Lanai to See Test of Solar-Powered Drone for 5G Service

A solar-powered unmanned aerial vehicle developed by SoftBank and built by U.S. drone manufacturer AeroVironment will test 5G delivery over the island of Lanai, Hawaii. The Hawk 30 High-Altitude Pseudo-Satellite drone is in the same family of aircraft developed by NASA. 

The University of Hawaii Research Organization has a support agreement with program sponsor HAPSMobile Inc. to perform the test project on Lanai. SoftBank Corp., Loon LLC and AeroVironment also are involved in the project.

source: NASA

Service Providers Believe Enterprise Will Generate Most New 5G Revenue

With the caveat that service provider executives can be collectively wrong at times, there is fairly broad concurrence that new revenue sources in the 5G era will come from enterprise use cases, according to GSMA. 

source: Cisco

How Do You Build a Communications Network at Population Density of 6 People Per Square Mile?

As rural areas have the lowest population density, and since communications network costs are density related, American Indian reservations are tough places to finance communication networks. By some estimates, the most-difficult places to connect are homes in the last two percent of locations, representing the most-isolated areas. 

The average population density for the U.S. is approximately 345 persons per square mile. The Navajo Nation has a population density of 6.33 persons per square mile. That means the business model is not only astoundingly difficult, it likely can be characterized as non-existent. In other words, service can only be provided if subsidies are provided, since there actually is no positive business case. 

Assume 55 locations per square mile, and two fixed network suppliers in each area. That means a theoretical maximum of 27 customers per square mile, if buying is at 100 percent. Assume for the moment that buying rates really are at 100 percent. Two equally skilled competitors might expect to split the market, so each provider, theoretically, gets 27 accounts per square mile.

At average revenue of perhaps $75 a month, that means total revenue of about $2025 a month, per square mile, or $24,300 per year, for all the customers in a square mile.

The network reaching all homes in that square mile might cost an average of $23,500 per home, or about $1.3 million.

At 50 percent adoption, that works out to roughly $47,000 per account in a square mile, against revenue of $900 per account, per year. Over 10 years, revenue per account amounts to $9,000.

The business case does not exist, without subsidies. The business case is, of course, far worse at densities of 6.33 persons per square mile. As one engineering analysis noted, “areas with a density density of less than 10 households per square mile (survey areas C, D, and F) are unlikely to see investment from the private sector.”

In rural areas everywhere, there are two fundamental reasons mobile networks have a better business case than fixed networks: customers want service and network costs are lower than for fixed networks.

CEO of Genexis on FTTH in United Kingdom

Gerlas van den Hoven, CEO of Genexis on full fiber connectivity in the United Kingdom. As an industry mentor once told me, a few decades ago, "fiber to the home is the future, and always will be." He was joking, but only in a sense. Since then, in the U.S. market, hybrid fiber coax has become the market leader for fixed network internet access, with service almost ubiquitously at 1 Gbps, and a roadmap to 10 Gbps. Other options keep popping up, and some might eventually become commercial forces.

In some markets, FTTH provided by a single wholesale provider might be the future. The issue is, when does it stop being the future, in the sense of finally being deployed as commonly as copper was. And, if the access market remains competitive, what is the financial return?

"Fiber to where you can make money" is my own formulation, when markets are seriously competitive.

The Earth is Getting Greener, Because of Higher CO2

Science is pretty amazing. Without minimizing carbon dioxide levels in the atmosphere, which can cause warming, carbon dioxide also is food for plants. That might explain why plant coverage now is increasing. 

Enterprise AI is in Early Stages, Expect Disappointment

A recent International Data Corporation survey of global organizations that are already using artificial intelligence solutions found only 25 percent have developed an enterprise-wide AI strategy. 

It is likely very few enterprises had comprehensive personal computing or local area networking strategies in place in the early days, either. In fact, one might be safe in arguing that productivity paradox will continue to hold, and that quantifiable benefits from a shift to AI-enabled processes will lage investment by quite some degree.

The primary drivers behind these organizations' AI initiatives were to improve productivity, business agility, and customer satisfaction via automation, IDC researchers say. 

Faster time to market with new products and services was another leading reason for implementing AI. 

As always, though, there is a lag between capital investment in new technologies and tangible business results, if only since entire business processes must be redesigned before the full advantage can be gained. 

So much disappointment with the outcomes of AI are to be expected. Quite often, big new information technology projects or technologies fail to produce the expected gains. 

That “productivity paradox,” where high spending does not lead in any measurable way to productivity gains, is likely to happen with artificial intelligence and machine learning, at least in the early going. And that “early going” period can last far longer than many believe.

To note just one example, much of the current economic impact of “better computing and communications” is what many would have expected at the turn of the century, before the “dot com” meltdown. Amazon, cloud computing in general, Uber, Airbnb and the shift of internet activity to mobile use cases in general provide examples.

But that lag was more than 15 years in coming. Nor is that unusual. Many would note that similar lags in impact happened with enterprises invested in information technology in the 1980s and 1990s.

So prepare now: artificial intelligence and machine learning are eventually going to have the impact many now expect. It simply will take far longer than many expect.

No doubt, spending is growing. Some surveys suggest enterprises have dived into machine learning (artificial intelligence).

Half of those adopting machine learning are looking for insights they can use to improve their core businesses. About 46 percent report they are looking for ways to gain greater competitive advantage. Some 45 percent are looking for faster gleanings of insight. And 44 percent are looking at use of machine learning to help them develop new products.

But clear and quantifiable benefits will lag the investments. Thus it always is.

Net Neutrality Rules, or Absence of Rules, are Just Noise

Some argue U.S. customers are worse off since the end of network neutrality regulations, referring to transparency of charges and fees. Others would offer argue that net neutrality rules are sort of “noise,” not signal. 

With or without the rules, speeds are climbing--and fast--while prices are in line with global norms or have decreased. 

source: Speedtest

Though there are many other reasons for investments in networks that lead to speed improvements, network neutrality rules were in place from 2015 to 2017. 

source: Trading Economics

The larger point is that average U.S. fixed network speeds have risen dramatically since 2010. 

source: Statista

Some focus on price as the problem, but absolute prices do not appear to have changed much, even as speeds and usage allowances have grown rapidly. Stand-alone pricing for residential internet access costs between $50 and $60 a month, less if purchased as part of a bundle, which most consumers do.  

Broadband prices also are not high in U.S. markets, as a percentage of household or national income. Prices over time arguably have dropped, as between 1990 and 2000, for example. Adjusting for purchasing power parity, U.S. prices are in line with global prices.