Showing papers on "Network planning and design published in 2019"

Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network

Abstract: In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs and latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs’ deployment based on the notion of truncated octahedron shapes is proposed, which ensures full coverage for a given space with a minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs’ latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from an optimal transport theory. The simulation results show that the proposed approach reduces the latency of drone-UEs compared with the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared with the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.

Adaptive Event-Triggered Transmission Scheme and $H_{\infty}$ Filtering Co-Design Over a Filtering Network With Switching Topology

Abstract: This paper addresses the distributed adaptive event-triggered ${H}_{\infty}$ filtering problem for a class of sector-bounded nonlinear system over a filtering network with time-varying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. Moreover, the gain of the correction term in the adaptive rule varies directly with the estimation error and inversely with the transmission error. The switching filtering network is modeled by a Markov jump nonlinear system. The stochastic Markov stability theory and linear matrix inequality techniques are exploited to establish the existence of the filtering network and further derive the filter parameters. A co-design algorithm for determining ${H}_{\infty}$ filters and the event parameters is developed. Finally, some simulation results on a continuous stirred tank reactor and a numerical example are presented to show the applicability of the obtained results.

Network topology design at 27,000 km/hour

Abstract: Upstart space companies are actively developing massive constellations of low-flying satellites to provide global Internet service. We examine the problem of designing the inter-satellite network for low latency and high capacity. We posit that the high density of these new constellations and the high-velocity nature of such systems render traditional approaches for network design ineffective, motivating new methods specialized for this problem setting.We propose one such method, explicitly aimed at tackling the high temporal dynamism inherent to low-Earth orbit satellites. We exploit repetitive patterns in the network topology to avoid expensive link changes over time, while still providing near-minimal latencies at nearly 2× the throughput of standard past methods. Further, we observe that the geometry of satellite constellations admits more efficient designs, if a small, controlled amount of dynamism in links is permissible. For the leading Starlink constellation, our approach enables an efficiency improvement of 54%.

Vulnerabilities and Limitations of MQTT Protocol Used between IoT Devices

Abstract: With the proliferation of smart devices capable of communicating over a network using different protocols, each year more and more successful attacks are recorded against these, underlining the necessity of developing and implementing mechanisms to protect against such attacks. This paper will review some existing solutions used to secure a communication channel, such as Transport Layer Security or symmetric encryption, as well as provide a novel approach to achieving confidentiality and integrity of messages. The method, called Value-to-Keyed-Hash Message Authentication Code (Value-to-HMAC) mapping, uses signatures to send messages, instead of encryption, by implementing a Keyed-Hash Message Authentication Code generation algorithm. Although robust solutions exist that can be used to secure the communication between devices, this paper considers that not every Internet of Things (IoT) device or network design is able to afford the overhead and drop in performance, or even support such protocols. Therefore, the Value-to-HMAC method was designed to maximize performance while ensuring the messages are only readable by the intended node. The experimental procedure demonstrates how the method will achieve better performance than a symmetric-key encryption algorithm, while ensuring the confidentiality and integrity of information through the use of one mechanism.

On Network Design Spaces for Visual Recognition

Abstract: Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition.

Design and Deployment of UAV-Aided Post-Disaster Emergency Network

Abstract: Designing a reliable, resilient, and quickly deployable emergency communication network is a key challenge for post-disaster management. In this paper, a UAV-assisted emergency Wi-Fi network is proposed to expedite the rescue operations by guiding the survivors to the nearest rescue camp location. Here, the Raspberry PI (RPI) development board, mounted on UAV is considered to form a Wi-Fi chain network over the disaster region. During network set-up, the proposed solutions for the design challenges like UAV synchronization, avoid communication disruption and surveillance data management are the key contributions of this paper. The designed UAV network is capable of doing on-site surveillance and transmitting the data to the relief center for better rescue planning. One major challenge is to alert a survivor about the emergency network, which is addressed by designing a captive portal. Furthermore, to extend the Wi-Fi network, an Android-based application is developed by which each smartphone acts as a relay for its neighbor. Three types of field experiment are carried out to evaluate the performance of the designed prototype. It is found from the field results; the Wi-Fi access point mode and user datagram protocol are more suitable for network design as compared to Ad-Hoc mode and transmission control protocol, respectively. It is also observed from the experiment that the maximum hop distance for the prototype is 280 meters and 290 meters for a Wi-Fi configuration following IEEE 802.11n and IEEE 802.11ac protocol, respectively.

Robust reliable humanitarian relief network design: an integration of shelter and supply facility location

Abstract: The human societies are threatened by natural disasters. Thus, preparedness and response planning is necessary to eliminate or mitigate their negative effects. Relief network design plays an important role in the efficient response to the affected people. This paper addresses the problem of relief logistics network design under interval uncertainty and the risk of facility disruption. A mixed-integer linear programming model is proposed (1) to consider distribution center (DC) disruption (2) to support the disrupted DC by backup plan (3) to take in to the account both supply and evacuation issues (4) and finally, to mitigate disruption impact by investment. Moreover, robust optimization methodology is applied to hedge against uncertain environments. We conduct computational experiments by using generated instances and a real-world case to perform sensitivity analysis and provide managerial insights. The results show that the total cost of relief network increases by increasing the conservatism level. Moreover, the result show that the total cost of the network can be decrease by reducing the interval of uncertain parameters. As a result, providing more information and better estimation about uncertain parameters can reduce network costs. Disruption probability effect is also investigated and the result indicates that the network tries to establish more reliable facilities as the disruption probability increases. To demonstrate superiority of reliable network described in this paper over the classic network, a Monte Carlo procedure is used to compare two networks and results confirmed superiority of reliable network.

Integrated Railway Rapid Transit Network Design and Line Planning problem with maximum profit

Abstract: We solve the Integrated Network Design and Line Planning Problem in Railway Rapid Transit systems with the objective of maximizing the net profit over a planning horizon, in the presence of a competing transportation mode. Since the profitability of the designed network is closely related with passengers’ demand and line operation decisions, for a given demand, a transit assignment is required to compute the profit, calculating simultaneously the frequencies of lines and selecting the most convenient train units. The proposed iterative solving procedure is governed by an adaptive large neighborhood search metaheuristic which, at each iteration, calls a branch-and-cut algorithm implemented in Gurobi in order to solve the assignment and network operation problems. We provide an illustration on a real-size scenario.

Service network design with mixed autonomous fleets

Abstract: We propose a service network design problem for the tactical planning of parcel delivery with autonomous vehicles in SAE level 4. We consider a heterogeneous infrastructure wherein such vehicles may only drive in feasible zones but need to be guided elsewhere by manually operated vehicles in platoons. Our model decides on the fleet size and mix as well as on the routing of vehicles and goods. We observe cost savings and show that the strategies to coordinate a fleet using platooning depend upon the infrastructure, demand, and fleet mix. We discuss our results and identify areas for future research.

A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment

Abstract: This paper develops a multi-objective mixed robust possibilistic flexible programming (MOMRPFP) approach for the sustainable dry port network design under uncertain environment. The optimal number, location, and capacity of dry ports, and the optimal number of containers transferred through dry ports are determined with minimizing the economic costs and environmental and social impacts. Finally, numerical analyses indicate that the total network cost decrease about 1.14% with the proposed approach. Moreover, the MOMRPFP approach improves the efficiency about the average computational time for large-sized networks with the presence of uncertainty in flexible objective functions and constraints as well as in data.

On Network Design Spaces for Visual Recognition

Abstract: Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition.

Optimal Design for Distributed Secondary Voltage Control in Islanded Microgrids: Communication Topology and Controller

Abstract: This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains First, upon the consensus-based secondary voltage control, the sufficient condition for network connectivity of communication topology is revealed by the reachability matrix A multi-objective optimization criterion is first proposed for the network design, taking the convergence performance, network-relevant time delays, and communication costs into consideration After obtaining the Pareto frontier of this multi-objective model, an optimal network is selected to meet the practical requirements Based on static output feedback, a small-signal dynamic model of an MG installed with a secondary voltage controller is established, where the distributed secondary voltage controller can be converted into an equivalent decentralized controller Thereby, a linear quadratic regulator is formulated for the near-optimal design of controller parameters Our approach customizes the optimal design framework of the topology and controller, which have been largely ignored in the existing literatures Therefore, it promises to improve the performance of distributed secondary control The effectiveness of the proposed methodology is verified by a simulation study

Integrated transit route network design and infrastructure planning for on-line electric vehicles

Abstract: The emergence of electromobility along with recent developments in wireless power transfer (WPT) technology offer potentials to improve the carbon footprint of bus transport, while offering quality services. Indeed, the deployment of fast charging stations and dynamic charging roadway segments (lanes) can ensure fast energy transmission to electricity-powered buses, mitigating existing energy-related concerns and limitations. Existing models for public transport network design cannot adequately capture the dependence between electric vehicle charging infrastructure requirements and route operational characteristics. In this context, this paper investigates the combined Transit Route Network Design and Charging Infrastructure Location Problem and proposes a bi-level formulation to handle both planning stages. At the upper level, candidate route sets are generated and evaluated, while at the lower-level wireless charging infrastructures are optimally deployed. A multi-objective Particle Swarm Optimization (MO-PSO) algorithm embedded with an integer programming solver is employed to handle the complexity of the problem and the conflicting design objectives related to passengers and operators. The resulting model is applied to an established benchmark network to assess the tradeoffs arising between user-oriented and operator-oriented solutions and highlight the complex decision process associated with the deployment of electric public transport networks.

Low-margin optical networking at cloud scale [Invited]

Abstract: Every day, customers across the globe connect to cloud service provider servers with requests for diverse types of data, requiring instantaneous response times and seamless availability. The physical infrastructure which underpins those services is based on optics and optical networks, with the focus of this paper being on Microsoft’s approach to the optical network. Maintaining a global optical networking infrastructure which meets these customer needs means Microsoft must utilize solutions which are highly tailored and optimized for the application space which they address, with appropriately streamlined solutions for metropolitan data center interconnect and long-haul portions of the network. This paper presents Microsoft’s approach for tackling these challenges at cloud scale, highlighting the low-margin solutions which are employed. We provide a survey of Microsoft’s regional network design and corresponding optical network architectures, and present volumes of real-time polled metrics from the thousands of lines systems and tens of thousands of transceivers deployed today. We close by describing our approach to a unified software-defined networking toolset which ultimately enables the velocity and scale with which we can grow and operate this critical network infrastructure.

SDN-Based Load Balancing Scheme for Multi-Controller Deployment

Abstract: Software-defined networking (SDN) separates the control plane from the data forwarding plane and realizes the flexible management of the network resources. With the explosive growth of network traffic and scale, multi-controllers need to be deployed to improve the scalability and reliability of the control plane. However, unreasonable subdomain partitioning of SDN controllers may cause the unbalanced distribution of controller loads and reduces the communication performance of the network. Therefore, in this paper, a dynamic multi-controller deployment scheme based on load balancing is proposed. We transform the flow requests into a queuing model and consider the traffic propagation delay and the capacity of controllers as two main factors affecting the deployment of the multi-controllers. In the initial static network, a modified affinity propagation algorithm (PSOAP) based on particle swarm optimization is proposed to solve the problem of clustering performance being affected by the initial values of the bias parameters and convergence coefficients, getting the reasonable network planning. With the dynamic traffic network, switches in different sub-domains are reassigned by breadth-first search (BFS) algorithm to achieve controller load balancing. The extensive evaluations demonstrate that the scheme can provide better stable, accurate, and load balancing multi-controller deployment when compared with affinity propagation (AP) and genetic algorithms.

Fast and Practical Neural Architecture Search

Abstract: In this paper, we propose a fast and practical neural architecture search (FPNAS) framework for automatic network design. FPNAS aims to discover extremely efficient networks with less than 300M FLOPs. Different from previous NAS methods, our approach searches for the whole network architecture to guarantee block diversity instead of stacking a set of similar blocks repeatedly. We model the search process as a bi-level optimization problem and propose an approximation solution. On CIFAR-10, our approach is capable of design networks with comparable performance to state-of-the-arts while using orders of magnitude less computational resource with only 20 GPU hours. Experimental results on ImageNet and ADE20K datasets further demonstrate transferability of the searched networks.

Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China

Abstract: In 2017, Shenzhen replaced all its buses with battery e-buses (electric buses) and has become the first all-e-bus city in the world. Systematic planning of the supporting charging infrastructure for the electrified bus transportation system is required. Considering the number of city e-buses and the land scarcity, large-scale bus charging stations were preferred and adopted by the city. Compared with other EVs (electric vehicles), e-buses have operational tasks and different charging behavior. Since large-scale electricity-consuming stations will result in an intense burden on the power grid, it is necessary to consider both the transportation network and the power grid when planning the charging infrastructure. A cost-minimization model to jointly determine the deployment of bus charging stations and a grid connection scheme was put forward, which is essentially a three-fold assignment model. The problem was formulated as a mixed-integer second-order cone programming model, and a “No R” algorithm was proposed to improve the computational speed further. Computational studies, including a case study of Shenzhen, were implemented and the impacts of EV technology advancements on the cost and the infrastructure layout were also investigated.

MANOaaS: A Multi-Tenant NFV MANO for 5G Network Slices

Abstract: The dramatic densification of connected mobile devices and the expected use cases from the vertical industry demand an innovative network design that meets upcoming stringent requirements. The adoption and harmonized integration of novel concepts, such as network functions virtualization and network programmability, enables the system to master the high expectation -- from the fifth generation communication network in support of flexibility -- to provide tailored and mutually isolated network slices, high performance, agility, and automation. This effectively involves a number of technical challenges for managing and orchestrating physical and virtualized slice resources by means of an advanced management and orchestration (MANO) system. This article sheds light on potential benefits and implementation aspects when the MANO framework is abstracted into customized and distributed MANO instances, thereby empowering the MANO-as-a-service (MANOaaS) paradigm. In particular, such distributed instances are provided to different network tenants for a greater level of control on requested network slice(s). The notion of management level agreements in the context of MANOaaS is introduced as well as differentiated per tenant while being embedded into the proposed architecture. We also position the proposed MANOaaS concept and associated extensions to the MANO reference architecture from the viewpoint of standardization bodies and ongoing open source projects.

Demand-Aware Network Design with Minimal Congestion and Route Lengths

Abstract: Emerging communication technologies allow to reconfigure the physical network topology at runtime, enabling demand-aware networks (DANs): networks whose topology is optimized toward the workload they serve. However, today, only little is known about the fundamental algorithmic problems underlying the design of such demand-aware networks. This paper presents the first bounded-degree, demand-aware network, ct-DAN, which minimizes both congestion and route lengths. The designed network is provably (asymptotically) optimal in each dimension individually: we show that there do not exist any bounded-degree networks providing shorter routes (independently of the load), nor do there exist networks providing lower loads (independently of the route lengths). The main building block of the designed ct-DAN networks are ego-trees: communication sources arrange their communication partners in an optimal tree, individually. While the union of these ego-trees forms the basic structure of cl-DANs, further techniques are presented to ensure bounded degrees (for scalability).

Network Design for Accurate Vehicle Localization

Abstract: Network-based localization plays a key role on the introduction of emerging road applications, such as connected autonomous driving. These applications demand unprecedented precise, reliable and secure positioning, with localization requirements below 1 m. This stringent demand is pushing for the use of road-side units (RSUs) from fifth generation and vehicular networks for accurate vehicle localization. However, these networks are not typically designed for positioning but for data communication purposes, which follow a different paradigm for the network deployment and operation. This paper provides design guidelines on dedicated network deployments with the aim of achieving accurate vehicle-to-infrastructure positioning in road scenarios. First, the network layout or site placement is assessed with a geometrical metric. Then, the minimum density of RSUs along the road is bounded with line-of-sight probability models for urban street and highway scenarios. Finally, the Cramer–Rao bound for joint time-of-arrival (ToA) and angle-of-arrival (AoA) localization is used to maximize the distance between RSUs along the road, by exploiting multi-antenna deployments. According to the simulation results, the network sites are recommended to be located at alternate sides of the road, with a maximum distance between RSUs of 40 and 230 m for urban and rural environments, respectively. Following these design guidelines, there is also the need to exploit antenna arrays to combine uplink ToA and AoA estimates, in order to ensure a vehicle location accuracy below one meter on the 95% of the cases. The use of eight antenna elements at the RSUs is found to reduce nearly one and a half times the minimum network density in highway localization deployments.

Expanding EV Charging Networks Considering Transportation Pattern and Power Supply Limit

Abstract: For large-scale adoption of electric vehicles (EVs), a charging network is needed near the road. The charging station planning and deployment problem should consider the increasing penetration ratio of EVs over a long period of time, and the highly dynamic and location-dependent demands and power grid constraints. This paper focuses on the dynamic charging network design, i.e., how to optimize the charging station locations and the number of chargers in each station at different time stages with an increasing EV penetration ratio. For each candidate location, we first model its coverage area to estimate the dynamic EV charging requirements. Then, we formulate the problem at each time stage as profit maximization, which is a mixed-integer optimization problem. To make it tractable, we investigate the profitability of candidate locations and derive their upper and lower bounds on the expected profit. Then we take two steps to transform and relax the problem to convex optimization. A fast-converging search algorithm, named RMCL-E, is proposed. Using real vehicle traces, simulation results show that the proposed algorithm can make a good trade-off between the service blocking probability and the construction cost to maximize the total profit, which is attractive for charging service providers.

Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis.

Abstract: Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy. Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.