When planning cellular networks, the goal is to position antennas and adjust their parameters to maximize the coverage and minimize the interference between antennas. In new generations of cellular networks like 5G, network planning is becoming critical due to the use of high frequencies and the densification of the network. Antenna locations are typically decided based on the availability of cellular towers, however, tunable parameters, like tilt and transmission power, determine the network capacity and the quality of service. To optimize the network and compute coverage and interference for different tilt and power values, radio propagation is simulated using ray tracing over a geospatial model of the environment. In this paper, we present a ray-tracing module that computes long-distance effects of cellular transmissions, to accurately model interference between remote antennas. The two main novelties are (1) the computation of diffraction to include non-line-of-sight propagation, and (2) parallel computation for efficiency and scalability. The demonstration presents the geospatial effect of diffraction on the computation and the use of GPUs for scalability while avoiding race condition in the transformation from a polar coordinate system to a Cartesian representation.

Planning of cellular networks is a process in which network engineers select locations for the cellular antennas and adjust parameters like transmission frequency, transmission power and tilt. The goal of network planning is to provide cellular coverage in places that are populated while decreasing interference between nearby antennas. To compute the coverage and interference, a geospatial model of the environment is created and radio propagation models are used for simulating the propagation of the electromagnetic waves. In this paper, we demonstrate a ray tracing tool that we have developed for computation of radio propagation. The tool computes the radio propagation over a 3D model of the world and presents the result as a heat map, in real time. The two main novelties of the system are (1) the ability to play the radio propagation as a video, for analyzing the effect of obstacles on the signal strength in different locations, and (2) inverse ray tracing which finds for a given location the antennas whose transmission affects the cellular signal or the interference at that location.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers’ view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

The growing demand for high-speed networks is increasing the use of high-frequency electromagnetic waves in wireless networks, including in microwave backhaul links and 5G. The relative higher frequency provides a high bandwidth, but it is very sensitive to obstructions and interference. Hence, when positioning a transmitter-receiver pair, the line-of-sight between them should be free of obstacles. Furthermore, the Fresnel zone around the line-of-sight should be clear of obstructions, to guarantee effective transmission. When deploying microwave backhaul links or a cellular network there is a need to select the locations of the antennas accordingly. To help network planners, we developed an interactive tool that allows users to position antennas in different locations over a 3D model of the world. Users can interactively change antenna locations and other parameters, to examine clearance of Fresnel zones. In this paper we illustrate the interactive tool and the ability to test clearance in real-time, to support interactive network planning.

In telecommunication networks, microwave backhaul links are often used as wireless connections between towers. They are used in places where deploying optical fibers is impossible or too expensive. The relatively high frequency of microwaves increases their ability to transfer information at a high rate, but it also makes them susceptible to obstructions and interference. When deploying microwave links, there should be a clear line of sight between every pair of receiver and transmitter, and a buffer around the line of sight defined by the first Fresnel zone should be clear of obstacles. In this paper we discuss the geospatial aspects of microwave backhaul planning and the challenges in developing a system for large scale planning, with the following requirements: (1) the need to cover all of the USA, (2) distance of up to 80 kilometers between towers, and (3) computing batches of thousands of pairs within a few minutes.

Three-dimensional models of buildings have a variety of applications, e.g., in urban planning, for making decision where to locate power lines, solar panels, cellular antennas, etc. Often, 3D models are created from a LiDAR point cloud, however, this presents three challenges. First, to generate maps at a nationwide scale or even for a large city, it is essential to effectively store and process the data. Second, there is a need to produce a compact representation of the result, to avoid representing each building as thousands of points. Third, it is often required to seamlessly integrate computed models with non-geospatial features of the geospatial entities.

The exact calculation of all‐terminal reliability is not feasible in large networks. Hence estimation techniques and lower and upper bounds for all‐terminal reliability have been utilized. Here, we propose using an ordered subset of the mincuts and an ordered subset of the minpaths to calculate an all‐terminal reliability upper and lower bound, respectively. The advantage of the proposed new approach results from the fact that it does not require the enumeration of all mincuts or all minpaths as required by other bounds. The proposed algorithm uses maximally disjoint minpaths, prior to their sequential generation, and also uses a binary decision diagram for the calculation of their union probability. The numerical results show that the proposed approach is computationally feasible, reasonably accurate and much faster than the previous version of the algorithm. This allows one to obtain tight bounds when it not possible to enumerate all mincuts or all minpaths as revealed by extensive tests on real‐world networks. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 66(4), 282–295 2015

Using IEEE 802.15.4 and Zigbee for home area networks (HANs) in the Smart Grid is becoming an increasingly prominent topic in the research area. As the standard designed for low data rate and low cost wireless personal area networks, IEEE 802.15.4 is widely employed in the construction of home sensor networks to assist with real-time environment information. For the purposes of Smart Grid the Zigbee Alliance has defined new Smart Energy Profile Protocol that leverages the existing TCP and HTTP protocols. In this paper, we provide an overview of the Smart Grid's Advanced Metering Infrastructure (AMI) and Demand Response (DR) functionalities, and the communication requirement they pose for the new SEP protocol. The discussion is followed by an evaluation of the theoretical performance bounds of the new architecture based on a analytical model. We conclude, by extending the model to account for WiFi interference which is expected to be present in home and office environments.

Models to predict the future location of users have been developed in the past few decades. However, these efforts cannot drive applications related to location-based targeting since they focus on flat geographic prediction with no semantic information. With the emergence of Location Based Social Ne