[American Institute of Aeronautics and Astronautics AIAA Infotech@Aerospace 2010 - Atlanta, Georgia...

American Institute of Aeronautics and Astronautics

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Quantitative Capability Delivery Increments: A Novel Approach for Assessing DoD Network Capability

Craig M. Burris1 Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723

Dan Gonzales2 RAND, Arlington, VA 22202

Jimmie G. McEver, III,3 David T. Signori,4 and Mike D. Smeltzer5 Evidence Based Research, Inc., Vienna, VA 22182

and

Heather Schoenborn6 Office of the Assistant Secretary of Defense for Networks and Information Integration, Arlington, VA 22202

Although Joint network enabled operations promise the DoD improved agility and effectiveness in dealing with a wide range of conflicts, missions & situations, such operations pose significant challenges for decision makers faced with the job of identifying major gaps in network capability and the potential contribution of investment alternatives to mission success. Traditional analysis methods based on information exchange requirements have been found to be resource intensive, time consuming, and often limited by the experience of the supporting subject matter experts who are unable to anticipate either the situations that might arise or the manner in which new capabilities and business processes might evolve over time. In a paper to appear elsewhere, the authors describe a new but complementary approach to estimate future demand for network capability based on the premise that aggregate demand for network capability is driven by trends in communication devices used to access the network. That paper describes the Quantitative Capabilities Delivery Increments (QCDI) demand model and tool developed to meet DoD’s need to project future network demands of military units. This paper proposes a flexible approach for applying the QCDI demand model to assess the adequacy of network capability offered by existing, programmed, planned or proposed capabilities to meet the needs of a variety of units in appropriate mission contexts. Key elements of the approach include: (1) relating the capability offered by a system or program to the demand for types of devices and classes of users by appropriately parsing the associated demand to match the needs of the problem being addressed; and (2) aggregating capabilities associated with component systems and programs within a portfolio to estimate overall network capability supplied to units. This paper describes a multi-level methodological framework that can account for interactions among programs or program elements to varying degrees depending on the data, time and resources available. Two illustrative applications, each with incrementally increasing analytical complexity are included. Extensions of the methodology and related research necessary to determine mission implications of capability gaps or degradation due to a spectrum of threats are also discussed briefly.

1 Project Manager, Applied Information Sciences, 21-S200, 11100 Johns Hopkins Rd., Member 2 Senior Scientist, Technology and Applied Sciences, 1200 South Hayes Street, Member 3 Scientist, C3 Futures, 1595 Spring Hill Rd., Suite 250, and Senior Member 4 Chief Scientist, C3 Futures, 1595 Spring Hill Rd., Suite 250, and Associate Fellow 5 Senior Scientist, C3 Futures, 1595 Spring Hill Rd., Suite 250 6 Senior Engineer, Systems Engineering and Integration, 1550 Crystal Drive, Suite 1000

AIAA Infotech@Aerospace 2010 20 - 22 April 2010, Atlanta, Georgia

AIAA 2010-3550

Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc. Under the copyright claimed herein, the U.S. Government has a royalty-free license to exercise all rights for Governmental purposes. All other rights are reserved by the copy-right owner.<NULL>


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I. Introduction ITH over 2.1 million active military and civilian personnel and a budget in excess of $600 billion a year, the U.S. military is one on the largest and most complex organizations in the world by virtually any measure.7

The legal and policy constraints governing the equipping of this force necessitate specifying the parameters of future systems years ahead of planned delivery. Increasing mandates to view capability needs from a Joint and in some cases enterprise perspective (in addition to service-specific imperatives8) only add to the complexity and difficulty of determining the network needs of the future force.9

With threats that vary from global terrorist movements, insurgencies in failed states, and major nation states, a key element of the DoD strategy10 for addressing the new environment is to transform to a net enabled agile force that can span the full spectrum of crisis and conflict, ranging from natural disasters through irregular warfare to major conventional operations. It is recognized at the highest levels in DoD that to be successful in this endeavor commanders and warfighters must be provided with a Joint network that provides a decisive advantage over adversaries. This network must be resilient to attack and robust in performance across the full range of situations that might be encountered—from traditional roles such as support of convoy operations as shown in Figure 1,11 to secure connection of the newest unmanned sensor platform with tactical edge ground forces. Determining the specific levels of performance required for this type of network poses great challenges for decision makers at all levels and, in particular, for the analysts that advise them. They grapple with questions such as: how much capability is enough to assure mission success? How might degraded network performance impact the force’s ability to employ preferred methods, accomplish essential tasks, and achieve desired end states? Or, ultimately, how can investment alternatives be weighed in the context of mitigation of mission risks? Because of the critical role of the Joint network, it is important to understand the impact of network capability on mission success when making key decisions related to investment, system design and development, and the operational plans that these systems support.

Many commercial endeavors owe their success in large part to the ability to obtain a clear understanding12 of how information technology and networks can enable innovative business processes that provide a quantifiable competitive advantage in the global market place. There are a host of network based innovations intended to attract and keep customers.13 When evolving such enterprises, companies attempt to understand not only the role of their networks in gaining competitive advantage, but also how the size and performance of their network contribute to their ultimate measure of mission success.14

7 Office of the Secretary of Defense, DoD Personnel & Procurement Statistics, http://siadapp.dmdc.osd.mil. Accessed 17 December 2009). 8 Owens, William A. and James R. Blaker, “Overseeing Cross-Service Trade Offs,” Joint Forces Quarterly, Autumn 1996. 9 The Joint Staff, CJCSI 6212.01 series, “Interoperability and Supportability of Information Technology Systems and National Security Systems.” 10 Office of the Secretary of Defense, DoD Information Management & Information Technology Strategic Plan 2008-2009. pp. 1-10. 11 Source: U.S. Marine Corps photo, “All Four Services Participate in Convoy Operations,” URL: https://www.us.army.mil. Accessed 12 January 2010. 12 Straussman, Paul A., The Squandered Computer:Evaluating the Business Alignment of Information Technologies, The Information Economics Press, New Canaan Connecticut, 1997. 13 Friedman, Thomas L., The World is Flat; A Brief History of the 21ST Century, Picardi Reading Group Guide, 2007. 14 Straussman, Paul A., The Business Value of Computers, Information Economics Press, New Canaan, Connecticut, 1990, pp. 225-251.

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Figure 1. Human integration of convoy operations using Jointnetwork components.


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The DoD, which admittedly differs from commercial industry in some important ways, has struggled with mixed success for years to relate information systems to mission outcome. Decision-makers have often been forced to resort to ad hoc prioritization of requirements that bubble up from below with little quantitative understanding of how the related programs contribute to mission success. The growing importance of a capable global Joint network in enabling force agility and cyber operations has increased the need to better understand and ultimately quantify the role of the network in meeting mission needs. A first step in better understanding the role of future networks is achieving consistent and repeatable estimates of future demand across DoD. The Quantitative Capability Delivery Increments (QCDI) demand model provides an analytic framework and tool set to meet this need. After reviewing the key features of QCDI, this paper describes a flexible methodology for achieving the second step. That is applying QCDI to assess the adequacy of Joint network capability supplied by existing, programmed or proposed systems or programs. Research is underway on methods and tools to achieve the third and final step of determining the mission implications of gaps in capability; particularly those that might occur in realistic high threat environments. Emerging results regarding this Network Mission Assurance initiative will be reported in future papers.

II. Overview of the QCDI Demand Model and Methodology The QCDI Demand model and tool are described in more detail in a forthcoming paper in another forum,15 but

the key elements are summarized below:

• Functional Segments of the Joint Network: The choice of functional capability areas is based on the Joint Capability Area concepts developed to serve as DoD’s capability management framework. The JCA taxonomy16 for the top-level Net Centric capability identifies four key net centric capability areas: Information Transport, Net Management, Information Assurance and Enterprise Services, that formed the basis for the identification of capability areas for QCDI demand.

• Metrics: Metrics were chosen to be user oriented, widely applicable, and easy to apply. Table 1 shows metrics chosen for the various functional segments of the Joint network. In this paper, emphasis is placed on metrics for information transfer, which include not only typical data rates, protected data rates as well as upload and down load rates but also data rates for users at-the-halt or on-the-move.

• User Areas and Classes of Users: Users are grouped into classes with similar net-work demands. These classes vary with operational domain and echelon or Tier. Table 2 shows the user class structure used in the QCDI model.

15 Burris, Craig M., Dan Gonzales, Jimmie McEver, Isaac Porche, Heather Schoenborn, David Signori, and Stephen Sudkamp, “Quantitative Capability Delivery Increment (QCDI) demand model: a novel approach for estimating future DoD network needs,” Proceedings of the 15th International Command and Control Research and Technology Symposium, Santa Monica, CA, June 2010 (forthcoming). 16 The Joint Staff, Joint Capability Areas Framework, Joint Force Development and Integration Division (JFDID) website, http://www.dtic.mil/futurejointwarfare/strategic/jca_framework.xls. Accessed 8 April 2010.

Table 1. QCDI Metrics by Capability Area

Information Transport

Enterprise Services

• Typical Required Data Rate • Protected Communications Data Rate• Voice Data Rate • Availability (%) • Voice Packet Delivery Ratio (%) • Packet Delivery Ratio (%) • Communications Set-up Time (max) • Data End-to-End Delay (max) • Voice End-to-End Delay (max) • Upload (%) • External Traffic (%)

• Amount of Assured Data Storage • Service Discovery Request Rate • Chat Request Rate • Authentication Service Request

Rate • Email Request Rate • Search Request Rate • File Delivery Request Rate • DNS Request Rate • Service Discovery

Response Time

Information Assurance

Network Management

• Cross-Domain Transfer Time • Validation Time • Authorization Management Time • Pedigree Production Rate (%) • Data-at-rest Compromise Time • Compliant COMSEC Tier • Incident Detection Time • Incident Response Time

• Interoperability Depth (Higher Network Tiers)

• Response Time • Time to Refresh Contextual SA • Priority Information Delivery Mgt

(%) • Connection Resilience (%) • End User Device RF Spectrum

Efficiency • RF Spectrum Reallocation Time


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• Types of Units: Using data from official tables of organization and equipment, the QCDI model currently characterizes 360 military organizations across echelons and from all services. The number and type of users associated with specific units are provided to enable the calculation of aggregate demand for the unit.

• Time Frames: in order to be able to reflect trends in demand, QCDI model accounts for demand in three time frames that correspond to those in the Capability Description Increments , which characterize the evolution of the Joint network capability and provides a point of reference for change architectural assumptions; i.e. 2012, 2016 and 2020.

Since major units can be characterized by classes of users and the number of users in each class, computing aggregate demand is very tractable. So, for example, in the case of information transport metrics, in addition to individual and aggregate data rate required, the model projects demand of users at the halt and the move, their uplink and downlink data rates, and the amount of data sent outside or sent into the user area. Such estimates are produced for users that employ both LOS and BLOS Devices to gain direct access to the Joint network as well as those who gain indirect access through a shared mechanism such as a local area network connected to a network router. The definitions used for these types of demand in the QCDI Demand model are as follows:

• Direct Beyond-Line-of-Sight (BLOS): User demand directly supported through a BLOS wireless device (generally direct use of a low data rate SATCOM terminal).

• Direct Line-of-Sight (LOS): User demand directly supported through use of line-of-sight (LOS) wireless device. • Indirect: User demand not directly supported by a wireless receiver or transmission device. This demand is

aggregated with demand from other users before transport outside of local area networks by either LOS or BLOS capacity.

Appropriate values for metrics were developed by applying the following a number of general guidelines. Key examples include: (1) Estimated demand should be consistent with user expectations. Military users will demand what others have, particularly civilians or adversaries who also have access to commercial technology. (2) Demand values should not be dependent on knowing exactly how systems are supposed to be used. Users will find new ways to leverage network capabilities as the demands of the situation change. (3) Demand estimates should be mitigated by what is feasible. Capabilities to satisfy demand should be potentially available in the time frame of interest. Architectural assumptions should be kept to a minimum.

The 2009 version of the QCDI model has over 20,000 values for metrics that reflect demand across the entire framework. In order to fully utilize the model, a user friendly tool with a graphical interface was developed. It provides the flexibility to compile demand for any unit selected; drill down to illuminate demand for specific user classes, device types, metrics and time frames throughout the space of demand. It also includes stochastic tools that help explore the impact of variation and uncertainty in key parameters and demand estimates. A web based version of this tool is available to authorized users.

III. The General Approach to Supply versus Demand Assessment Within DoD, capabilities are normally fielded to units versus individuals. This allows the unit commander to

prioritize and employ resources in ways that best fit the mission at hand. For network related capabilities,

Table 2. User areas and user classes in QCDI demand model. Commander, Static Sensor, and ES/NM/IA Infrastructure users are characterized for all user areas.

Core Intermediate Tactical Edge Terrestrial/

Ground Area A

Local Worker CP High

USS High UAS High

Area B Dismounted

Ground Surface Mobile Local Worker

CP High CP Low

USS High USS Low UAS High UAS Low

Area C Dismounted

Ground Surface Mobile Local Worker

CP High CP Low


Airborne Area D C2 Air ISR Air

UAS High

Area E LO Air

Mobility Air TAC Air

UAS High

Maritime Area F Surface Mobile Local Worker

CP High CP Low


Area G Surface Mobile Local Worker

CP High CP Low

USS High USS Low UAS Low


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prioritization of resources could result in certain units receiving more spectrum, bandwidth or even terminal devices than other units. Since capabilities are supplied at the unit level, it follows that supply vs. demand comparisons should normally be conducted at the unit level as well. There are certain analytic benefits to assessing supply versus demand at the unit level, including the ability to capture a moderate or better size sampling of individual demand and the accompanying variation. This facilitates statistically meaningful aggregation of demand metrics such as typical data rate for a unit—a metric frequently used in various analysis and decision processes. In order to support this framework, the QCDI model is optimized to provide demand for individual units and task forces comprised of collections of units. The smallest echelon units available in the QCDI model include the company for ground forces, the squadron for air forces, and an individual ship for maritime forces. At the other extreme, supply versus demand analysis has been conducted using the QCDI for all units in a theater of operations.

Since the QCDI model/tool represents a very flexible capability to project network demand by users throughout the DoD enterprise, it can be applied to address a range of problems and issues that confront DoD decision makers. The initial focus of the QCDI initiative was on assessing the adequacy of portfolios of capabilities programmed in various time frames to support units in major combat operations; identifying gaps in capability and determining the degree to which proposed solutions mitigate the shortfall. It has served as the basis for a number of study efforts that focused on specific programs, or types of programs intended to support one or more classes of users in selected user areas. In these efforts the size and mix of force, types of devices and metrics vary widely. The primary measure of merit in these efforts was the degree to which aggregate supply exceeded the associated demand. The key steps of the general approach for addressing such problems are outlined below.

• Define the issue in both programmatic and operational terms; e.g. the degree to which a particular device, program or set of programs comprising an architecture satisfies the demand of a type of unit or mix of units associated with a type of operation. Identify the relevant functional domain(s), device type(s) and key demand metric(s) that will be used for the assessment.

• Characterize the supply architecture(s) at issue in terms of additional dimensions of the QCDI demand framework; e.g. the types and numbers of devices provided, relevant modes of operation and how they will be used or configured to support a typical unit.

• Estimate the aggregate supply provided by the mix of programs to the relevant units. This assessment can be done at different levels of sophistication, depending upon the data and time available, and the nature of program and architectural considerations that need to be accounted for given the nature of the systems and the nature of the analytic problem. Assessments are generally conducted by estimating the aggregate supply associated with the relevant components of the program or programs being assessed, identifying key architectural and system engineering assumptions regarding important interactions among devices or programs, and finally, aggregating supply across all devices and programs in the architecture

• Determine the appropriate demand from the QCDI model to serve as a point of reference for the assessment. In some cases, it may be necessary to parse the demand of users further to map portions of demand (e.g., from subsets of users) to the systems and programs in the architecture.

• Compare aggregate supply with aggregate demand for each of the metrics chosen. In addition to this high-level comparison, it is usually desirable to drill down to compare supply with demand at a more detailed level to ensure that high-level results are not due to the fact that some user classes have excess supply while others are shortchanged. This additional analysis can identify which sets of users may need additional capability.

This process can be repeated to explore alternative solutions to fill gaps and conduct sensitivity analysis.

As indicated above, to make a bandwidth demand vs. capacity comparison, the bandwidth demand must be attributed to systems that can satisfy these bandwidth demands in a manner consistent with the properties of the demand. These properties include whether the user is at-the-halt (ATH) or on-the-move (OTM), the device type on which the user created the demand, and subsequent assumptions on what capacity providers can satisfy that demand. For the purposes of the types of analysis described in this paper, each capacity provider is generally assigned to the demand source it most typically supports. It is important to note, however, that as tactical military communications systems are modernized away from stovepipe solutions toward net-centric systems, the allocation of capacity to demand sources is not completely pre-determined or fixed. Here, supply sources may need to be partitioned among a number of different types of demand supported.


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IV. Methodology for Estimating Aggregate Supply for Mix of Programs Although a template could be crafted to help structure and populate a data base with programmatic information

necessary for estimating the capability supplied by these programs, modeling the aggregate supply provided by a set of programs supporting major units is extremely challenging. The interactions among programs that comprise the Joint network are complex and vary with the network architecture, system configurations and manner in which they are used. For example the capacity supplied by one program can affect the capacity supplied by another program, because on an end-to-end basis, both play a role. It is seldom the case that network centric capabilities provided by one program don’t interact with network centric capabilities provided by one or more other programs. It is this interaction among combinations of programs that challenges the supply side estimation process. Often additional system engineering assumptions, judgment and analysis are required to project the network capability that could be provided for a type of operation or mission. In order to deal with this notion - the analysis of multiple programs viewed as a single entity - a useful idea is a step wise approach in which complexity and constraints to supply capability are added incrementally by accounting for more interaction. By so doing the quality of the estimates can be improved in a way that illuminates the limiting factors. At the low end, a program stands alone, but at the more complicated high end, combinations of programs not only interact with each other but are also impacted by external demands for a shared resource. This idea of different levels of analysis is captured by defining the concept of maturity levels for supply estimation.

A. Maturity Levels for Supply Estimates Maturity levels account for the different degrees of interaction among network centric (NC) programs supplying

NC capability to users. As we move from one maturity level to the other, the engineering analysis gets more sophisticated and more costly in terms of time and resources. In general it is easier to begin analysis at maturity level one and enhance the results as we learn more and move up the maturity scale.

Level 1: The first maturity level evaluates capacity that is supplied as a function of programs and associated devices, independent of network constraints. The analysis is based on the capabilities as specified in program specifications and documentation. For example, consider a notional tactical radio program that provides a communications capability with one, two and four channel radios. The Level 1 analysis considers the specified capability, as defined in program documentation, of the individual devices assuming they stand alone, and there is an infinite unconstrained network capacity. Subsequently, this demand can be aggregated across the program for all devices within the program. So one might aggregate the demand associated with a 1-channel radio with that from two and four channel radios. The orientation for Level 1 is on the individual program components.

Level 2: The second maturity level begins to add some constraints to the information analyzed at Level 1. At this level, we introduce the idea that devices do not operate independently of each other, but, in fact, the use of one device may affect the capacity of another device when they are operating on the same network. For example, an unconstrained handheld device may be capable of communicating with a single other device at the maximum data rate that waveform permits. However when several of these devices are on the same subnet, the radio bandwidth supplied is actually much lower. The orientation for Level 2 is on the system of individual components from within a program.

Level 3: The third maturity level extends the potential constraints to those imposed by the communications architecture and other interacting network centric program. For example, the capacity supplied by an On-The-Move SATCOM capability has to examine the limitations of both the satellite and the mobile satellite terminal.(or hand held satellite radio).simultaneously. Even if there is only one radio using one radio channel on a subnet, the total capacity of the subnet may not be achievable when accessing the satellite because the satellite waveform may limit the supply to something less than the radio can accommodate. The orientation at Level 3 is on a system of systems originating from different programs.

Figure 2. Maturity Levels for Estimating Supply

Maximum NominalTrans-

ProgramDemand-Loaded

FactorsAccounted

For

Performance of Program

Components

Performance of Program Networks

Program Interfaces

DemandLoads

IncreasedQuality

ofEstimate

Data generally available from

programs

Data available from studies

Extensions requiring richer data and other elements of

analysis


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Level 4: The fourth maturity level addresses considerations associated with the impact of the demand itself on the supply provided by programs and systems of interest. There are three general classes of effects considered. (1) The effects of demand as a load on the supply networks and devices; since units may have demand that is different than the design loads considered by the programs, it may be important to understand variations in performance or capacity that are likely to result. (2) The effects of other programs and users that are not explicitly part of the demand or supply being studied; other programs and their users may be contending for shared resources such as spectrum or SATCOM capability, and other users may require some portion of the capability provided by the programs and devices of interest, reducing what is available to users in the unit of interest. (3) Other effects in which the nature and structure of demand affects the ability of programs to provision it (e.g., the amount of demand that can be satisfied by broadcast is limited to some portion of the external, download demand of the unit). The orientation at Level 4 is on the accounting for impact of demand at both the unit and enterprise levels.

B. Specific Methods for Estimating Aggregate Supply This section describes formulas, provides simple illustrations and identifies the type of engineering and

operational factors that must be considered to generate credible estimates of aggregate supply for a major unit at each level of estimation maturity.

1. Estimation Methods for Level 1 The first step is to develop supply information at the program level for the capabilities being assessed. Program

documentation should provide details on what each program and program element17 being assessed provides, and should also be of assistance in determining the type of demand (Direct LOS, Direct BLOS, or Indirect) to which the capability maps. Note that some program elements will only support a single type of demand, whereas other could provide capability to multiple demand types.

This program information will be used to complete tables such as Tables 3 and 4 below for each program of interest. For the simple example below, consider a tactical radio program that has one-channel and two-channel radios for dismounted personnel and 4 channel radios for vehicles, all providing capability that would be mapped to the Direct LOS type of demand. This program would be represented by three devices, each with a documented capability.

The next step is to determine the number of elements of each type providing capability to the unit of interest. The analyst needs to examine the unit’s table of organization and equipment, available architectures, program fielding plans, and other documents to identify the information needed for Table 4.

Using data rate as the metric for an illustrative example, these two tables can be multiplied together to determine the total data rate supplied to a unit by the tactical radio program. This next step of analysis involves incrementally aggregating across all of the supply devices of a particular type supporting the unit (e.g., 36 Type 1 devices each providing 0.6 Mbps of data rate capacity yields 21.6 Mbps provided by all Type 1 devices), across all of the devices associated with a particular program supporting the unit, and across all of the programs supporting the unit. This process yields an aggregate data rate supply of 21.6 + 450.0 + 72.0, or 543.6 Mbps for this notional program.

The simple technique described above can be used to generate estimates of supply provided by devices whose configuration and performance remain constant over time. However, in the case of tactical ground, on-the-move (OTM) and at-the-halt (ATH) communications capabilities, some equipment can do one, some can do the other, and some can do both. If a piece of equipment can do both with different data rates but not simultaneously, separate

17 For example, the Joint Tactical Radio System, or JTRS, will provide a number of different types of systems, including handheld radios, backpack-sized man-transportable units, vehicle-mounted units, etc. Each of these JTRS components has its own capability, which must be accounted for when assessing the capability provided by the JTRS program as a whole.

Table 3. Exemplar data rates provided by three program elements of a notional tactical radio program

PROGRAM DATA RATES

Device Type 1

Device Type 2

Device Type 3

LOS 0.6 Mbps 1.2 Mbps 2.4 Mbps BLOS n/a n/a n/a

Indirect n/a n/a n/a

Table 4. Quantity of program elements providing capability to an illustrative unit of interest.

UNIT DEVICE QUANTITIES

Device Type 1

Device Type 2

Device Type 3

LOS 36 375 30 BLOS n/a n/a n/a

Indirect n/a n/a n/a


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calculations may need to be made to characterize the supply provided in OTM and ATH situations before meaningful comparisons with demand can be made.

Notice that at this level these formulas are simply an aggregation of the results of calculations for separate devices as they support an entire major unit. However, formulas can be generated to estimate supply for a specific classes of users or mix of users that comprise a particular type of unit of any size (for example, how well does the supply provided by a particular program as fielded to support a sub-element of a unit, such as dismounted ground users, support the data rate needs of those users?). This requires that the number and type of devices that support various classes of users be known, but is necessary to explore details of how capability is provided to subsets of users within a unit of interest.

2. Estimation Methods for Level 2 Level 1 supply estimates consider only the capabilities provided by the individual program elements without

regard to adjustments to performance that may be introduced when the elements that comprise a program are assembled and used in typical deployment configurations. In most cases, such configurations will constrain the performance of the program elements in ways that should be taken into account in generating realistic estimates of program capability. For example, in certain tactical radios, it has been observed that when configured in typical subnet structures, the effective data rate capacity of individual handsets is far less than the single-pair point-to-point transmission capacity the handsets could have in isolation (as reported by the program). We have developed two general methods for incorporating these factors into a Level 2 supply estimate.

The best approach is to leverage a study that has determined the actual performance of the devices when deployed. Such studies may be available from the program office, from an independent honest broker, or not at all.

When no information is available, it may be possible to apply engineering judgment to estimate the effect of a constraint. In the illustrative case of the tactical radios discussed above, each channel across the program elements is capable of delivering 0.6 Mbps in isolated, point-to-point mode. However, when configured into a realistic subnet structure as anticipated by future program architectures, 30-device subnets are anticipated to provide a shared capacity of 200 kbps – resulting in a device-level data rate supply that could be as low as:

200 / / 30 / 7 / (1)

Therefore, to get a more realistic estimate of supply capacity, 7 kbps should be used in the calculations above rather than 0.6 Mbps for each channel. The other devices would need to be adjusted similarly.

3. Estimation Methods for Level 3 Level 3 estimation techniques account for the interaction of multiple programs as manifested in such phenomena

as gateway effects and other kinds of capacity constraints. For example, in the case of the supply provided by tactical satellite terminals, a separate analysis needs to be completed that establishes SATCOM capacity – which the terminal access to meet BLOS communication needs. The lesser of the SATCOM and terminal capacities is what is actually available to users. However, as the number of programs being analyzed at this level increases, these calculations become more sophisticated – and more complicated. For example, if satellite broadcast capabilities are added to the configuration (a Level 4 calculation – see the discussion below), SATCOM capacity used for broadcast supply will be accompanied by a loss on point-to-point supply necessary to carry the broadcast. So each mode must be addressed separately and then combined in a manner that accounts for this tradeoff. Similarly, when multiple SATCOM links are being analyzed, an assumption must be made about priorities of satellite usage in order to truly understand supply. .For example, if mission critical communications always uses the protected satellite link first, how does that affect the supply to non-critical users who don’t ever use protected communications capabilities?

4. Estimation Methods for Level 4 Level 4 capability estimates go beyond accounting for factors associated with the arrangement of elements from

multiple programs in an architecture supporting a group of users. Here, analysts begin to account for the end-to-end information architecture that arises from how the systems and programs in question are leveraged by users to meet their demand. Such considerations include both the magnitude of information that is flowing through different parts of the network as well as the structure of those flows.

The particular methods employed will depend on the systems in question and the factors at work, but may generally include techniques such as the following:

• Load-based performance adjustments. System performance adjustments (to account for variations from performance specifications when systems experience non-design loads) can either be interpolated from program data, or estimated from load-performance relationships of similar programs for which such data are


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available. Information relating performance/throughput to relative load is sometimes available from the program or from subsequent studies.

• Broadcast. When broadcast capability is available, a relatively small amount of supply that would otherwise be used to provide point-to-point capability would be used to broadcast appropriate common-use content originating from external sources. Such a capability can create a “broadcast multiplier” effect in which a single instance of supply (a single satellite broadcast stream) can be used to satisfy multiple instances of demand (more than one user consumes elements of this stream as a part of his or her demand). How much of a multiplier is achieved depends on a number of factors, including the commonality of information in the broadcast, the portion of a unit’s demand that is broadcast- appropriate (generally some fraction of the demand for incoming information from outside the unit) and the number of different users with overlapping demands. If excess broadcast capacity remains once broadcast demand has been satisfied, it is usually assumed that this broadcast supply is available to provision non-broadcast demand, but this depends on the nature of the system and of the demand, as there is often significant delay associated with fulfilling unique information requests via broadcast systems. Any broadcast-eligible demand not satisfied by the available broadcast supply added back into the demand that must be satisfied via non-broadcast means.

• Relay considerations. As information proceeds on its end-to-end path, it may generate demand on multiple program networks. Where these relay effects are deemed to be important, it is possible to introduce relay factors that serve to reduce the effective supply or increase the effective demand to account for the roles that programs may play in providing connectivity (that is, extending the reach of the network) versus capacity. To first order, relay factors used to date in QCDI studies have corresponded to expected number of program networks user traffic traverses as it proceeds within the mesh network either to destination or core. This varies by type of demand (LOS, BLOS and Indirect) and is also a function of the portion of user traffic that originates from or is destined to users in other areas. Where needed, supply capacities can be divided by appropriate relay factors to estimate effective supply for comparison with demand.

• Competition from Non-modeled Demand. In addition to the considerations above, it is often the case that elements of the architecture that provides capability to a unit of interest are also providing capability to other units whose demand restricts the total capability available to the unit being studied. It is not usually possible or feasible to extend the scope of the analysis to include these other sources of demand. Instead, to capture first-order effects, the analysis must recognize what assets are likely to be subject to such competition, and estimate the portion of those assets capabilities that will be assumed to be available to the unit in question (e.g., 1/6 of available SATCOM Ka capa-city is assigned for use by the Navy Expeditionary Strike Group, or ESG, under study) .

Level 4 estimates are more data- and analysis- intensive than lower-level estimates, requiring data and analytic resources commensurate with constructing an increasingly complete and detailed picture of the systems and information architectures comprising supply of and demand for networking capability. However, when the nature of the issues being assessed include Level 4 factors such as loading, broadcast, and sharing of capacity with non-modeled units, these techniques can capture these effects to first order without exhaustive, packet-level modeling of the systems in question.

V. Illustrative Supply versus Demand Applications This section describes two examples of capability assessments that have been conducted to illustrate how the

QCDI framework can be applied to a range of problems that increase in complexity and require increasing levels of analytical sophistication. These examples start with a basic problem and context and discuss how a range of analytic considerations can be incorporated as appropriate in the assessment. Emphasis is placed on explaining the additional factors that were considered in dealing with the new dimensions rather than replicating all the steps necessary produce the results presented.

Figure 3. The scaling of analysis and data requirements with maturity.

Maximum Nominal Trans-ProgramDemand-Loaded

Data and Analysis

Required

Characteristics of devices or service instancesNumber of devices or service instances

Program network designNominal design-point loads

Cross-program demand use casesPerformance at interfaces

Integrated view of loadsAllocation of loads to program networks

IncreasedEffort


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Note that the numbers used in the supply calculations are not intended to represent any specific program. However, they illustrate the analytical process and the type of assumptions and calculations that are necessary to address various classes of problems. All the demand estimates were derived from the QCDI demand model for the units and user classes pertinent to the examples.

It is important to also note that, in order to create an interesting story line, notional conclusions have been drawn from the results of the calculations. However, in practice, before reaching first order conclusions, some additional steps are usually warranted; for example, not only determining sensitivity to key assumptions and parameters but also exploring the degree to which the demand of specific user classes are being met.

A. Tactical Radios for in a Major Ground Unit This example explores the support provided users in an Army unit (a Heavy Brigade Combat Team) by a

notional tactical radio program, RADIOX. It begins by assessing how well the demand of a single type of HBCT user is supported by the elements of RADIOX available to those users. The metric used in this analysis is Typical Required Data Rate.

In this example, the Direct Line-of-Sight demand of a class of users (Dismounted Ground users) is supported by two types of tactical multimode radios provided by RADIOX. Dismounted ground users in a typical HBCT are assumed to share 36 single-channel radios and 24 dual-channel radios (in this example, one of the channels on Type 2 devices is used for Voice, so each of the radio types provides the same data rate capability). These devices are configured into sub-networks, each of which has a subnet data rate capacity of 200 kbps and supports an average of 30 radios. The resulting per-radio data rate is estimated at approximately 7 kbps (per previous discussion) rather than at 600 kbps per channel as would be indicated by a Level 1 analysis.

As with the Level 1 calculation illustrated in a previous section, the aggregated data rate for a device type is arrived at by taking the product of the number of devices and device data rate for each type in the program – except for the fact that in a Level 2 calculation, the device capacity used is the estimated performance of the device when networked with other program devices, rather than the maximum potential capability of the device. Data rate supply to the unit is then calculated by summing over the device types supporting each group of users, then summing over the groups of users in the unit, as indicated in Equation 2:

∑ 2 , (2)

where Nj is the number of devices of type j supporting the unit of interest (in this case, the Dismounted Ground users in an HBCT), and DR(Lv2)j is the subnet-constrained data rate provided by device j. As the above discussion suggests, the significant differences between the capability of the program’s devices in isolation and their capability when embedded within subnets as anticipated indicates that this is a case in which at least a Level 2 treatment is essential to analysis of this program. Table 6 provides a summary of the results of the supply calculation for this program.

The data rate is summed across device types to yield a total data rate capacity supplied to dismounted ground troops in a HBCT of 0.42 Mbps. For comparison, the Direct LOS demand for dismounted ground users in an HBCT in 2016 is estimated at 0.38 Mbps in the QCDI Demand Model, indicating that this supply is anticipated to be adequate for 2016 demand of these users.

As a next step in this example, the provisioning by the full RADIOX program of on-the-move (OTM) Direct LOS demand of all OTM users in the HBCT is examined.

Table 5. Ground Example Part 1 Profile

Programs Two elements of a tactical multimode radio program, RADIOX

Devices A single channel radio for data (Type 1) and dual channel radio with one channel devoted to data (Type 2).

Type device demand Direct Line-of-Sight

Metrics Typical required data rate User classes Dismounted Ground

Unit structure Heavy Brigade Combat Team Timeframe 2016

Table 6. Estimated data rate capacity provided by illustrative tactical multimode radio program. Level 2 assessment accounts for capacity constraints introduced by subnet structures.

Device Type 1

Device Type 2

Number of Devices 36 24 Nominal Data Rate per device (kbps) 7 7

Aggregated Nominal Data Rate (kbps) 252 168


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The single- and dual-channel radios (Types 1 and 2 respectively) are configured for subnet participation as described above. For the four-channel radio, each channel configured for data can be treated as a separate device depending on the waveform being used. This example assumes that the four channel radios considered here, in support of an HBCT, are configured in one of two ways:

• Narrowband configuration (Type 3N): Two channels are confi-gured for voice networks, and the two channels configured for data are participating in low-bandwidth (200 kbps) subnets of, on average, 30 nodes each, resulting in a nominal (Level 2) data rate of 14 kbps (7 kbps per channel).

• Wideband configuration (Type 3W): As with the Narrowband configuration, two channels are configured for voice networks. However, one of the remaining channels is configured for operation on a wideband subnet, with per-channel on-the-move data rate of 16 kbps. The fourth channel is configured as a gateway or portal to another network, which adds network reach but does not add to the metric of interest here, typical data rate for on-the-move users.

For this example, the HBCT of interest is supported by 36 Type 1 devices, 377 Type 2 devices, and 75 Type 3 devices, of which 30 are in narrowband configurations and 45 are in wideband configurations. Note that the ability of Type 3 devices to be configured in different ways with different capabilities results in the need to treat this device as two distinct device types. Otherwise, the approach is similar to the Dismounted Ground case, except that the demand now includes the other OTM user classes in the HBCT. The results of this Level 2 analysis are shown in Table 8.

The QCDI-estimated demand for direct line-of-sight data rate from OTM users in the HBCT in 2016 is 5.3 Mbps. With a total OTM direct-LOS supply of only 4.03 Mbps, this program is insufficient to fully satisfy this demand. An option for addressing this shortfall (using surpluses in other types of demand) is addressed in the next example.

A final excursion in this example examines the extent to which the demand (both direct and indirect) of all users in a major ground unit can be satisfied by the devices or Joint network access points provided by RADIOX capabilities augmented by a program providing backbone transmission capabilities.

The methods for calculating supply from these two programs are basically the same as in previous examples, although there are some important considerations particularly associated with the backbone transmission program:

• The only LOS capability in the backbone program is a wideband microwave transmission system that supports only users who are at-the-halt.

• The BLOS capability of the backbone program consists of three types of devices – Those permitting the narrow band tactical radio to be extended – Those providing broadband support to users sometimes ATH and sometimes OTM; the data rates for

these devices vary depending on the state of the user. – Those providing only support to ATH users; these are transportable, but not mobile, terminals

Table 8. Estimated direct line-of-sight data rate provided to on-the-move users by an illustrative tactical multimode radio program with three different types of supply devices (2016 timeframe).

Device Type 1

Device Type 2

Device Type 3N

Device Type 3W Total

Number of Nodes in HBCT 36 377 30 45 OTM Nominal Data Rate (kbps) 7 7 14 16

Aggregated OTM Data Rate (kbps) 252 2,639 420 720 4,031


Programs An entire tactical multimode multiband radio program

Devices

A single channel radio for data (type 1 device) and dual channel radio with one channel devoted to data (type 2 device) and a four channel radio that can be programmed to use a narrow band (type 3N device) and wide band (type 3W device) wave forms for data exchange

Type device demand Direct Line-of-Sight

Metrics On-the-move (OTM) data rate User classes Dismounted Ground, Surface Mobile and Commanders



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Available sources of capacity information for the two programs differ, as well. For the tactical radios, the capacities are based on the results of prior studies of the performance of the radios when networked together to support an HBCT. On the other hand, capacities associated with the elements of the backbone program are taken from specifications provided by the program office. This is equivalent to assuming that adequate satellite capacity will be available to achieve the full capability of the terminals. Thus, this example uses a combination of estimation maturity Levels 1 and 2.

In order to compare supply with demand it is necessary to relate the program capabilities to the type of device demand (Direct LOS, Direct BLOS or Indirect demand). In determining support for Indirect demand, it is important to understand that, in an HBCT, this demand reflects that of users in fixed and/or mobile command posts who gain access to the Joint network by way of shared LAN/WLAN connected to LOS or BLOS access points.18 To facilitate comparison the following was assumed:

• The transportable wideband LOS and BLOS capabilities are used to satisfy only indirect demand of users in large command posts

• The mobile BLOS capabilities support the direct BLOS demand of commanders or the indirect BLOS demand of mobile command posts

• The OTM demand represents the demand of Dismounted Ground, Surface Mobile and Commanders user classes

The results are shown in the table below for both OTM and ATH demand;19 each is calculated and shown separately to maintain transparency of the analysis. For similar reasons, the results have also been color-coded to reflect the difference in the maturity of the input data. White represents the use of data regarding constrained performance from prior studies; dark gray represents the use of specified data; and light gray indicates both types have been mixed. A supply:demand ratio of less than 1 for Indirect demand indicates that this portion of the demand of users in headquarters would not be satisfied even under the assumptions that adequate satellite capacity is available and that the impact of traffic outside the HBCT is negligible. Some additional exploration is required to determine which elements (e.g. Satellite terminals or LOS microwave terminals) are limiting. Even then the results would be subject to the caveats mentioned at the beginning of this section and earlier in the description of the methodology.

18 In some cases, it may be necessary to parse this Indirect demand into Indirect LOS and Indirect BLOS portions. Although a methodology has been developed for this parsing, it is not used in this example and not shown here. 19 Note that, as used here, ATH demand is the same as total demand, as OTM units are assumed to preserve their demand and capability even when not moving.

Table 10. Estimated direct line-of-sight data rate provided to all users (when at the halt) and on-the-move users by an illustrative tactical multimode radio program and a program to provide backbone transmission capabilities (2016 timeframe).

Type of Demand

Total (ATH) On-The-Move Supply Demand S/D Ratio Supply Demand S/D Ratio

Direct LOS 43.2 Mbps 19 Mbps 2.3 37.6 Mbps 5.3 Mbps 7.1 Direct BLOS 32.4 Mbps 5.9 Mbps 5.5 6.4 Mbps 4.3 Mbps 1.5 LOS + BLOS 75.6 Mbps 24.9 Mbps 3.0 44.0 Mbps 9.6 Mbps 4.6

Indirect Demand 108.8 Mbps 137 Mbps 0.8 40.4 Mbps 4.2 Mbps 9.6


Programs An entire tactical multimode multiband radio program

and a backbone transmission program

Devices Multimode satellite terminals and line-of-site microwave terminals as well as the single and multi-channel radios addressed in the previous example.

Type device demand

Direct line-of-sight, direct line-of-sight and indirect demand

Metrics At-the-halt (ATH) and on-the-move (OTM) data rate

User classes Dismounted Ground, Surface Mobile, Commanders and Command Post



13

B. Alternative Satellite Communication Capabilities for a Maritime Force This example assesses the extent to which alternative satellite system-of-system communication capabilities,

including a mix of different satellites and terminals, meet the indirect demand of key users in mix of maritime strike groups.

A base case and three alternatives that become increasingly more complex but promise to progressively increase supply were assessed. The basic method of assessing supply in these cases was to examine the capability provided by the satellites and by the satellite terminals on board the ships, recognizing that constraints on the total capability provided could come from either a lack of adequate satellite throughput, or a lack of adequate terminal capacity at the receiving end. In addition, the demands being made by the other Services global assets such as satellite constellations were accounted for in assumptions about the portion of satellite capacity that is available to the maritime force in question. As a result, this example represents a Level 3 analysis, with some elements of Level 4 as demand considerations such as broadcast are taken into account in the alternatives considered.

In the base case, ships only have DoD satellite terminal providing point-to-point links to a single military satellite with unprotected communications capability and a single military satellite with protected communications (e.g., high robustness and encryption) capability. Base case results are shown in the Table 12 to the right. Notice that one military satellite with protected capability satisfies a little more than a third of the protected data rate demand. Also, the military satellite with unprotected capability satisfies about one fifth of the unprotected demand. The following three alternatives explore potential means of reducing or eliminating the gap associated with the later.

Alternative 1: Commercial capacity is leased for every ship in the force to increase the unprotected data rate The results are shown in Table 13 to the right. In this alternative, the Military unprotected supply and the

satellite capability provided by commercial leases (50 Mbps of commercial SATCOM bandwidth, with corresponding terminals in the JTF whose capability sum to 540 Mbps) were combined to determine the unprotected total. Notice that the addition of commercial SATCOM and terminals increases supply marginally, but that due to constraints that persist in the capacity of military and commercial satellites (resulting from the extremely high cost of leasing commercial satellite bandwidth), this alternative does not close the demand-supply gap identified in the Base Case.

Table 11. Maritime Example Profile

Programs Point-to-point military satellite capabilities with and without jamming resistance, commercial point-to-point satellite capabilities and satellite broadcast capability

Devices Mix of configurable multimode satellite terminals and antenna groups that vary with the type of ship supported

Type device demand Indirect demand

Metrics Typical data rate and protected communications data rate User classes Command Post

Unit structure JTF comprised of 4 Carrier Strike Groups and 5 Expeditionary Strike Groups, each of which includes both large and small ships

Timeframe 2016

Table 12. Capability Provided to Maritime JTF by DoD Point-to-Point Satellite Assets.

Military Unprotected

Military Protected

Terminal Supply (Mbps) 267 250 Satellite Supply (Mbps) 214 250

Constrained Supply (Mbps) 214 250 Demand (Mbps) 1000 750

Percent Demand Satisfied 21% 33%

Table 13. Capability Provided to Maritime JTF by DoD and Commercial Point-to-Point Satellite Assets and Commercial Terminals and Leased SATCOM.

Military Unprotected

Commercial Leases

Total Unprotected

Military Protected

Terminal Supply (Mbps) 267 540 807 250 Satellite Supply (Mbps) 214 50 264 250

Constrained Supply (Mbps) 214 50 264 250 Demand (Mbps) 1000 750

Percent Demand Satisfied 26% 33%


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Alternative 2: The capability to broadcast data to multiple ships simultaneously is added to the military satellite with unprotected capability.

In Alternative 2, the military satellite providing unprotected communications is assumed to be able to transmit in both point-to-point and broadcast modes, and terminals have been added to ships within each of the strike groups to receive broadcast signals. Recall from an earlier section the potential for achieving supply multiplier effects when employing broadcast; here, the broadcast multiplier was assumed to be 9 (the common broadcast content was of interested to each of the 9 strike groups in the JTF), and the magnitude of the common broadcast demand was computed from the external download demand of each of the strike group units. Satellite capability not used for broadcast is available to satisfy point-to-point demand.

To conduct this analysis, supply and demand are, in effect, parsed into broadcast and point-to-point components, each of which is computed and compared separately in Table 14. Notice that Total Unprotected Supply has dropped from 264 Mbps to 250 Mbps. This reduction of available supply of unprotected satellite communications bandwidth reflects the use of some portion of the Military Unprotected Satellite Supply for broadcast to the mission participants. Note also that this reduction (13.9 Mbps) corresponds to a larger quantity of broadcast demand satisfied (125 Mbps), a manifestation of the broadcast multiplier effect discussed above.20 Because of this effect and the advantage it conveys, the assumption is made that broadcast traffic will have satellite priority, and thus broadcast demand satisfaction will be ensured before allocating satellite capacity to the point-to-point demand for unprotected communications that remains.

Alternative 3: An additional military satellite is added in the hope of further increasing unprotected supply For Alternative 3, the impact of launching an additional satellite to mitigate the shortfall in unprotected

communications supply was assessed (it is assumed that all of the strike groups in the JTF are within the footprints all each of the satellites considered in this analysis). As before, a broadcast capability is included with the same parameters and impact as above. The results of the analysis of this alternative are shown below. The 414 Mbps now available as the Satellite Supply of Military Unprotected Communications derives from an assumption of two satellites each providing 214 Mbps of capacity, with a reduction of 14 Mbps in effective capacity due to the use of some bandwidth for broadcast.

Notice that for this alternative, the addition of the second satellite for unprotected communications shifts the constraint from the space segment to the terminals onboard the JTF ships. Although being able to utilize the full capacity of the ship-board terminals does provide a marginal improvement in demand satisfied, the bulk of the capabilities of the additional satellite are not realized due to bandwidth constraints of the terminals. Adding additional terminal capacity on board the ships is necessary to alleviate this bottleneck – but that solution is challenging due to the lack of deck space for additional antennae and other associated equipment.

20 This is indicated by an asterisk in Table 14 as a reminder that the “effective” supply from broadcast is computed from demand considerations.

Table 14. Capability Provided to Maritime JTF by DoD Point-to-Point Assets, Commercial Terminals and Leased SATCOM, and Broadcast Capability

Total Unprotected

Point-to-Point

Total Unprotected Broadcast

Total Unprotected

Military Protected

Terminal Supply (Mbps) 807 450 1257 250 Satellite Supply (Mbps) 250 14 375 250

Constrained Supply (Mbps) 250 125* 375 250 Demand (Mbps) 875 125 1000 750

Percent Demand Satisfied 29% 100% 38% 33%

Table 15. Capability Provided to Maritime JTF with Additional Military Satellite Providing Unprotected Communications Capacity

Military

Unprotected Commercial

Leases

Total Unprotected

Point-to-Point Total

Unprotected Military

Protected Terminal Supply (Mbps) 267 540 807 1257 250 Satellite Supply (Mbps) 414 50 464 478 250

Constrained Supply (Mbps) 267 50 317 442 250 Demand (Mbps) 875 1000 750

Percent Demand Satisfied 36% 44% 33%


15

Importantly, though this result seems obvious when presented, it is only by conducting a multi-program analysis that considers both satellite and terminal programs that this issue is highlighted. It is for precisely this reason that the kinds of tools and techniques discussed in this paper have been developed – to provide the community with a high-level means for gaining relatively rapid insight into the key factors and systems contributing to the ability to meet the demand of real units for net-centric capabilities.

VI. Closing Thoughts The methodological framework presented here for utilizing the QCDI demand model to assess the adequacy of

supply afforded by a portfolio of programs is intended to evolve and mature along with the QCDI demand model. It has been applied initially to a range of problems of varying complexity and proven to be extremely flexible in its ability to be adapted to the problem at hand and the analytical resources available. The ultimate goal is to define in sufficient detail a repeatable methodology that can be implemented by a family of tools, some developed within this effort and some available within the analytic community. These tools would be employed in concert with the overall methodological framework, not only to identify the gaps in capability, but also to determine the associated risk of mission failure. These initial applications suggest wide applicability, but more research and engineering analysis are needed to achieve the highest level in the analytical maturity model and, when needed, reflect the full impact of the underlying information architecture. To this end, an explicit methodology is being developed to parse QCDI demand estimates into requirements for the exchange of information among nodes that characterize a force conducting a mission. In addition simple network design tools are being used to rapidly reflect these requirements as loads on a supporting system architecture comprised of multiple programs. This work, along with a methodology for assessing the mission impact of gaps in capability, will be presented in subsequent papers.

Acknowledgments The authors wish to acknowledge the significant contributions of a range of individuals and groups who

supported this work, both organizationally and technically. The authors are grateful to the following individuals for their contributions to the technical content of this work; from the Johns Hopkins University Applied Physics Laboratory: Paul Kim, Steve Sudkamp and Keith Wichmann; from Evidence Based Research, Inc.: Christine Anderson, Ayanah George, Heather Noell and Karl Selke; from RAND: Isaac Porche and Bradley Wilson; and from Telcordia: Darrell Woelk. The authors appreciate a review that Carolyn Turbyfill of the Johns Hopkins University Applied Physics Laboratory performed on an early version of this paper.

This work could not have been conducted without the support and sponsorship of Dr. Ronald Jost, the Deputy Assistant Secretary of Defense for C3 and Space Systems and others within the Office of the Assistant Secretary of Defense for Networks and Information Integration. This work was also supported and shaped by Ms. Mary Shupack and Dr. Tibor Schonfeld of the Johns Hopkins University Applied Physics Laboratory.

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