Andrew Seybold, Inc., 315 Meigs Road, A-267, Santa Barbara, CA 93109
805-898-2460 voice, 805-898-2466 fax, www.andrewseybold.com
New Approaches To Optimizing
Radio Spectrum
Prepared by:
Andrew M. Seybold
CEO and Principal Consultant
www.andrewseybold.com
New Approaches To Optimizing Radio Spectrum
Introduction
Recent reports continue to show that demand for wireless services is increasing at over 50% per year.
Most of this demand is for broadband data services; however, there is also an increase in voice demand
as more people unplug their wired phones and rely strictly on wireless devices for all of their calling
needs. A study by Cisco Systems, as well as several undertaken by the Federal Communications
Commission (FCC), indicate that the demand for wireless services will not slow in the coming years but
rather will continue to grow—in fact, doubling year over year as shown in Figure 1.
Figure 1: from Mobile Broadband: The Benefits of Additional Spectrum (FCC Report 10/2010)
In another study, Credit Suisse found that today’s commercial networks are operating at 80% of their
total capacity, and that 26% of existing cell sites are already operating at full capacity. The bottom line is
that we are nearing saturation of our limited licensed spectrum resources.
The FCC has pledged to “find” 500 MHz of additional spectrum within the next 10 years, 300 MHz of
which is supposed to be identified and made available within the next 5 years. However, once that
spectrum has been indentified, it must be reallocated (with existing users relocated to some other
portion of the spectrum) and then auctioned. All of this will take at least 3-5 years to accomplish.
Because this doesn’t address the bandwidth needs we have today, it is clear that we also need to find
ways to better utilize the spectrum we have available now, with the goal of making current wireless
systems more spectrally efficient.
As this white paper will show, there are a number of ways this can be accomplished, but the long-term
answer is that a combination of all of the methods available to us will have be to deployed in concert
with each other to meet this exploding demand.
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Radio Spectrum – A Primer
Radio spectrum is a finite resource. We cannot “make” any more of it. If we are to “find” more of it, we
are essentially limited to reallocating swaths held by existing users that will have to either lose some of
their spectrum or be moved to other portions of the band.
The best spectrum for two-way radio or cellular types of communications is in lower frequencies. These
frequencies are scarce due to technical, historical, and regulatory reasons. A large part of the spectrum
(30 MHz to 900 MHz) that is well suited for cellular and land mobile radio (LMR) is occupied by existing
business, industrial, public safety, and other license holders. Additionally, only a fraction of this
spectrum is practical for commercial consumption as the usage of lower frequencies requires antennas,
filters and other components that do not fit into a portable handheld form factor. There are also many
services that have long used valuable spectrum in frequencies that should be reassigned for mobile data
and voice since those services could equally well use some other spectrum.
Fast forward to the present day and we see that widespread high-speed wireless broadband has led to
customer uptake that was far greater than the network operators, device manufacturers, and
application developers had predicted. The introduction of the iPhone by Apple was really the starting
point for soaring broadband wireless service demand, and since that time, new applications including
streaming video for TV and movie services have proliferated. Network operators are struggling with how
to keep up with this demand. AT&T recently reported that 4% of its iPhone customers were accounting
for more than 50% of the data traffic on its 3G network, and a Cisco report indicates that today almost
50% of the data traffic on these networks is streaming video. This trend is expected to accelerate as
network operators begin deploying 4G (fourth-generation cellular services) (see Note 1). However, 4G
does not address our current spectrum issues, and in fact may make it worse as new bandwidth and
spectrum-intensive services are brought to market.
A recent report from the FCC’s Office of Broadband Development cites a technical paper showing the
demand for broadband services outstripping spectrum capacity as early as the end of 2012 (Figure 2).
Figure 2: from the Office of Broadband Development (FCC)
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Addressing Demand – Network Operator Strategies
Given that access to more spectrum is still a number of years away, network operators have only a few
options available to cope with rising demand for broadband services. One is to implement new pricing
plans that will help manage this demand going forward. When 3G broadband services were first
introduced in the United States, the pricing plans included unlimited data usage. With demand soaring
as it has, network operators are now changing their strategy. In recent months many have implemented
tiered data pricing plans whereby users pay more as their data use goes up. In the future we will see
other types of data pricing that will also be intended to manage data usage. One of these is time of day
data pricing. That is, if you want to download a large file at 2 p.m. in the afternoon there will be an extra
charge you will have to pay, but if you defer the download to off-peak hours, say 2 a.m., then the
download will be included in your normal data pricing model. This will impact how, when, and where
broadband services can be economically used for most consumers.
Other methods being employed by network operators to meet bandwidth requirements have included
off-loading some of the demand to unlicensed Wi-Fi hotspots and selling in-building femtocells that
make use of a customer’s own wired Internet connection. Another approach is to build more cell sites
closer together, but this is expensive (as well as time-consuming), due to local permitting and other
considerations. Again, many of these approaches have been driven by the need to squeeze more
capacity out of limited spectrum.
Demand for more spectrum and capacity has also been a key factor in industry consolidation. The
rationale given for the AT&T/Cingular merger was based on the fact that AT&T had more spectrum than
Cingular, and by combining the companies they could more efficiently serve their customers. Likewise,
AT&T’S ongoing attempt to acquire T-Mobile is primarily driven by AT&T’s desire to secure additional
spectrum and cell sites in order to provide more capacity across its network.
The commercial cellular networks do not represent the only services that are running out of capacity
due to spectrum limitations. This is also true for first responders, who must often wait up to three years
to obtain additional channels adjacent to their existing frequencies. Other wireless users around the
globe such as trucking companies, industrial and enterprise users, and those who use unlicensed
spectrum (such as Wi-Fi and white spaces) are also feeling pinched by regulators who are under
pressure to dedicate more and more spectrum to commercial mobile carriers as the demand for both
voice and broadband access continues to explode.
Identifying Solutions to the Spectrum Problem
In order to maximize the efficient use of current spectrum assets, we will need to implement multiple
approaches. For its part, the FCC has taken steps to help mitigate the anticipated spectrum shortfall in
two ways. As noted above, it has pledged to find more broadband spectrum for both commercial and
unlicensed use. It has also mandated that all land mobile radio systems that are using spectrum below
512 MHz must further narrowband their channels, effectively doubling the number of land mobile radio
channels in existence. In the UK, communications regulator Ofcom is preparing for its biggest ever
spectrum auction for new 4G services. Bloomberg reported that the spectrum sale might raise as much
£2.6 billion (US$4.1 billion). Ofcom is also preparing to let loose unlicensed TV white space spectrum.
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However, these offerings have been regularly challenged by incumbent mobile operators and already
have experienced multi-year delays.
While the above efforts will help, they will not solve our spectrum problems in the long-term. In fact, the
entire issue of spectrum management and spectrum usage needs to be thought about and dealt with
differently than before, i.e., adding incremental amounts of additional spectrum. There are many
positive things going on within the wireless community that will help us find better ways to manage
spectrum. This includes intelligently sharing spectrum in better ways than we do today. In short, it is
time to rethink the way spectrum is allocated and used and to work on a model for the future—a model
that will need to be radically different than today’s.
What is needed is a combination approach, a total re-thinking of spectrum. It is in everyone’s best
interest to rethink the flexible use of our spectrum and develop ways to use it more efficiently.
Fortunately, there are some very smart companies with some very bright engineers who are working on
these issues and in some cases are demonstrating their solutions for both commercial and government
agency sectors.
Better Spectrum Utilization via Game Changing Technology
In addition to the FCC’s efforts, we need to adopt fresh thinking about our overall spectrum
management policies. We must continue to encourage advances in technology that will provide
additional capacity on the airwaves in use today. In truth, some of these licensed frequencies are lightly
used and could be shared, if the sharing could be accomplished in an intelligent manner so as not to
preclude access when it is needed by the primary license holder. There are a number of companies
working on ways to make radio systems more intelligent and one of the solutions to ease the strain of
spectrum demand is to employ more intelligent (i.e., cognitive) radios and back-end infrastructure.
One of the most exciting new ways of providing better spectrum management and utilization is through
an approach called cognitive radio networking. While the general idea of cognitive radio networks is
relatively straightforward, the key technologies and industry terminology used can be confusing. For
example, when the terms cognitive radio and software-defined radio (SDR) are used, there is no general
agreement on what these terms mean. Moreover, these terms are often mistakenly used
interchangeably, further muddying the waters.
Therefore I will set the stage with a definition of cognitive radio networking. The definition is as follows:
Cognitive radio networking is a paradigm for wireless communications in which
either a network or a wireless node changes its transmission or reception
parameters to communicate efficiently while avoiding or mitigating interference
with licensed or unlicensed users.
This is the definition I will use as a starting point, but I will break the technology down into subsegments, and then discuss each one and the role it plays in the process of smart spectrum optimization.
The key elements of cognitive technology include spectrum sensing, spectrum management, spectrum
mobility, spectrum sharing, and spatial processing.
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Spectrum sensing may be defined as interference-based detection of transmitters with the ability to
look at a portion of the spectrum to see if it contains any transmitters that could cause interference to
the cognitive radio system.
Spectrum management is the ability of the system to capture the best available spectrum for use at any
given point in time. It is based on the premise that both terminals and base stations can be directed to
change their operating frequencies dynamically as needed to keep the communications from interfering
with others in that portion of the spectrum, or of being interfered with by others in the same spectrum.
Spectrum mobility refers to the ability to make use of spectrum dynamically, commonly called dynamic
spectrum access (DSA). The system can decide to change bands or channels within the spectrum in
which they are operating.
Spectrum sharing is the ability for a cognitive radio system to operate in shared spectrum (unlicensed
spectrum for example), detect stations that interfere with the transmissions, mitigate that interference
if possible, or avoid it by changing operating frequencies or other system parameters.
Spatial processing is the use of multiple integrated receiver chains known as multiple input multiple
output (MIMO) systems that can provide another layer of resistance to interferers. MIMO processing
allows better use of the radio channel to improve link budget and data rates. The system can also be
used to track and cancel interference from multiple mobile transmitters using sophisticated signal
processing algorithms.
A true cognitive or intelligent radio network will make use of most, if not all, of these capabilities in
order to be able to dynamically keep the system operating by mitigating or dodging interference that
may show up in the frequencies the cognitive network is currently using. If the interference becomes
too severe, an intelligent system will be able to locate other spectrum and shift the radio links to new
frequencies nearly instantaneously.
Dealing with Interference
In general, the limiting factor in high capacity wireless systems is interference. As stated above, there
are a number of ways to deal with interference to keep the communications link up and running. Unlike
traditional systems (such as 3G and 4G), cognitive systems can recognize and then deal with
interference locally and in real-time, thus greatly increasing the capacity of new and existing spectrum.
There is both a science and an art to knowing when there is too much interference in the portion of the
spectrum in use. At that point, a truly cognitive system will locate another portion of spectrum with less
interference, move the communications to the new, clearer portion of the spectrum, and monitor for
interference in these new frequencies. It should be pointed out that there are multiple types of
interference that can create issues for communications, and different corresponding approaches to
mitigate their effects (see Note 2).
Some of the systems use a special MAC (Media Access Control) that senses other user transmissions and
delays its own transmissions until the desired radio channel becomes available. Wi-Fi is an example of a
system that uses this method. These methods work well when networks are carefully designed, or when
interference levels are low due to a small number of users on the system. They typically start failing
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when interference levels increase, a situation that is becoming common in both unlicensed bands (think
Wi-Fi in a crowded apartment building) and on commercial mobile networks.
Cognitive radios can have a major impact in such interference-laden environments. We are starting to
see cognitive radio products that not only have sophisticated DSA capabilities, but also advanced
interference mitigation techniques that allow these radio networks to remain on channels with
moderate to high levels of interference that would keep other radio systems from functioning. Some of
the highest performance cognitive systems use powerful algorithms to numerically “cancel” the
interference in their receiver chains. The ability to mitigate, rather than simply run away from
interference will be critical going forward. It is becoming clear that soon (if not already) there will be no
more “white spaces” and that all spectrum will be made up of “gray spaces” (interference laden
frequencies) caused by a system’s own self-interface or that which is caused by other nearby systems.
Cognitive Approaches
As can be seen from the above, there are many pieces to the cognitive technology puzzle. There are a
handful of companies working on these issues, some on specific parts and a few others on all of them.
For example, both Spectrum Bridge (www.spectrumbridge.com) and Microsoft (www.microsoft.com)
have developed a database approach to frequency reuse. This method was developed specifically to
enable unlicensed broadband systems to coexist with existing TV transmitters in the TV white spaces
band. In order to make sure that these devices are transmitting on unused spectrum, both companies
have developed database systems that plot the location of the unlicensed broadband transmitters and
compare that to a known database of the TV channels in use in the same area.
However, it can be said that this approach does not really meet the criteria for a cognitive network. TV
stations are static and once identified, the unlicensed broadband transmitters are told what channels
they can use, but not what channels they should use. Even so, this is a good example of one form of
spectrum management where two different services (TV broadcast and unlicensed wireless devices) can
coexist within the same portion of the spectrum.
Other companies, such as xG Technology (www.xgtechnology.com), have embraced a much more indepth approach of providing truly intelligent networks, essentially by encompassing all of the various
cognitive techniques detailed above. xG has been running an experimental wireless voice system in
Florida for almost two years using unlicensed spectrum in the crowded 900-MHz band. It has also
undertaken extensive trials with the US military as well as American rural telecom operators in Arkansas
and Florida.
Taking Cognitive Radios Further
A drive test I conducted of the xG xMax network in Fort Lauderdale, Florida (all while watching the
spectrum in real-time on a spectrum analyzer) showed that when the mobile unit drives into an area
with existing interference, the xMax network automatically changes to a different channel to avoid the
interference. This change occurred fast enough that not a single syllable of speech was missed. This
prototype network has led to further development by xG, and earlier this year the company fielded test
systems with the U.S. Army that can be used in combat zones to establish a fully mobile expeditionary
network.
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xMax is the world’s first carrier-grade cognitive radio cellular network. The technology itself is
frequency-agnostic and is designed to answer the affordability and capability gaps of today’s cellular
technologies. It can deliver bandwidth for needed communications by accessing unused or underutilized
spectrum in a given area.
xMax is 100% IP-based, easy to deploy, and the terrestrial components can be connected via satellite
over long distances making it an ideal network for the military, public safety, or other mission-critical
network needs. It is also designed to enhance and extend commercial cellular networks with dynamic
spectrum access and self-organizing “cognitive” nodes.
Unlike other cognitive radio systems currently being trialed, xMax features advanced interference
detection and avoidance capabilities designed to work around both intentional and unintentional
interference. It has also been designed so it can be set up in an area to supplement a commercial
cellular network on a permanent or temporary basis. This need could arise due to damage to the cell
system resulting from a major storm or man-made disaster. It can also be used for capacity offload in
areas where a mobile carrier might have a shortage of spectrum. In this case, the system can be set up
to augment spectrum that is already in use by the network operator by tapping into other unused
spectrum in the area. This spectrum may be found in the 900-MHz ISM band where xMax operates
today, or the TV white spaces or other licensed or unlicensed bands in the future. A high-level overview
of the xMax architecture is shown in Figure 3.
Figure 3: xMax Network Overview
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There are several aspects of the xMax system architecture worth calling attention to:

First is its ability to integrate and support commercial off the shelf (COTS) devices such as
smartphones, laptops, tablets, and other Wi-Fi enabled devices. This means that existing and
highly capable end user hardware, operating systems, and applications can be deployed
seamlessly as part of the xMax system. It is also worth noting that these devices do not give up
any existing connectivity to commercial or private cellular networks.

Second is its use of network infrastructure that resembles small access points, yet offers range
and capacity comparable to large cellular base stations. As proven in the past, scalability and
flexibility in locating deployment (as facilitated by compact wireless nodes) decreases
deployment costs while allowing operators to tailor coverage and capacity to improve network
economics.

Third, while xMax uses its dynamic sensing capability to change channels and avoid interference,
it will also couple real-time sensing with the cognitive intelligence found throughout the
network elements to simplify deployment and operation via self-RF planning and self-organizing
networking.
Clearly the evolution of this technology is moving quickly. However, there are years of development
effort behind this product and many thousands of man-hours of research and development that has led
to the capabilities outlined above.
As we can see from this overview, the xMax network is designed to be scalable, flexible, and easily
deployed. It will support small teams to large commercial networks and will be able to seamlessly merge
with them or keep them separate as requirements dictate.
xG Technology is a unique example of a company that is using all of the various key elements of
cognitive technology discussed previously, and its system goes well beyond what is thought of as simply
a cognitive radio. In reality, xG has developed an end-to-end cognitive network solution that will help
make more efficient use of scarce spectrum and make it available for more types of applications and
users.
Cognitive Solutions in Practice
There are many instances where this type of network can be of assistance in making our spectrum go
further and providing more channels for use, even in congested areas. For the public safety community,
for example, which makes use of narrowband spectrum in seven different portions of the spectrum, xG’s
xMax system can be used to increase the capacity of these voice channels by adding real-time sensing
and interference avoidance. However, it can also augment existing spectrum capacity by leveraging
unlicensed spectrum for non-mission-critical communications, such as report uploads and administrative
traffic. This added capacity could also be used to carry other broadband traffic including video and dataintensive applications during a response or on an as-needed basis. This has the added benefit of taking
this traffic off the primary mission-critical network, while also adding more capacity to the overall
communications system.
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By supporting the use of existing smartphones and laptops that public safety agencies already own and
use on a daily basis, this approach makes both operational and economic sense. Also of importance to
first responders is to minimize the time and attention needed to set up a communications network in
the initial phase of a response outside an existing communications footprint. In particular, any task that
can be offloaded or delayed during the first hour (called the golden hour) enables first responders to
focus on the emergency at hand, rather than focusing on setting up their gear.
As highlighted above, the xG system is designed to be self-configuring and self-planning. However, of
equal importance to public safety users is that the system will be able to automatically reconfigure itself
if the network becomes inoperative due to damage, power outage, etc. to an access node. For example,
the xMax system will be able to change to mesh operation until such time as the network is repaired and
fully operational again (Figure 4).
Figure 4: Example of Leveraging Meshing Capabilities in xMax
In addition to mission-critical applications for public safety and the military user outlined above, there
are a number of other examples where the xG system can be deployed.
It is ideal for cost-effective voice and data services in rural areas where it can serve as a DSL loop
extension using unlicensed frequencies. Typically there are significant amounts of freely available TV
white spaces, 900 MHz, 2.4 and 5.8 GHz spectrum, etc. available in rural areas. The xMax wireless VoIP
and data system can easily serve existing and remote customers with voice and broadband connectivity.
This will be critical for telcos in rural America since the FCC recently voted to cut funding for POTS (plain
old telephone service) in favor of broadband deployments.
Another application for rural carriers would be to use xMax to offer wireless voice and data service
outside of their traditional market footprints to capture new customers from a competitor’s market.
This “edge out” strategy is well suited for wireless broadband systems such as xMax since the carrier’s
own DSL lines can be used as backhaul for the wireless access points deployed at the edges of their
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market boundaries. In particular, the system’s self-planning and self-optimization features make it a
good choice for wireline telcos, since it minimizes the need for in-house radio expertise, thus providing
additional cost savings.
Finally, it should be noted that these issues are not limited to a particular country or application.
Spectrum access, mission-critical communications, and rural broadband are issues faced on a daily basis
around the globe.
Conclusion
Spectrum is a finite resource that needs to be managed differently than it has been in the past. New
approaches and new ways of sharing spectrum must be found and implemented while assuring the best
possible communications on an as-needed, where-needed basis. As commercial technologies advance,
they become more spectrally efficient, but we are coming close to bumping up against Shannon’s Law
(see Note 3), so we need to add more ways of both managing and using our spectrum more efficiently.
Early work with cognitive and smart radio designs held promise but they were, for the most part,
unproven and cost prohibitive. Only recently have technology advancements in computing power and
other aspects of the technology become affordable. Companies such as xG Technology have been able
to rethink what cognitive radio is and how it should work, and then approach the solutions from an endto-end systems angle.
The bottom line is that this technology is available today, and gives us a workable platform for updated
ways of implementing spectrum management and optimization.
xG Technology’s xMax network architecture is intelligent, elegant, and provides affordable ways of
implementing, as well as enhancing, voice and in the near future, data networks. It has been designed
from the ground up as an end-to-end solution and the intelligence is shared both within the network
and in the devices at the edge of the network. In this way, the network can self-configure on installation
while dynamically optimizing its radio frequency (RF) plan.
It also will self-heal and reconfigure itself to adapt to network outages and failures.
Finally, xMax technology enables spectrum to be shared efficiently within the xMax network as well as
with other systems operating in the same frequency bands.
There is no one silver bullet for meeting our bandwidth and spectrum needs, but new ways of managing
our spectrum allocations, along with new technologies such as the smart cognitive solutions that have
been developed by xG Technology, will certainly take us closer to the goal.
Andrew M. Seybold
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ENDNOTES
Note 1
Unlike the first three generations, 4G cellular services have been developed from the ground up for
broadband data services, with voice to be added in the form of Voice over IP (VoIP) at a later time.
These fourth-generation services provide even higher data speeds and more capacity, but voice is still
transmitted over existing 3G equipment.
Note 2
To simplify the explanation, interference can be divided into bursty and continuous types. The first are
short bursts (a few hundred microseconds long) of RF power that are typical of cordless phones and
telemetry devices. They can be mitigated by applying adaptive interference error correction. A
traditional method to mitigate burst interference is to use forward error correction coding with
interleaving, increased transmit power, or reduced data rate in order to increase the total energy per bit
of the desired radio signal. One major issue with real-time data, like voice and video, is that the small
size data packets they transmit severely limits the effectiveness of traditional error correction coding.
These systems may also be limited by regulations and are not able to increase their transmit power. This
leaves slowing the data rate as one of the few workable options in traditional networks - which reduces
system and user capacity.
Longer bursts (several hundred milliseconds in duration and longer) can be removed from the spectrum
by using the spatial processing techniques enabled by multiple input multiple output (MIMO)
technologies. This is accomplished by sampling the interference on multiple antenna/receiver chains
and using spatial processing to eliminate the interference.
One of the cognitive methods used to avoid burst interferers is to employ a special type of redundant
coding such as that used in xMax. Interference will randomly destroy some of the data but the error-free
pieces can be identified on the receive end and reassembled to create an error free data packet.
Cognitive systems can take this a step further by using real-time sensing to identify the conditions they
are working in, and then dynamically optimizing their error correction operations to mitigate
interference. The key to this is the ability to detect the redundant pieces that are interfered with
without adding large overhead to the signal or latency to the data flow.
Note 3
Shannon’s Law refers to the Shannon–Hartley theorem, which defines the theoretical maximum rate at
which error-free digits can be transmitted over a bandwidth-limited channel in the presence of noise.
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