Increase Network Capacity Research Articles

Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches

The emergence of next-generation Internet-of-Things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.

Temperature Field Simulation and Structure Improvement of 12kV Switchgear

Aiming at the heating problem of 12kV switchgear, the model of its main circuit module was established and simplified. Then, the temperature field and airflow field were simulated by finite element analysis software ANSYS. Besides, the distribution of temperature and airflow in the circuit breaker room was analyzed respectively and the heat dissipation structure of the switchgear was improved. Finally, two axial fans were installed at the top of the switchgear and the ventilation hole was designed at the bottom. The simulation results showed that this method can accelerate the air convection and reduce the temperature rise effectively. With the continuous increase of distribution network capacity, switchgear would have overheating problem during the long-term operation. If this problem cannot be solved, it would affect its insulation performance and even cause safety accidents. This study provides a reference for the design of switchgear and solves its overheating problem.

User-Centric Resource Scheduling for Dual-Connectivity Communications

In this letter, we study adaptive user-centric resource scheduling management for dual-connectivity (DC) heterogeneous networks with different numbers of user terminals (UTs) and different quality-of-service (QoS) requirements. We present a general framework for adaptively selecting the connection mode between UTs and base stations (BSs) to maximize energy efficiency. Based on this framework, we propose a user-centric resource scheduling algorithm to further increase network capacity, including traffic offloading among heterogeneous access technologies and available resource allocation, which results in less traffic congestion and better QoS of UTs. Simulation results demonstrate that our proposed algorithm can significantly improve energy efficiency and throughput over state-of-the-art schemes.

Power Loss Reduction and Voltage Profile Improvement in Electrical Power Distribution Networks Using Static Var Compensators

<p class='IJASEITAbtract'>Rising demand for electrical power due to the global technological advancement has brought so many challenges such as instability of voltage, huge power loss, and unstable power factor on the distribution network. This work applied Static Var Compensator (SVC) to the power distribution network of Ado-Ekiti, Nigeria, to study its effect on active power loss reduction and voltage profile improvement of the network. The bus voltage, power, and the current flowing through the selected feeders were measured and recorded accordingly for analysis. Test network parameters like route length, transformer parameters, and maximum power flow were obtained from Benin Electricity Distribution Company, Ado-Ekiti, Nigeria. The distribution network was then modeled and simulated with and without SVC in NEPLAN software environment. The simulation results of the power flow and voltage stability analyses of the network without SVCs showed that some distribution lines were overloaded and that the network parameters were not within the statutory tolerable limits of 0.95 p.u. and 1.05 p.u. nominal voltage. There was 9.73% reduction in the active power loss when SVCs were incorporated into the test network. The voltage stability curve showed an increase in distribution network capacity from an initial steady-state of 150% to 263% of the total active load when the SVCs were incorporated. Hence, the need to normalize the network by applying SVCs to all the buses with very low voltages. This work will assist the power distribution supply companies in making some informed decisions in reducing power losses on their networks.

SeQUeNCe: a customizable discrete-event simulator of quantum networks

Recent advances in quantum information science enabled the development of quantum communication network prototypes and created an opportunity to study full-stack quantum network architectures. This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. Our simulator consists of five modules: hardware models, entanglement management protocols, resource management, network management, and application. This framework is suitable for simulation of quantum network prototypes that capture the breadth of current and future hardware technologies and protocols. We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories. The simulation capabilities are illustrated in three use cases. We show the dependence of quantum network throughput on several key hardware parameters and study the impact of classical control message latency. We also investigate quantum memory usage efficiency in routers and demonstrate that redistributing memory according to anticipated load increases network capacity by 69.1% and throughput by 6.8%. We design SeQUeNCe to enable comparisons of alternative quantum network technologies, experiment planning, and validation and to aid with new protocol design. We are releasing SeQUeNCe as an open source tool and aim to generate community interest in extending it.

Compositional modeling of railway Virtual Coupling with Stochastic Activity Networks

The current travel demand in railways requires the adoption of novel approaches and technologies in order to increase network capacity. Virtual Coupling is considered one of the most innovative solutions to increase railway capacity by drastically reducing train headway. The aim of this paper is to provide an approach to investigate the potential of Virtual Coupling in railways by composing stochastic activity networks model templates. The paper starts describing the Virtual Coupling paradigm with a focus on standard European railway traffic controllers. Based on stochastic activity network model templates, we provide an approach to perform quantitative evaluation of capacity increase in reference Virtual Coupling scenarios. The approach can be used to estimate system capacity over a modelled track portion, accounting for the scheduled service as well as possible failures. Due to its modularity, the approach can be extended towards the inclusion of safety model components. The contribution of this paper is a preliminary result of the PERFORMINGRAIL (PERformance-based Formal modelling and Optimal tRaffic Management for movING-block RAILway signalling) project funded by the European Shift2Rail Joint Undertaking.

FDD LTE MIMO CLOSED-LOOP VS OPEN-LOOP PERFORMANCE EVALUATION IN COMMERCIAL NETWORK

With a growing network traffic Mobile Network Operators (MNO) looking for ways to increase network capacity and improve customer experience. One of the ways is to find the best parameters from the set defined by 3GPP. In the study, closed-loop MIMO was compared to open-loop MIMO on the LTE FDD network. Network performance was evaluated in 3 different scenarios: slow and fast-moving UE under different SINR levels and large scale on 2T2R and 4T4R cells. The result shows gains of using closed-loop and it is recommended to use it commercial LTE networks.

AccelSDP: A Reconfigurable Accelerator for Software Data Plane Based on FPGA SmartNIC

Software-defined networking (SDN) has attracted much attention since it was proposed. The architecture of the SDN data plane is also evolving. To support the flexibility of the data plane, the software implementation approach is adopted. The software data plane of SDN is commonly implemented on a commercial off-the-shelf (COTS) server, executing an entire processing logic on a commodity CPU. With sharp increases in network capacity, CPU-based packet processing is overwhelmed. However, completely implementing the data plane on hardware weakens the flexibility. Therefore, hybrid implementation where a hardware device is adopted as the accelerator is proposed to balance the performance and flexibility. We propose an FPGA SmartNIC-based reconfigurable accelerator to offload some of the operation-intensive packet processing functions from the software data plane to reconfigurable hardware, thus improving the overall data plane performance while retaining flexibility. The accelerated software data plane has a powerful line-rate packet processing capability and flexible programmability at 100 Gbps and higher throughput. We offloaded a cached-rule table to the proposed accelerator and tested its performance with 100 GbE traffic. Compared with the software implementation, the evaluation result shows that the throughput can achieve a 600% improvement when processing small packets and a 100% increase in large packet processing, and the latency can be reduced by about 20× and 100×, respectively, when processing small packets and large packets.

Novel fragmentation-aware algorithms in space Division Multiplexing Elastic Optical Networks

Space Division Multiplexing Elastic Optical Networks (SDM-EONs) has been presented to provide flexibility and high speed transmission to increase network capacity and optimal use of network resources. Dynamic establishment and release of connections causes fragmentation in EONs, thus increasing blocking probability as a result of increased fragmentation. In this paper, we present three novel algorithms that focus on how to allocate resources in SDM-EONs under Multi-Core Fiber (MCF) in order to reduce blocking probability by reducing bandwidth fragmentation. The Core Classification Aware of Fragmentation (CCAF) is a method based on the core classification mechanism that uses the concept of demand splitting and a cost function to determine the appropriate spectral space for a new request. The Fragmentation Aware Spectrum and Core Assignment (FASCA-without priority) is a routing and spectrum allocation method that uses a cost function to specify the appropriate spectral space to improve spectrum efficiency and reduce fragmentation. The Fragmentation Aware Spectrum and Core Assignment (FASCA-with priority) is a routing and spectrum allocation method that considers priority of requests to improve spectrum efficiency and reduce fragmentation. Simulation results show that the proposed CCAF algorithm can achieve better performance compared with the FASCA-without priority and FASCA-with priority in terms of blocking probability and spectrum utilization. Moreover, FASCA-without priority can provide better performance than FASCA-with priority.

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs.

IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment’s topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features.

Channel Measurement for Multiple Frequency Bands in Subway Tunnel Scenario

In next-generation radio communication systems, the use of higher frequency bands and the massive multiple-input-multiple-output (MIMO) systems has turned into hot research topics because they have the potential to increase network capacity significantly by exploiting the available narrowband and broadband spectrums. Therefore, the narrowband channel measurements are executed at the following five potential frequency bands, including 2.6 GHz, 3.5 GHz, 5.6 GHz, 10 GHz, and 28 GHz in the Shanghai subway tunnel environment in order to fulfill the latest standards of fifth generation (5G). Moreover, in the broadband channel measurements, the center frequency is 3.5 GHz and 5.6 GHz and the bandwidth is considered as 160 MHz, respectively. At the transmitter (Tx) side, a uniform rectangular antenna array composed of 32 elements is fixed on the platform near the tunnel walls. The receiver (Rx) is equipped with a uniform cylindrical antenna array consisting of 64 elements, which is set on a trolley along the track. Based on the acquired massive MIMO channel impulse responses, delay spread, angle spread, eigenvalue and channel capacity are analyzed. The results reveal that the multipath delay in the tunnel scenario is quite short, the delay spread and angle spread drop rapidly as the distance between Tx and Rx increases and the channel matrix gradually becomes serious. This research provides a reference for the deployment of future 5G systems in the subway tunnel.

CGDNet: Efficient Hybrid Deep Learning Model for Robust Automatic Modulation Recognition

In this letter, we introduce CGDNet, a cost-efficient hybrid neural network composed of a shallow convolutional network, a gated recurrent unit, and a deep neural network, for robust automatic modulation recognition for cognitive radio services of modern communication systems. Our model employs pooling layers, small filter sizes, Gaussian dropout layers, and skip connections which leads to an increase in network capacity, a reinforced process of feature extraction, and prevents the vanishing gradient problem. From our experiments, CGDNet incurs a low computational complexity and reaches the overall n-modulation recognition accuracy of 93.5% and 90.38% on two widely used Deep-Sig datasets.

Cost-Effective Increase of Photovoltaic Electricity Feed-In on Congested Transmission Lines: A Case Study of The Netherlands

In many areas in the world, the high voltage (HV) electricity grid is saturated, which makes it difficult to accommodate additional solar photovoltaic (PV) systems connection requests. In this paper, different scenarios to increase the installed PV capacity in a saturated grid are assessed on the basis of the net present value (NPV). The developed scenarios compare an increase of grid capacity, PV system azimuth variation, curtailment, and battery storage. For each scenario the net present value (NPV) is assessed using an optimization model as a function of the overbuild capacity factor, which is defined as the relative amount of PV capacity added beyond the available capacity. The scenarios are applied on a case study of the Netherlands, and the analysis shows that, by optimising curtailment, a PV system’s capacity can be increased to 120% overbuild capacity. For larger overbuild capacity investments in the electricity-grid are preferred when these costs are taken into account. However, the optimum NPV lies at 40% overbuild, thus the societal and NPV optimum are not always aligned. Furthermore, the use of a battery system as an alternative to an infrastructure upgrade was not found to be a cost-effective solution. Thus, applying curtailment could be cost-efficient to a certain extent to allow for additional PV capacity to be connected to a saturated grid. Furthermore, the inverter size compared to the installed PV capacity should be significantly reduced. For a connection request that exceeds 120% overbuild increasing network capacity should be considered.

Pre-Filtering Techniques for Spectrum Narrowing Caused by Optical Node Traversal in Ultra-Dense WDM Networks

To improve the spectral efficiency and increase network capacity, wavelength signals must be multiplexed densely, i.e., ultra-dense wavelength-division-multiplexing (WDM) networks. However, the spectrum of each WDM signal is narrowed by the wavelength-selective switches placed in optical nodes, so the transmissible distance and node hop count of the signal are strictly limited. To counter this spectrum narrowing problem caused by optical node traversal in ultra-dense WDM networks, pre-filtering techniques have been proposed. This paper comprehensively investigates the performance of four pre-filtering techniques: conventional Nyquist filtering, pre-emphasis filtering, partial-response filtering, and the combination of pre-emphasis and partial-response filtering. We conduct extensive computer simulations and discuss the optimality of pre-filtering techniques. The simulation results show that the adaptive use of pre-filtering can substantially extend the maximum attainable transmission distance and hop counts of 4/16/64-QAM signals.

Analysis and Optimization of Channel Capacity Based on Modified IFR Algorithm

Massage passing can be communicated over different frequency channels at the same time. With the rapid development of the wireless system, more and more wireless applications have to compete for limited spectrum resources. These days, however, the spectrum vacancy is a serious issue that has aroused many studies. Therefore, how to make use of the resource properly has become a very significant problem to be mitigated. In this paper, we first study the frequency allocation by analyzing the capacity through using the Soft Frequency Reuse (SFR) system model applied in the Cellular Networks and building up a basic SFR model. Then we analyze the influence of the temperature by modifying the interference temperature model into a basic IFR model to find the impact on the environment. Finally, this result for IFR is extended to our SFR model to study the influence of the environment for SFR.In this model, as our expectation, with the ratio of cell radius over the center radius grows, the network capacity increases. However, due to the spectrum’s shortage, this upward trend does not emerge significantly when the ratio is over 0.75. Considering factors related to temperature, even in the simplest environment, the influence of thermal noise cannot be ignored. Therefore, it is crucial to put temperature into account when simulating a more complex model in an environment similar to the real world.

Joint Resource Allocation Optimization of Wireless Sensor Network Based on Edge Computing

Resource allocation has always been a key technology in wireless sensor networks (WSN), but most of the traditional resource allocation algorithms are based on single interface networks. The emergence and development of multi-interface and multichannel networks solve many bottleneck problems of single interface and single channel networks, it also brings new opportunities to the development of wireless sensor networks, but the multi-interface and multichannel technology not only improves the performance of wireless sensor networks but also brings great challenges to the resource allocation of wireless sensor networks. Edge computing changes the traditional centralized cloud computing processing method into a method that reduces computing storage capacity to the edge of the network and faces users and terminals. Realize the advantages of lower latency, higher bandwidth, and fast response. Therefore, this paper proposes a joint optimization algorithm of resource allocation based on edge computing. We establish a wireless sensor allocation model and then propose our algorithm model combined with the advantages of edge computing. Compared with the traditional allocation algorithm (PCOA, MCMH, and TDMA), it can further improve the resource utilization, reduce the network energy consumption, increase network capacity, and reduce the complexity of the schemes.

Security and Energy-aware Collaborative Task Offloading in D2D communication

Device-to-device (D2D) communication technique is used to establish direct links among mobile devices (MDs) to reduce communication delay and increase network capacity over the underlying wireless networks. Existing D2D schemes for task offloading focus on system throughput, energy consumption, and delay without considering data security. This paper proposes a Security and Energy-aware Collaborative Task Offloading for D2D communication (Sec2D). Specifically, we first build a novel security model, in terms of the number of CPU cores, CPU frequency, and data size, for measuring the security workload on heterogeneous MDs. Then, we formulate the collaborative task offloading problem that minimizes the time-average delay and energy consumption of MDs while ensuring data security. In order to meet this goal, the Lyapunov optimization framework is applied to implement online decision-making. Two solutions, greedy approach and optimal approach, with different time complexities, are proposed to deal with the generated mixed-integer linear programming (MILP) problem. The theoretical proofs demonstrate that Sec2D follows a [O(1∕V),O(V)] energy-delay tradeoff. Simulation results show that Sec2D can guarantee both data security and system stability in the collaborative D2D communication environment.

QuadScatter: Computational Efficiency in Simultaneous Transmissions for Large-Scale IoT Backscatter Networks

Backscatter communication has attracted attention owing to its ultra-low-power consumption ability, which is expected to enhance internet of things (IoT) technology that aims to enable many novel applications for object-to-object communication. Such a network with a large and continuously increasing number of connected objects will benefit significantly from resource-saving. This work introduces a system named QuadScatter, which is a set of algorithms that select and associate transmitters, tags and readers to enable simultaneous backscatter transmissions and increase network capacity. Consequently, the energy consumption in the network is considerably lessened. Intensive simulations have been conducted to demonstrate the effectiveness of backscatter simultaneous transmissions. QuadScatter shows promising results compared to the exhaustive search algorithm. The simulation results highlight computational time and simultaneous transmission improvements of at least $ 250\rm \times$ and $ 2\rm \times$ , respectively. Furthermore, while the exhaustive search is limited to a few nodes ( $ ), our proposal uses numerous nodes. Additionally, an implementation of limited simultaneous backscatter transmissions is conducted to show its feasibility in the real world.

Capacity of Vehicular Ad Hoc Networks Based on Multibeam Directional Transmission

The development of multibeam directional transmission technology used in vehicular ad hoc networks is drawing much more attention in recent years due to its wider coverage ability than omnidirectional transmission. In this paper, we analyse the transport capacity of the vehicular network using different antenna modes in the transmitter and receiver end, respectively. We first construct the cross‐layer model comprising the characteristic of the directional antenna model, arbitrary network model, and interference model. Then, based on scaling laws, we calculate the upper and lower bound of the network capacity with and without the directional multibeam transmission technology. In order to reduce the capacity lower bound computation complexity, several topology frameworks are constructed while taking various interferences into account included in the actual project. Finally, we analyse the capacity under changes of different parameters and also evaluate the law of capacity changes to discover how much improvement multibeam transmission technology can bring to the network performance. Analysis shows that compared with DTOR and OTDR mode, DTDR mode can continue to increase network capacity by 2 to 3 times on the basis of the above two modes.

ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation

Efficient modeling of high-dimensional data requires extracting only relevant dimensions through feature learning. Unsupervised feature learning has gained tremendous attention due to its unbiased approach, no need for prior knowledge or expensive manual processing, and ability to handle exponential data growth. Deep Autoencoder (AE) is a state-of-the-art deep neural network for unsupervised feature learning, which learns embedded-representations using a series of stacked layers. However, as the AE network gets deeper, these learned embedded-representations can deteriorate due to vanishing gradient, leading to performance degradation. This article presents ResNet Autoencoder (RAE) and its convolutional version (C-RAE) for unsupervised feature learning. The advantage of RAE and C-RAE is that it enables the user to add residual connections for increased network capacity without incurring the cost of degradation for unsupervised feature learning compared to standard AEs. While RAE and C-RAE inherit all the advantages of AEs, such as automated non-linear feature extraction and unsupervised learning, they also allow users to design larger networks without adverse effects on feature learning performance. We performed classification on learned embedded-representation to evaluate RAE and C-RAE. RAE and C-RAE were compared against AEs on MNIST, Fashion MNIST, and CIFAR10 datasets. When increasing the number of layers, C-RAE outperformed AE by showing significantly lower performance degradation of classification accuracy (less than 3%) compared to AE (33% to 65%). Further, C-RAE exhibited higher mean accuracy and lower variance of accuracy than standard AE. When comparing RAE and C-RAE with widely used feature learning methods (Convolutional AE, PCA, ICA, LLE, Factor Analysis, and SVD), C-RAE showed the highest accuracy.