Monday, December 2, 2013

Most Additional Mobile Spectrum Has to Come from Existing Licensees

Though better air interfaces, offloading, small cells and possibly retail pricing policies will help mobile service providers cope with growing mobile data demand, there actually is very little unused spectrum to allocate for additional mobile use.

That means reallocating existing spectrum is what has to happen, according to the Phoenix Center for Advanced Legal and Economic Policy Studies.

The U.S. federal government itself controls about half (1,687 MHz) of the spectrum
between 225 MHz and 3.7 GHz most useful for mobile or fixed communications.

And one problem is that government agencies do not have incentives to use spectrum efficiently.
“The PCAST Report, for example, states plainly, ‘Federal users currently have no incentives to improve the efficiency with which they use their own spectrum allocation.’”

The European Commission’s WIK-Consult Report notes that “public sector agencies may
not face sufficient incentives to make maximally economically efficient use of
their spectrum assignments (e.g. through sharing with other compatible uses), or
to give spectrum back to the spectrum management authority if they no longer need it.”

As we see it, it is the inefficiency of spectrum management, not spectrum use, which is most problematic, the Phoenix Center says. The fact that the Pentagon pays $750 for a hammer does not mean a consumer can’t purchase one for $10 at the local hardware store.

In contrast, if the government is an inefficient manager of spectrum, then the consequences of the inefficiency are realized across the entire spectrum ecosystem.

The issue then is one of creating a licensing regime that maximizes efficiency. The analysis suggests it is preferable for the Government to sell spectrum rather than lease it.

Where spectrum is shared, it likewise would be better for the management process to be conducted by the private market rather than a government entity.

No comments:

Generative AI Will NOT have the Impact Many Expect

Generative artificial intelligence, to say nothing of machine learning or neural networks (and eventually general AI), might collectively re...