Link Constraints Research Articles

Energy Aware Virtual Network Migration

In network virtualization, one of the key problems is to embed a sequence of virtual networks with both node and link constraints onto the physical network, which is known to be NP-hard. Recent studies focus on how to minimize the energy cost while maximizing the revenue of the physical network when the VN request arrives. However, after a period of time, due to the dramatic dynamics of the resources of the physical network, the previous solution may become less energy efficient. In this paper, we study how to re-optimize the energy cost by leveraging the migration technique. In particular, we first give the problem model of virtual network migration. Then we design two energy aware virtual network migration algorithms called EA-VNM and EA-VNM-G. For EA-VNM, it answers the following key questions: when to perform migration, migrate which virtual nodes to where, and how to perform migration. Especially, for EA-VNM-G, it further reduces the high time complexity problem of EA-VNM by grouping the virtual nodes to be migrated with fewer conflicts. Extensive simulations show that EA-VNM significantly reduces the energy cost by up to 25% over the state-of-the-art algorithm while maintaining similar revenue and EA-VNM-G reduces the running time significantly.

Link-aware semi-supervised hypergraph

Hypergraph learning has been widely applied to various learning tasks. To ensure learning accuracy, it is essential to construct an informative hypergraph structure that effectively modulates data correlations. However, existing hypergraph construction methods essentially resort to an unsupervised learning paradigm, which ignores supervisory information, such as pairwise links/non-links. In this article, to exploit the supervisory information, we propose a novel link-aware hypergraph learning model, which modulates high-order correlations of data samples in a semi-supervised manner. To construct a hypergraph, a coefficients matrix of the entire dataset is first calculated by solving a linear regression problem. Then, pairwise link constraints are exploited and propagated to the unconstrained samples, upon which the coefficients matrix is adjusted accordingly. Finally, the adjusted coefficients are used to generate a set of the hyperedges, as well as calculate the corresponding weights. We have validated the proposed link-aware semi-supervised hypergraph model on the problem of image clustering. Superior performance over the state-of-the-art methods demonstrates the effectiveness of the proposed hypergraph model.

Near-Optimal Resource Allocation Algorithms for 5G+ Cellular Networks

Fifth-generation and beyond (5G+) systems will support novel cases, and hence, require new network architecture. In this paper, network flying platforms (NFPs) as aerial hubs are considered in future 5G+ networks to provide fronthaul connectivity to small cells (SCs). We aim to find the optimal association between the NFPs and SCs to maximize the total sum rate subject for quality of service, bandwidth, and supported number of link constraints. The formulated optimization problem is an integer linear program and the optimal association between the NFPs and SCs is found using numerical solvers at the expense of high computational complexity. We propose two algorithms (centralized and distributed) to reach a sub-optimal association at reduced complexity. Simulation results show that the performance of the proposed algorithms approaches the counterpart of its optimal solution and outperforms the state-of-the-art techniques from the literature.

Joint FSO Fronthaul and Millimeter-Wave Access Link Optimization in Cloud Small Cell Networks: A Statistical-QoS Aware Approach

We investigate resource optimization for the downlink cloud small cell network, where the baseband unit pool communicates with the buffer-aided small remote radio heads (SRRHs) through free space optical fronthaul, and SRRHs transmit to the user equipments (UEs) by using time division multiplexing-based millimeter wave access links. Our objective is to maximize the supportable aggregate data arrival rate in the network by exploiting the inter-dependence of fronthaul and access links. Toward this objective, we consider maximum acceptable end-to-end queue-length bound violation probability constraints, load-balancing constraints in the access link, fronthaul link selection constraints, and transmit power budget constraints of fronthaul and access links. Since the joint fronthaul and access link optimization is a non-convex and combinatorial problem, we develop an iterative solution by decomposing the original optimization problem into two sub-problems. The first sub-problem optimally obtains fronthaul and access link power allocation and fronthaul link selection by using Lagrangian dual decomposition and canonical one-to-one matching techniques. By employing the Lagrangian dual decomposition and alternating optimization techniques, the second sub-problem obtains near optimal data arrival rate for each UE, UE-SRRH associations, fronthaul rate allocation among the transmitted data for the UEs, and the transmission duration scheduling in millimeter wave access link. An algorithm of polynomial complexity is developed in order to determine the supportable aggregate data arrival rate by considering the statistical quality-of-service requirements, and its convergence is proved. The simulation results depict that the proposed scheme significantly improves the aggregate data arrival rate over several benchmark schemes.

Time-Expanded Graph-Based Resource Allocation Over the Satellite Networks

In this letter, we propose a transceiver resource allocation scheme based on the time-expanded graph (TEG) for the satellite networks. Due to the time-varying topology of the satellite networks, the design of the efficient transceiver resource allocation requires multiple nodes to make joint decision across time. To overcome this problem, the distribution of the transceiver resources is first depicted by the TEG. Then, we add some virtual nodes and edges in the TEG to represent the resources, and obtain the modified TEG, which can transform the nodes’ transceiver resources into the link constraints. Finally, the joint resource allocation will be implemented through solving the max-flow problem over the modified TEG. Numerical simulation results will also be presented to verify our proposed scheme.

Energy Efficient Resource Allocation Algorithm in Energy Harvesting-Based D2D Heterogeneous Networks

Energy harvesting (EH) from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many benefits to green communications. In this paper, we investigate the device-to-device (D2D) user equipments (DUEs) multiplexing cellular user equipments (CUEs) downlink spectrum resources problem for EH-based D2D communication heterogeneous networks (EH-DHNs). Our goal is to maximize the average energy efficiency of all D2D links, in the case of guaranteeing the quality of service of CUEs and the EH constraints of the D2D links. The resource allocation problems contain the EH time slot allocation of DUEs, power and spectrum resource block (RB) allocation. In order to tackle these issues, we formulate an average energy efficiency problem in EH-DHNs, taking into consideration EH time slot allocation, power and spectrum RB allocation for the D2D links, which is a nonconvex problem. Furthermore, we transform the original problem into a tractable convex optimization problem. We propose joint the EH time slot allocation, power and spectrum RB allocation iterative algorithm based on the Dinkelbach and Lagrangian constrained optimization. Numerical results demonstrate that the proposed iterative algorithm achieves higher energy efficiency for different network parameters settings.

Orchestrating Resource Management in LTE-Unlicensed Systems With Backhaul Link Constraints

Long term evolution (LTE)-unlicensed, an extension of LTE Advanced to unlicensed spectrum, can provide high performance and seamless user experience. To reap the full benefits of the LTE-unlicensed deployment, efficient resource allocation and interference management are critical to ensuring a harmonious coexistence between LTE-unlicensed and WiFi systems. In this paper, we study a resource orchestration scheme for an LTE-unlicensed network where small cells share the same unlicensed spectrum with a WiFi system. An optimization problem for channel and power allocations is formulated to maximize the overall network utility, which is an NP-hard problem. The problem is constrained on meeting the desired data rate demands of the served small-cell users, the capacity-limited backhaul links, and the maximum tolerable interference at the WiFi access point. To solve this challenging problem, a distributed solution based on Lagrangian relaxation is proposed to assist the LTE-unlicensed network in making decisions on channel allocation and transmit power. Furthermore, low-complexity solutions are devised upon applying the one-to-one matching game theory. The simulation results with practical parameter settings show that the proposed algorithms converge to the suboptimal solution after a small number of iterations in the considered examples.

Single or Multiple Frames Content Delivery for Next-Generation Networks?

This paper addresses the four enabling technologies, namely multi-user sparse code multiple access (SCMA), content caching, energy harvesting, and physical layer security for proposing an energy and spectral efficient resource allocation algorithm for the access and backhaul links in heterogeneous cellular networks. Although each of the above mentioned issues could be a topic of research, in a real situation, we would face a complicated scenario where they should be considered jointly, and hence, our target is to consider these technologies jointly in a unified framework. Moreover, we propose two novel content delivery scenarios: 1) single frame content delivery (SFCD), and 2) multiple frames content delivery (MFCD), where the time duration of serving user requests is divided into several frames. In the first scenario, the requested content by each user is served over one frame. However, in the second scenario, the requested content by each user can be delivered over several frames. We formulate the resource allocation for the proposed scenarios as optimization problems where our main aim is to maximize the energy efficiency of access links subject to the transmit power and rate constraints of access and backhaul links, caching and energy harvesting constraints, and SCMA codebook allocation limitations. Due to the practical limitations, we assume that the channel state information values between eavesdroppers and base stations are uncertain and design the network for the worst case scenario. Since the corresponding optimization problems are mixed integer non-linear and nonconvex programming, NP-hard, and intractable, we propose an iterative algorithm based on the well-known alternate and successive convex approximation methods. In addition, the proposed algorithms are studied from the computational complexity, convergence, and performance perspectives. Moreover, the proposed caching scheme outperforms the existing traditional caching schemes like random caching and most popular caching. We also study the effect of joint and disjoint considerations of enabling technologies for the performance of next-generation networks. On the one hand, our proposed caching strategy increases slightly the computational complexity by 3%, 1.1%, and 2% compared to no caching, SFCD, and disjoint solution, respectively. On the other hand, the proposed caching scheme achieves a performance gain of 43%, 9.4%, and 51.3% compared to the three state-of-the-art schemes.

An Improved Accurate Solver for the Time-Dependent RTE in Underwater Optical Wireless Communications

Underwater optical wireless communication (UOWC) has been widely advocated as a viable way to satisfy these high-speed links constraints in the marine medium through the use of the visible spectrum. Nevertheless, UOWC faces several limitations, such as the path-loss due to the absorption and scattering phenomena, caused by underwater particles. Thus, quantifying this path-loss is of paramount importance in the design of futuristic UOWC systems. To this end, several approaches have been used in this regard, namely the Beer–Lambert’s law, Monte Carlo simulation, as well as radiative transfer equation (RTE). This last mentioned evaluates the optical path-loss of the light wave in an underwater channel in terms of the absorption and scattering coefficients as well as the scattering phase function (SPF). In this paper, an improved numerical solver to evaluate the time-dependent RTE for UOWC is proposed. The proposed numerical algorithm was improved based on the previously proposed ones, by making use of an improved finite difference scheme, a modified scattering angular discretization, as well as an enhancement of the quadrature method by involving a more accurate seven-point quadrature scheme in order to calculate the weight coefficients corresponding to the RTE integral term. Importantly, we applied the RTE solver to three different volume scattering functions, namely the single-term Henyey–Greenstein (HG) phase function, the two-term HG phase function, and the Fournier–Forand phase function, over both Harbor-I and Harbor-II water types. Based on the normalized received power evaluated through the proposed algorithm, the bit error rate performance of the UOWC system is investigated in terms of system and channel parameters. The enhanced algorithm gives a tightly close performance to its Monte Carlo counterpart by adjusting the numerical cumulative distribution function computation method as well as optimizing the number of scattering angles.

Direct Trajectory Planning Method Based on IEPSO and Fuzzy Rewards and Punishment Theory for Multi-Degree-of Freedom Manipulators

Manipulator is a kind of commonly used multi-degree-of-freedom nonlinear system. There is a strong coupling between the links, and the movement of each link constraints and affects each other, which increases the difficulty of motion analysis of the system and reduces its trajectory planning efficiency under specific task targets. To solve this problem, a direct trajectory planning method based on an improved particle swarm optimization (PSO) algorithm, called IEPSO, and the fuzzy rewards and punishment theory is proposed in this paper. First, on the basis of preserving the local search ability of PSO, the global search ability of the population is improved by increasing a population exchange item. At the same time, in order to avoid the population falling into the local optimal value, the last elimination principle is incorporated into the standard PSO algorithm. Second, the fuzzy rewards and punishment theory is introduced to reduce the redundant decoupling operation, which can not only ensure the accuracy of manipulator trajectory planning but also effectively reduce the calculation amount of the trajectory planning for the multi-degree-of-freedom manipulator, to improve the optimization efficiency. Finally, the direct trajectory planning method of the multi-degree-of-freedom manipulator is compared and tested. It can be seen that the efficiency scalar and accuracy of the proposed direct trajectory planning method are significantly higher than those of other optimization methods.

Assignment of Virtual Networks to Substrate Network for Software Defined Networks

Assigning multiple virtual network resources to physical network resources, called virtual network embedding (VNE), is known to be non-deterministic polynomial-time hard (NP-hard) problem. Currently software-defined networking (SDN) is gaining popularity in enterprise networks to improve the customizability and flexibility in network management service and reduced operational cost. A central controller in SDNs is an important factor that we need to take care of when we want to assign virtual networks to physical resources. In this work, we address virtual network embedding problems for SDNs. Indeed, our objective is to propose a method to assign virtual networks in the substrate network with minimum physical resources, and also minimizing delays between the virtual network controller and the switches in the virtual network. Our proposed algorithm considers the link and node constraints such as CPU and bandwidth constraints which is necessary to consider when we try to solve virtual network embedding problems.

An Interactive Independent Topic Analysis for a Mass Document Review Service

In this paper, we propose an interactive constrained independent topic analysis in text data mining. Independent topic analysis (ITA) is a method for extracting independent topics from document data using independent component analysis. In this independent topic analysis, the most independent topics between each topic are extracted. By extracting the independent topic, managing documents with a large number of text data is easy with document access support systems and document management systems. However, the topics extracted by ITA are often different from the topics a user requests. For the system to be of service to users, an interactive system that reflects the user’s requests is necessary. Thus, we propose an interactive ITA that works for the user. For example, if there are three topics, i.e., topic A, topic B, and topic C, and a user choose the content from topics A and B, a user can merge those topics into one topic D. In addition, if a user wants to analyze topic A in more detail, a user could separate topic A into topics E and topic F. To that end, we define Merge Link constraints and Separate Link constraints as user requests. The Merge Link constraint is a constraint that merges two topics into one topic. The Separate Link constraint is a constraint that separates two topics from one topic. In this paper, we propose a method for extracting a highly independent topic that meets these constraints. We conducted evaluation experiments on our proposed methods, and obtained results to show the effectiveness of our approach.

Optimal traffic signal control under dynamic user equilibrium and link constraints in a general network

In this study, an optimal traffic signal control framework is proposed for finding the signal control settings that minimize the total travel time in a road network with traffic lights. A novel aspect of this framework is its integration of the continuous-time double queue traffic flow model in a signal controlled traffic network to capture queue spillbacks in continuous-time. Furthermore, drivers’ long term responses to changes in traffic signal control settings are captured by their route choices following Wardrop’s first principle, which results in the dynamic user equilibrium state. Two signal control strategies, the fixed-timing control and the adaptive signal control, are considered. A continuous approximation method for the signal control is applied to eliminate integer variables and enhance the computational efficiency. A heuristic genetic algorithm based solution procedure is proposed to solve the proposed nonlinear programming problem with time-varying delay terms. Numerical tests are conducted in two testing networks and the results show that adaptive control with drivers taking into account signal timing on their route travel times performs best, and in some cases nearly as well as the benchmark performance derived from system optimal control without equilibrium constraints. The results also show that the advantage of adaptive over fixed-time signal control is more pronounced under UE than SO routing behavior.

A Novel Optimal Mapping Algorithm With Less Computational Complexity for Virtual Network Embedding

Network virtualization (NV) is widely accepted as one enabling technology for future network, which enables multiple virtual networks (VNs) with different paradigms and protocols to coexist on the shared substrate network (SN). One key challenge in NV is VN embedding (VNE), which maps a VN onto the shared SN. Since VNE is NP-hard, existing efforts mainly focus on proposing heuristic algorithms that try to achieve feasible VNE in reasonable time, consequently the resulted embedding is not optimal. To tackle this difficulty, we propose a candidate assisted (CAN-A) optimal VNE algorithm with lower computational complexity. The key idea of the CAN-A algorithm lies in constructing the candidate substrate node subset and the candidate substrate path subset before embedding. This reduces the mapping execution time substantially without performance loss. In the following embedding, four types of node and link constraints are considered in the CAN-A algorithm, making it more applicable to realistic networks. Simulation results show that the execution time of CAN-A is hugely cut down compared with pure VNE-MIP algorithm. CAN-A also outperforms the typical heuristic algorithms in terms of other performance indices, such as the average VN request acceptance ratio and the average virtual link propagation delay.

A Virtual Network Embedding Algorithm Based on Cellular Automata Genetic Mechanism

The optimal embedding problem of virtual network requests, which satisfies nodes and link constraints, is a NP-hard problem. Heuristic algorithms solve the problem with the mathematical model optimization, but it fails to consider the influence of the virtual network embedding node itself on the optimal solution. So the cellular automata genetic mechanism is introduced into the problem, and the virtual network embedding algorithm based on cellular genetic algorithm (VNE-CGA) has been proposed. VNE-CGA uses the cellular automata to model the node, and replaces the "B4567/S1234" rule with the crossover operation in genetic algorithm. Through learning from neighbours to guide the individual's optimization process, VNECGA improves the inherent defects of traditional genetic algorithm. The experimental results show that the request acceptance ratio and the long-term average revenue increase about 5% and 12%.

Multi-Layer Downlink Precoding for Cloud-RAN Systems Using Full-Dimensional Massive MIMO

Full-dimensional (FD) massive multiple-input multiple-output (MIMO) has recently emerged as one of the physical layer enablers of 5G systems. Grounded on prior work on layered precoding, a novel framework for low-complexity multi-layer downlink precoding in multi-cluster cloud radio access network (C-RAN) systems using FD massive MIMO is introduced in this paper. The precoding matrix used at the cluster of remote radio heads (RRHs) associated to a given central control unit (CCU) of the C-RAN is decoupled as a multiplication of three precoding sub-matrices (or layers), a precoding architecture that leverages the special characteristics of the elevation component of the channel correlation matrix to manage both the C-RAN inter-cluster interference and the massive MIMO pilot contamination based on the availability of statistical channel state information. The proposed multi-layer approach is then adapted to the compress-after-precoding-based CCU-RRH functional split where the first and second precoding layers are locally applied at each of the RRHs in a cluster, whereas only the third precoding layer is implemented at the CCU. Optimal and suboptimal solutions are provided, which take into account both the per-RRH transmit power constraints and the capacity constraints of the fronthaul links between the CCU and the associated RRHs. The suboptimal approach, which is rooted on the vector normalization techniques used in massive MIMO precoding schemes with uniform power allocation, is shown to provide minor spectral efficiency losses when compared with the optimal solution. Numerical simulations are used to illustrate the potential of the proposed C-RAN-based framework when benchmarked against the classical FD massive MIMO scheme.

Control Link Load Balancing and Low Delay Route Deployment for Software Defined Networks

Software defined networking (SDN) separates the data plane and control plane on independent devices. Since the data plane, consisting of switches, is responsible for packets forwarding, previous work often considers the different constraints ( e.g. , data link capacity and flow-table size) only in the data plane to provide better QoS for users. However, due to limited CPU processing power and low speed of flow-table updating on each switch, the control channels/links between switches and the controller often have very limited capacity, which will cause QoS performance ( e.g. , response time and throughput) degradation when the switch should handle a high traffic load. The goal of our paper is to achieve better QoS by jointly considering the control link constraint and other different constraints of the data plane in SDNs. We formally define the control link load balancing and low delay route deployment problems, and prove the NP-Hardness. We present two algorithms with bounded approximation factors for each problem and implement the proposed methods on our SDN testbed. Extensive simulation results and experimental results show that our algorithms can reduce control link load by about 50% and response time by about 60%, and increase the network throughput by 65% compared with previous methods.

A performance evaluation of China's coal-fired power generation with pollutant mitigation options

Recently, performance evaluation of coal-fired power generation with high pollution based on data envelopment analysis (DEA) method has become an advanced research hotspot in China. Instead of a single process in previous studies, we divide the production system of coal-fired power plants into the production process and pollutants abatement process, with pollutant intermediates as the link to connect the two processes in the current paper. Then we present a new two-stage network DEA model to evaluate the performance of China's coal-fired power generation by improving the link constraint that different with traditional network DEA approaches. Meanwhile, we conduct an empirical study to verify the applicability of the proposed approach and find that the proposed model is more effective than the previous models in evaluating the inefficiencies of coal-fired power generation. The empirical results also show that: the inefficiencies of the pollutants abatement process bring poor performance of the regional coal-fired power plants; and the efficiency results vary greatly among different areas of China, efficiency is best in the west area but the worst in the northeast area. Finally, some appropriate propositions are present in order to create better conditions for China's sustainable development.

Electronic–Photonic Co-Optimization of High-Speed Silicon Photonic Transmitters

System-level driven electronic–photonic codesign is the key to improving the bandwidth density and energy efficiency for high-speed silicon photonic links. In many data-communication scenarios, optical link power is dominated by its transmitter side including the laser source. In this paper, we propose a comprehensive co-optimization framework for high-speed silicon photonic transmitters utilizing compact models and a detailed optical simulation framework. Given technology and link constraints, microring and Mach–Zehnder transmitter designs are optimized and compared based on a unified optical phase shifter model. NRZ and PAM4 modulation schemes are analyzed and compared for microring-based transmitters at 50 Gb/s. Multistage and traveling wave Mach–Zehnder transmitters are optimized and discussed as well. The results show that, for a 50 Gb/s NRZ optical link, an optimized microring transmitter could save more than 60% of the total laser and driver power compared to an optimized Mach–Zehnder transmitter under equivalent photonic technology constraints. For a given datarate and receiver sensitivity, design tradeoffs of silicon photonic processes, devices, and architecture choices are discussed in depth. In addition, this paper introduces a new Simulink toolbox for transient optical simulation. Combined with the proposed optimization engine, it provides an electrooptical co-optimization approach toward truly energy-efficient high-speed silicon photonic links.

Online assignment of non-SDN virtual network nodes to a physical SDN

Network virtualization is a promising solution to increase utilization of communication resources in data center networks. On the other side, software defined networks (SDNs) gather great attention from industry and academia to be used as the networking solution for data centers. Virtualizing SDNs and providing suitable virtual network embedding (VNE) techniques, paves the path for designing highly configurable virtualized cloud data centers. Considering the challenges which are arisen by using the SDNs as the substrate network for hosting non-SDN virtual networks, it is desired to develop an online VNE technique with a bounded competitive ratio as well as polynomial execution time in terms of problem size. To achieve this goal, this work is a primary step which solves the embedding problem with releasing the capacity constraints of physical network links. So, to the best of our knowledge, the problem of online virtual node assignment to physical nodes of SDNs with limited capacity of in-node and on-controller processing powers is presented for the first time in this work. Furthermore an online mapping algorithm is provided for the mentioned problem with poly-logarithmic competitive ratio. This can be considered as an online algorithm for general node assignment problem with batch arrivals of the requests and also can be used as a solution for online virtual machine allocation problem with batch arrivals of the requests.