductor Optical Amplifiers in the 1550 nm Band.

The 1550 nm band semiconductor optical amplifier (SOA) has great potential for applications such as optical communication. Its wide-gain bandwidth is helpful in expanding the bandwidth resources of optical communication, thereby increasing total capacity transmitted over the fiber. Its relatively low cost and ease of integration also make it a high-performance amplifier of choice for LiDAR applications. In recent years, with the rapid development of quantum-well (QW) material systems, SOAs have gradually overcome the shortcomings of polarization sensitivity and high noise. The research on quantum-dot (QD) materials has further improved the noise characteristics and transmission loss of SOAs. The design of special waveguide structures-such as plate-coupled optical waveguide amplifiers and tapered amplifiers-has also increased the saturation output power of SOAs. The maximum gain of the SOA has been reported to be more than 21 dB. The maximum saturation output power has been reported to be more than 34.7 dBm. The maximum 3 dB gain bandwidth has been reported to be more than 120 nm, the lowest noise figure has been reported to be less than 4 dB, and the lowest polarization-dependent gain has been reported to be 0.1 dB. This study focuses on the improvement and enhancement of the main performance parameters of high-power SOAs in the 1550 nm band and introduces the performance parameters, the research progress of high-power SOAs in the 1550 nm band, and the development and application status of SOAs. Finally, the development trends and prospects of high-power SOAs in the 1550 nm band are summarized.

Partitioning multi-layer edge network for neural network collaborative computing

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition.

Sustainable Resource Allocation and Reduce Latency Based on Federated-Learning-Enabled Digital Twin in IoT Devices.

In this article, we utilize Digital Twins (DT) with edge networks using blockchain technology for reliable real-time data processing and provide a secure, scalable solution to bridge the gap between physical edge networks and digital systems. Then, we suggest a Federated Learning (FL) framework for collaborative computing that runs on a blockchain and is powered by the DT edge network. This framework increases data privacy while enhancing system security and reliability. The provision of sustainable Resource Allocation (RA) and ensure real-time data-processing interaction between Internet of Things (IoT) devices and edge servers depends on a balance between system latency and Energy Consumption (EC) based on the proposed DT-empowered Deep Reinforcement Learning (Deep-RL) agent. The Deep-RL agent evaluates the performance action based on RA actions in DT to distribute its bandwidth resources to IoT devices based on iteration and the actions taken to generate the best policy and enhance learning efficiency at every step. The simulation results show that the proposed Deep-RL-agent-based DT is able to exploit the best policy, select 47.5% of computing activities that are to be carried out locally with 1 MHz bandwidth and minimize the weighted cost of the transmission policy of edge-computing strategies.

Event-based adaptive tracking control for robotic systems with deferred position constraints and unknown backlash-like hysteresis

This paper proposes an event-based adaptive tracking control scheme for the n-link robotic systems in the presence of unknown backlash-like hysteresis (BLH) and deferred position constraints. By combining a transformation error with an asymmetric Lyapunov function, the devised control tactic achieves that the position constraints of robotic systems are not violated after user pre-specified time. In contrast to the results of robotic systems with position constraints, this paper removes a common assumption condition generated by the conventional barrier Lyapunov function method. Then, the adverse effect of unknown BLH can be offset by the Nussbaum function. Meanwhile, an event-triggered mechanism is designed to economize on the network bandwidth resources. Finally, based on the Lyapunov theory, an event-based adaptive tracking control tactic is proposed to ensure that all the signals of robotic systems are bounded under unknown BLH and deferred position constraints. Some simulation results proof that the devised control scheme is valid.

Boosting Erasure-Coded Multi-Stripe Repair in Rack Architecture and Heterogeneous Clusters: Design and Analysis

Large-scale distributed storage systems have introduced erasure codes to guarantee high data reliability, yet inevitably at the expense of high repair costs. In practice, storage nodes are usually divided into different racks, and data blocks in storage nodes are often organized into multiple stripes independently manipulated by erasure code. Due to the scarcity and heterogeneity of the cross-rack bandwidth, the cross-rack network transmission dominates the entire repair costs. We argue that when erasure code is deployed in a rack architecture, existing repair techniques are limited in different aspects: neglecting the heterogeneous cross-rack bandwidth, less consideration for multi-stripe failure, and no special treatment on repair link scheduling. In this paper, we present CMRepair, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cross-rack Multi-stripe Repair</i> technique that aims to reduce the repair time for multi-stripes failure repair in heterogeneous erasure-coded clusters. CMRepair first carefully chooses the nodes for reading/repairing blocks and searches for the multi-stripe repair solution. It adopts different algorithms to adjust per stripe solution, including the Computation Time Priority (CTP) algorithm based on the greedy idea and the Repair Time Priority (RTP) algorithm based on the meta-heuristics idea. Furthermore, CMRepair selectively schedules the execution orders of cross-rack links, with the primary objective of saturating the unused upload/download bandwidth resources and avoiding network congestion. The experiments show that CMRepair with the CTP algorithm can reduce 27.59%-58.12% of the repair time while only introducing negligible computation overhead, and CMRepair with the RTP algorithm can reduce 33.52%-97.75% of the repair time in an acceptable computation time, over existing repair techniques.

Value of Service Maximization in Integrated Localization and Communication System Through Joint Resource Allocation

The rapid proliferation of smart devices and Internet of Things (IoT) applications have brought significantly increased demands for concurrent sensing, localization and communication services. To achieve multiple functions concurrently, new unified wireless systems including integrated localization and communication (ILAC) and integrated sensing and communication (ISAC) are facing the fundamental challenge of integrative resource allocation among coexisting functions and services. In addressing this challenge, an ILAC system based on the efficient allocation of the common hardware and radio resource pool for localization and communication is proposed. A novel concept, termed Value of Service (VoS), is coined to maximize the unified performance of ILAC system for diverse service provisioning including localization accuracy and communication data rate. Furthermore, the bandwidth and temporal resource allocation problem is formulated for ILAC to maximize its VoS. Specifically, the problem is treated as a mixed-integer nonlinear problem solved by an iterative joint resource allocation (JRA) strategy. In each iteration, the resource allocation is decomposed into two steps. Firstly, the bandwidth resource is optimized with a Kelly mechanism-based continuous allocation method followed by discretization. Secondly, the temporal resource is assigned with the aid of an adaptive particle swarm optimization (PSO)-based approach. Simulation results demonstrate the significant superiority of our proposed VoS evaluation metric and JRA method in ILAC system under limited resources.

Deep Learning for mmWave Beam-Management: State-of-the-Art, Opportunities and Challenges

Benefiting from huge bandwidth resources, millimeter-wave (mmWave) communications provide one of the most promising technologies for next-generation wireless networks. To compensate for the high pathloss of mmWave signals, large-scale antenna arrays are required both at the base stations and user equipment to establish directional beamforming, where beam-management is adopted to acquire and track the optimal beam pair having the maximum received power. Naturally, narrow beams are required for achieving high beamforming gain, but they impose enormous training overhead and high sensitivity to blockages. As a remedy, deep learning (DL) may be harnessed for beam-management. First, the current state-of-the-art is reviewed, followed by the associated challenges and future research opportunities. We conclude by highlighting the associated DL design insights and novel beam-management mechanisms.

A Decision for Throughput Optimization in UAV-Enabled Emergency Outdoor–Indoor Fairness Communication

This paper investigates the throughput optimization strategy in an unmanned aerial vehicle (UAV)-enabled emergency outdoor–indoor fairness communication scenario, with the UAV as a mobile relay station in the air, to provide outdoor–indoor communication services for users inside buildings. The occurrence of severe signal fading caused by outdoor transmission loss through wall loss as well as indoor transmission loss when the UAV forwards the information to the indoor users reduces the channel gain and degrades the system downlink throughput. To improve the downlink throughput of the system and ensure communication fairness for indoor users, we designed a joint UAV location deployment and resource allocation (JLRO) algorithm that optimized UAV three-dimensional (3D) deployment location, power and bandwidth resource allocation. The simulation results demonstrate the convergence and validity of the proposed JLRO algorithm, as well as its superiority compared to benchmark algorithms.

Explore deep reinforcement learning for efficient task processing based on federated optimization in big data

In recent years, along with the extensive application of consumer electronics, the task execution with cloud computing for big data has become one of the research focuses. Nevertheless, the traditional theories and algorithms are still employed by existing research work to explore the feasible solutions, which takes a beating from low generalization performance, system load imbalance, more response delay, etc. To solve the matter, a task execution method called DROP (Deep Reinforcement network aided Optimization method aiming at task Processing) has been put forward, which is capable of completing task request allocation through virtual network embedding. The prominence of this method is explained by its effect in reducing load balancing degree, minimizing bandwidth resource overhead, and preserving electric energy as well as meeting customer demands. It makes use of Deep Deterministic Policy Gradient (DDPG) instead of depending on tons of iterations for better path selection schemes in previous methods, through continuous environment interaction and trial-and-error evaluation to get better strategy selection for virtual link embedding. To realize the virtual node embedding in the federated optimization based system architecture, the intentional deep feature learning network is applied. Compared with the cutting edge approaches, the performance benefits of DROP can be verified by the experimental results in terms of bringing down the extra cost on resources and energy of the substrate network during the task execution for big data.