The authors have declared that no competing interests exist.

High-Throughput Satellites (HTS) are a distinctive class of communication satellites that provide significantly more throughput per allocated bandwidth than traditional wide-beam communication satellites. They are the proverbial wave of creative disruption in the space industry and are poised to disrupt the communication market in significant ways. The objective of this work is to develop a decision-analytic framework for assessing the value of High-Throughput Satellites and to provide meaningful results of the value of such systems under realistic design, operational, and market conditions. We develop the cost and revenue models of HTS. To build the revenue model, we develop a hybrid data-driven and scenario-based load factor model that combines historical data based on financial records from current HTS operators with extrapolations based on best-, nominal-, and worst-case scenarios. We then integrate the cost and revenue models within a stochastic simulation environment and perform Monte-Carlo analysis of the

On October 4, 1957, a small beeping satellite, Sputnik, heralded the beginning of the Space Age. From this humble start, the space industry grew into a multi-hundred-billion-dollar industry five decades later [

In April 2019, two announcements were met with a collective shrug by the general press, and tepid enthusiasm from the aerospace press, that Amazon is planning a High-Throughput Satellite (HTS) constellation for broadband access with over 3,000 satellites in Low Earth Orbit (project Kuiper), and that SpaceX obtained FCC approval to lower the orbit of nearly 1,600 satellites in its planned HTS constellation (project Starlink).

These announcements, we propose, are the tip of the iceberg of another Sputnik-like milestone in the Space Age. While much attention in the media and public discourse on space issues has focused on launch vehicles and their reusability, the more important but quieter revolution is unfolding in satellite communication with HTS, and it will usher a new era of broadband connectivity in the next decade. High-Throughput Satellites, we noted in a companion article [

What are High-Throughput Satellites? And why this assessment? We examined these issues in a companion article entitled, “Review of High-Throughput Satellites: market disruptions, affordability-throughput map, and the cost per bit/second decision tree,” [

In addition to this massive increase in bandwidth, the affordability of the bits per second or cost of throughput with HTS is significantly lower than that with conventional wide-beam satellites—by one or two orders of magnitude—and it is reaching terrestrial-like economics [

Two milestones in the timeline of advent of HTS are worth noting: the first HTS launch with Thaicom 4 in 2005 with 45 Gbps, and the launch of ViaSat 2 in 2017 with over 300 Gbps. Over this time period 2005–2017, the yearly launch rate of HTS has increased tenfold. A single HTS can now provide as much throughput as the entire fleet of traditional (wide-beam) communication satellites in GEO. The advent of HTS and the onset of obsolescence of traditional wide-beam communication satellites are driven jointly by cost considerations and by the changing nature of communication markets, in particular how people and businesses access and consume data [

We examined in the companion article both the technical details of HTS and the disruptions they are likely to bring to the space industry, in particular how manufacturers and operators will be impacted by these systems. The reader interested in this discussion is referred to [

How valuable are these satellites, and as a co-requisite, how can their value be assessed?

Under what conditions, given technical and operational (e.g., design and service pricing), as well as market uncertainty, will an HTS system be financially successful? What are the pathways to success or failure?

These are some of the issues we examine in this article. More broadly, the objective of this work is both to develop a decision-analytic framework for assessing the value of High-Throughput Satellites and to provide meaningful results of the value of such systems under realistic design, operational, and market conditions. Ultimately, these analytics and results are meant to help inform design, cost, and service pricing considerations, among other things, for these systems to be successful.

The theoretical underpinning of this work is the notion that an engineering system is a value-delivery artefact; its value derives from the flow of service it provides to different stakeholders. This is a central idea for the design and acquisition of space systems, and it is the conceptual pillar upon which this work rests. Given this underpinning, and since there are markets and quasi-rent for the services provided by the communication satellites here examined, discounted cash flow techniques are the standard method for capturing the value of such systems. This is the reason for the adoption of

The remainder of this article is organized as follows. In section 2, we provide a brief review of the previously developed value model of traditional wide-beam communication satellites. This will serve as a basis for the remainder of this work and against which the HTS value model will be benchmarked and contrasted. In section 3, we develop the cost, revenue, and value models for HTS. In section 4, we conduct Monte-Carlo value analyses under different market and operating scenarios, and we provide a comparative analysis of HTS with wide-beam satellites. We discuss our findings and conclude our work in section 5.

In this section, we provide a brief review of the cost, revenue, and

There are different levels of resolution for examining the cost structure of a satellite and aggregating its life cycle cost (LCC). One commonly used distinction in the space industry is the breakdown of LCC into cost to initial operational capability (IOC), which includes all costs incurred up to the delivery of the satellite on-orbit, and cost of operations incurred afterward. The model for the cost to IOC is given by Eq (

For this brief review, it is sufficient to model the acquisition cost of a communication (wide-beam) satellite as a function of the size of its payload, more specifically, as a function of the number of 36-MHz equivalent transponders carried onboard,

The model in Eq (

The second category of costs refers to the ongoing cost of operation, _{ops}, of the satellite over the duration of its life on-orbit, _{service}. The present value of the satellite’s cost of operation, _{ops}

When aggregated, the cost to IOC and present value of the cost of operation determine the present value of the life cycle cost,

We develop the revenue model next, following which we integrate both the discounted cost and revenue models to produce a

The revenue model of the satellite is more involved than its cost model, with several additional parameters or degrees of freedom to capture different services and revenue streams. Some parameters are common between the cost model and the revenue model, such as the time delay between when the acquisition cost is incurred and when the costs of operation begin,

_{0}, e.g., 3_{95%}.

The load factor can be broken down into various services for which the transponders are leased, such as audio, data, and video, which can have different lease prices in different markets and for different lease durations. The average revenue generated by service

Two additional degrees of freedom are included in the model that modify the expression of the load factor for more realistic market conditions, and consequently, they modify the revenue model as well. They also add to the stochastic nature of the loading model by introducing two new random variables: the first is the time to onset of obsolescence, _{obs}. This refers to the onset of customer churn as newer and better transponders are available on other satellites (covering the same geographic region) and customers begin to switch to these newer technologies. The second is the intensity of obsolescence, _{obs}, which captures the intensity (rate) of customer churn. In the current model, a simple linear decay determines the percentage points lost per year in the satellite load factor after the time of onset of obsolescence has elapsed. There are more involved models of obsolescence available, as discussed in [_{obs}. The previous expression for the satellite load factor is therefore extended to _{obs} and is given by Eq (

The present value of the revenue,

The

We provide next realistic calculations of the

The outputs of the Monte-Carlo simulations include probability distributions of the

In the previous section, we reviewed the development and results of the value model for traditional wide-beam communication satellites. In this section, we develop the cost, revenue, and value models of High-Throughput Satellites and discuss distinguishing features between the HTS models and those of traditional wide-beam communication satellites. The results and comparative analyses are provided in the next section.

As discussed in [_{total}, of HTS based on historical data [

The results in

There is a remarkable power relationship between HTS affordability and throughput, reflecting clear and substantial economies of scale in the cost of connectivity (cost per Gbps) to be reaped in designing higher throughput satellites;

A significant amount of the variability in affordability for GEO HTS is explained by throughput alone (^{2} = 0.93);

The “knee” in the throughput-affordability curve occurs around 100 Gbps. As a result, it will become increasingly more difficult to justify the acquisition of small- or medium-sized GEO HTS below this throughput threshold of 100 Gbps.

The regression model depicted in

Along with the cost to IOC, an additional component of the cost model of HTS is the cost of operation over a given unit time period (typically a year), _{ops,HTS}. Compared with that of wide-beams, this cost model is more involved by necessity, as will be discussed next. The cost of operation model is given in Eq (

Consider the model for an increase in the number of subscribers (Δ

The revenue model of HTS is developed next, following which both (discounted) cost and revenue models are integrated to produce the

We break the development of the HTS revenue model into three parts for clarity: the load factor model, the subscribers-versus-throughput model, and finally, the service revenue model.

As with wide-beams, a necessary component of the revenue model is the load factor. To construct the HTS load factor model, _{HTS}(

The result shows a monotonic and slow (sub-linear) increase in the number of subscribers over the observational period. While this model is valuable, it is limited to the duration for which the data was available (and extractable). To extend this model over a typical 15-year design lifetime, we combine this empirical, data-driven loading model with forecasted scenarios described as best-case, nominal-case, and worst-case scenarios, as depicted in ^{th} to 5^{th} year in service).

The first scenario is the best-case HTS loading model, _{B}(

The second scenario is the nominal-case HTS loading model, _{N}(

Finally, the third scenario is the worst-case HTS loading model, _{W}(^{th} year of service, as with the nominal scenario, but the load factor starts a linear decrease from ^{th} year of service (an average equivalent of 38% loading over this 10-year period). The piecewise worst-case loading model scenario is given in Eq (

The notation _{HTS}(

Unlike the revenue model of traditional wide-beam communication satellites, with HTS, the number of subscribers is modeled as a function of the total throughput of the satellite, a system-level design parameter. The maximum number of subscribers, _{max}, is given by Eq (

The product (_{1}∙_{2}) denotes the actual average downlink speed. Multiplying this product with the average user activity per user,

By combining the number of subscribers and the hybrid load factor models of HTS, we determine the average revenue generated per service for HTS per unit time period (quarter or year),

The total revenue generated per unit time period for HTS,

Note that the revenue does not decrease when the change in the number of subscribers,

In parallel with Eq (_{HTS}, as given by Eq (

In the next section, we use this model to assess the value of HTS and determine primary drivers of the expected

In this section, we examine the

The benchmark satellite was modeled as a medium-sized HTS with a design life of 15 years. We use a throughput capacity of 100 Gbps to highlight the throughput capacity threshold identified in

54 different combinations were generated and value-analyzed. Results for the three highlighted scenario combinations are here presented.

We selected, for this subsection, discrete sets of choices for each of these four parameters to facilitate the analysis and interpretation. These are realistic values, e.g., 100, 250, 500 Gbps satellite capacity (medium, large, and very large HTS [

The results for these three scenarios are provided in

The salient results in

Under all loading scenarios, this medium-sized HTS is

The load factor, as expected, is a significant driver of value, with the expected

The

The joint distribution histogram of

In this subsection, we relax the assumption of specific discrete scenarios adopted previously and examine the problem with a continuous range of

We chose this formulation because it helps identify and highlight several important tradeoffs for satellite operators to successfully manage their HTS. The results for a 10-year break-even horizon are provided in

A second reading grid for the results is the effect of increasing the downlink speed. One can read this result along a vertical or horizontal slice of

One last result, casually indicated in

The objective of this work was both to develop a decision-analytic framework for assessing the value of high-throughput satellites and to provide meaningful results of the value of such systems under realistic design, operational, and market conditions. These are meant to help inform design, cost, and service pricing considerations, among other things, for these systems to be successful.

In the process of developing these analytics, we derived an intermediate result, namely the power relationship between HTS affordability (cost per Gbps) and throughput, which reflects clear and substantial economies of scale in the cost of connectivity of these systems, with the “knee” in the curve occurring around 100 Gbps. The implication of this finding is that it will become increasingly more difficult to justify the design and acquisition of small GEO HTS below this throughput threshold.

More importantly, we found that a medium-sized HTS significantly outperforms a roughly equivalent traditional wide-beam satellite in terms of

Another important result here identified and quantified is the tradeoff between

Two important limitations and underlying assumptions of this work should be acknowledged. The first was noted in the previous section, and it concerns the elasticity of demand and satellite loading to changes in

The second limitation is more surreptitious, and it concerns the way the value of the satellite was assessed. The underlying assumption in our calculations is that of the traditional broadband business model in which the

Finally, we note that the analyses and results in this article were confined to a single GEO HTS. How the decision-analytic framework here developed can be adapted to LEO HTS constellations will be explored in a follow-up work.

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