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Integrated Planning of Tactical, Test Support, and Tactical Engagement Networks (IPT3N)

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Cellular networks, especially those used in the test and training domain, are often required to satisfy stringent performance requirements of coverage and throughput, while meeting resource constraints. These networks often operate in an environment where interference from transmissions in other networks operating in the same or neighboring areas may cause conflicts. To ensure that the network can reliably provide the service required of it, such conflicts must be identified and mitigated prior to deployment.

Integrated Planning of Tactical, Test Support, and Tactical Engagement Networks (IPT3N) is a network planning capability being developed under a project funded by the Science & Technology (S&T) Program of the Test Resource Management Center (TRMC). The primary objective of IPT3N is to aid in the planning of range networks, including those used in Operational Tests (OTs) or training events. OTs are expensive and cannot be rerun easily. Hence, it is critical that the laydown of the range network, typically referred to as the Test Support Network (TSN), that is used to collect relevant metrics on the operation of the Networked System Under Test (NSUT) be such that that it can reliably provide the required communication services for the Exercise Controller (ExCon).

Existing network planning tools are limited in their capabilities. Current tools that use coverage maps only account for received signal strength at the physical layer from a specific network but do not consider the dynamic impact on the application layer traffic volume and Quality of Service (QoS). Similarly, the existing software tools for determining cell tower or access point placement are insufficient as they typically handle range and capacity issues separately.

To address these gaps, SCALABLE is developing IPT3N to provide a capability for planning, optimizing, and visualizing cellular networks used on test and training ranges. IPT3N is a simulation-based framework that provides a suite of semi-automated tools to reduce the complexity of network planning for test and training ranges, thereby reducing costs and increasing realism of the Operational Test and Training events.

IPT3N employs simulation and optimization techniques to provide a planning capability that can propose an optimized network layout (base station locations) and network configuration (e.g., antenna height, slot allocation/transmission schedules) that can provide the required coverage and network capacity. The recommendations are based on the terrain and other features of the geographical area where the network is to be deployed and mobility patterns of the communication devices in the NSUT or network. IPT3N can also assess the energy requirements of the various components to determine whether the battery/energy resources are adequate for the required duration (e.g., the duration of a test or training event).

Figure 1. Use of IPT3N in Operational Test Planning

Major Technical Challenges

  • Efficient interference computation: As the number of transmitters and receivers increases, the cost of computation of interference among simultaneously operational networks grows exponentially. The problem is further compounded by interference due to transmissions in neighboring channels and environmental effects. IPT3N uses a mix of data-driven and statistical models together with parallel discrete-event simulation algorithms that can appropriately exploit Graphical Processing Units (GPUs) and multi-core processors to achieve the required computational efficiency.
  • Models for TSN waveforms: The TSN is responsible for relaying data collected during an OT to the test command center. IPT3N includes models for TSN waveforms, such as The ATEC Player and Event Tracking System (TAPETS), FlexTrain, and Home Station Instrumentation Training System (HITS).
  • Optimization of TSN deployment: Given the dynamic nature of force maneuvers, resource availability, and placement constraints, TSN deployment is particularly challenging. IPT3N uses semi-automated heuristics to select the most suitable TSN technology and layout for a given NSUT and expected traffic and mobility. IPT3N also supports dynamic re-planning of the TSN during a live test.

Major Features of IPT3N

  • A JNE-based simulation engine runs high-fidelity network simulations to gather detailed statistics, such as interference levels and network performance metrics, over time. JNE provides a suite of high-fidelity models for a variety of network technologies, including tactical waveforms. The simulation engine can federate with CGF simulation and training tools, such as OneSAF to provide an integrated simulation environment for analysis and optimization.
  • Using 2D and 3D heat maps of coverage and interference data obtained from simulations, a planner can easily determine if a network laydown provides the required coverage over the area(s) of interest. Simulation statistics can be analyzed to determine if measures of performance, such as coverage and throughput, are achieved.
  • The network laydown can be automatically modified, e.g., by changing tower locations, to improve the desired metric (coverage, throughput, energy consumption, etc.).
  • The optimization techniques used by IPT3N can also modify network configurations, for instance, by adding additional subnets to increase capacity. It leverages AI techniques and a database of network layouts and parameter configurations learned from past M&S studies, past planning tasks, or real-world deployments to propose optimal network layouts and configurations.
  • For planning OTs, traffic generation models provide realistic representations of traffic generated in field testing events, based on events and conditions which occur in the test event.
  • A thin-client web-based user interface allows multiple planners to concurrently perform planning, optimization, and analysis operations.
  • Map data (e.g., elevation and texture) can be streamed into IPT3N from online map data sources or IPT3N’s own Offline Map Server and rendered accordingly.

    • Example of Using IPT3N

    To illustrate the use of IPT3N in network planning, we show an example of iteratively refining a network layout (location of towers) to provide adequate coverage over the area of interest. In this example, troops move across a mountainous region and a network needs to be set up to provide coverage over the entirety of the paths taken by the troops (indicated by solid lines in Figure 2). The locations of towers are indicated by blue ^ symbols. Simulations are run for a given tower layout and performance metrics such as coverage and throughput over time are collected. Heat maps (shaded areas in Figure 2) show the coverage area of the towers. Coverage of troops over time is shown in Figure 3.

    The left part of Figure 2 shows the initial placement of the towers and the resulting radio coverage. The red line in Figure 3 shows the coverage over time for the initial placement. As can be seen, this layout does not provide adequate radio coverage as the troops move out of range of the towers (paths are not entirely covered by the heat map) and the coverage drops precipitously about halfway through the scenario.

    Figure 2
    Figure 3

    In the next iteration, the north most tower is moved closer to the troops’ paths. The right part of Figure 2 shows the adjusted tower locations and the resulting heat map which covers the paths completely. As the blue line in Figure 3 shows, the coverage is maintained at a high level throughout the scenario for this layout.

    This process demonstrates how IPT3N can be used iteratively to determine the best network laydown to meet the performance requirements.

    Impact of IPT3N

    A central requirement in test and training events is that the Test Support Network (TSN) provide sufficient coverage and bandwidth to ensure:

    • Personnel and platforms participating in the event can be monitored
    • All traffic needed to monitor the participants, both live and constructive, is delivered to the Exercise Control (ExCon) in a timely manner for accurate computation of Real-Time Casualty Assessment (RTCA)
    • The energy requirements of the various transmitters and other mobile equipment deployed as part of the TSN do not exceed battery capacity during the course of the Operational Test (OT)

     

    For a successful test or training event, the access points or towers of the TSN must be located such that they can meet the preceding requirements. On most ranges, towers are an expensive asset to deploy and monitor during the test, and as such they must be managed optimally: using more towers than needed will drive up the cost of the event, and having insufficient coverage may raise concerns on the validity of the data collected during the test. The primary goal of IPT3N is to provide an automated capability for planning and optimizing range network laydowns to meet specified coverage, bandwidth, and power consumption requirements.

    Network Planning Framework to Support Mobile Communications

    IPT3N can be used to determine the tower locations for an LTE network layout in Yosemite National Park. The park has numerous and varied terrain features that create a challenging and complex wireless environment. We are going to show how IPT3N can be used to automatically determine suitable tower locations based on the terrain and channel characteristics to create an LTE network layout to provide coverage to the area of interest while minimizing the amount of interference. Watch the webinar to learn:

    • Setting up test networks cost-effectively and efficiently using Integrated Planning of Tactical, Test Support, and Tactical Engagement Networks (IPT3N)
    • Using IPT3N to determine the tower locations for an LTE network layout in Yosemite National Park
    • How IPT3N can be used to automatically determine suitable tower locations based on the terrain and channel characteristics

    Key Features of EXata

    Real Time Emulator

    Seamlessly interface with other live equipment and applications

    Cyber Testing

    Test the resiliency of your network to Cyber Attacks

    Network Digital Twin

    Test your network in a low-cost, zero-risk environment

    Scalability

    Model thousands of nodes with parallel execution

    Model Fidelity

    Models simulate accurate real-world behavior

    Commercial enterprises, educational institutions, and governmental organizations around the world all depend on reliable, effective networks to deliver business-critical, mission-critical communications, and information. SCALABLE maintains a highly experienced group of technical professionals to support customers and projects of any scale and solve challenging problems with our advanced network digital twin technology.

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