Cognitive RF Systems and EM Fratricide
Gerard T. Capraro and Ivan Bradaric
Capraro Technologies, Inc., 2118 Beechgrove Place, Utica, NY 13501 USA
Abstract
In many parts of our world the radio frequency (RF)
spectrum is overcrowded. The Department of Defense
and researchers throughout the US have been
addressing this problem by developing cognitive
radios, networks, and radar systems to intelligently
choose frequencies, waveform parameters, antenna
beam patterns, etc. to operate with conventional
receivers without causing electromagnetic (EM)
fratricide. In most of the documented work that
addresses EM fratricide to date, there is an inherent
assumption that the cognitive system knows when and
where the fratricide occurs. However, the authors
usually do not make known how this information is
obtained. In this paper we propose two approaches of
how the victim receivers can work together with
cognitive systems and share the knowledge of their
EM status.
1.0 Introduction
We are informed via many articles and studies that the
radio frequency (RF) spectrum is crowded and more
space is needed for wireless internet access,
communications, and for military usage. Just recently
the US Congress passed a bill to open up more spectra
[1] to auction off RF frequencies belonging to the
television broadcast industries. However, this alone
will not solve spectrum crowding. When the frequency
spectrum is measured over time, technologists have
shown that the spectrum is underutilized. Recognizing
this, there have been numerous research projects
funded by the US Department of Defense (DOD).
These research efforts go back many years to the
USAF investigating software programmable radios.
The Defense Agency Research Project Agency
(DARPA) has probably funded the most projects in
this area. Through this research, we now have two
distinct users defined as the primary user (PU) (i.e.
those who own the license for the frequency range)
and the cognitive user (CU) (i.e. those users trying to
share the spectra either by using broadband signals or
sampling the spectra in time and transmitting when the
PU is not transmitting). Most significant projects in
this area include the DARPA XG program and the
Wireless Network after Next (WNaN) program. In
addition to these efforts, there has been a move to
apply Cognitive Radio (CR) technologies to the radar
domain (Cognitive Radar efforts) and radio networks.
Some of these systems sample the spectrum and
transmit if no one else is transmitting at any given
frequency. This approach can cause electromagnetic
interference (EMI) in nearby receivers. Many people
have recognized this problem and have addressed it in
many different ways [2 - 6]. Many of their solutions
inherently assume they know information about the
victim receiver, but do not address how this
information is obtained. We propose herein two
different approaches to solve this problem.
The paper is organized as follows. Section 2 briefly
describes some cognitive radio and radar efforts.
Section 3 defines the problem we address in this
paper. Section 4 presents some potential solutions.
Section 5 provides our conclusions and highlights
some topics for future work.
2.0 Some Cognitive Efforts
Next Generation (XG) Program
The XG (neXt Generation Communications) program
is developing an architecture that will open up the
spectrum for more use by first sensing and then using
unused portions of the spectrum. Some early goals of
the XG program were:
1. Demonstrate
through
technological
innovation the ability to utilize available
(unused, as opposed to unallocated) spectrum
more efficiently.
2. Develop the underlying architecture and
framework required to enable the practical
application of such technological advances.
Figure 1 from [7] is a logical functional diagram of the
concept of operations of XG’s policy-agile spectrum
user, which uses a computer understandable spectrum
policy capability. The major components are the
Sensor (which senses the environment for determining
its availability), Radio (the communications device
that can dynamically change its emission and
reception characteristics), Policy Reasoner (manages
spectrum policy information), and System Strategy
Reasoner (manages the multiple radios on a platform).
radio frequency (RF) spectrum and to provide reliable
communications at all times. A basic cognitive cycle
view of the radio is illustrated in Figure 2. A general
overview and projections of the Cognitive Radio in
our society can be found in [10].
Figure 1.0 Policy-Agile Operation of XG SpectrumAgile Radio
The last two components are of particular interest in
that they utilize Semantic Web technologies.
Operating a radio in different parts of the world
requires that radios abide by the policies in the area
where they are located. The XG program has
developed its own XG policy language (XGPL) which
uses OWL as its standard representation and will be
implemented within the Policy Reasoner.
The Wireless after Next (WNaN)
The WNaN being performed by Raytheon BBN
Technologies and funded by DARPA [8] is developing
a scalable, adaptive, ad hoc network capability that
will provide reliable communications to the military.
The basic ingredients of their design are composed of
a Dynamic Spectrum Address capability based upon
the XG program. It also has 4 multiple transceivers
and a disruptive tolerant networking (DTN) capability.
The four transceivers provide fault tolerance and
allows the system to pick the best channel for
communications. The DTN capability allows the
nodes to store packets temporarily during link outages.
The WNaN also has content based access that allows
users to query the network to find information and
allow the system to store critical data at locations to
minimize time and bandwidth. The system also has
multicast voice with quality of service and the network
protocols are designed for battery operated handheld
devices with energy conserving capabilities.
Cognitive Radio
Another effort related to communications, and having
similar goals to the XG program, is the Cognitive
Radio [9]. Its objectives are to efficiently utilize the
Figure 2.0 Basic Cognitive Cycle
Cognitive Radar
Interest in cognitive radar is growing in the radar
community. Figure 3 describes a recent architecture
we are currently working on while an earlier version is
described in [11]. Figure 4 describes a cognitive radar
that is primarily concerned with the tracking stages of
a radar [12]. In Figure 5 a cognitive radar architecture
is shown from the first textbook written on this subject
[13]. The commonality of these designs are the
feedback loop between the transmitter and receiver,
use of outside sources of information, and the
implementation of a learning process.
Figure 3.0 A Cognitive Radar Architecture
situations are nonlinear and we must protect our own
receivers.
If we are going to deploy cognitive radios and radars
nearby conventional receivers we may have to rethink
our current CR policy rules. To meet the challenges of
the future we need to change.
Figure 4.0 A Cognitive Tracking Architecture
Figure 5.0 Another Cognitive Radar Architecture
3.0 Problem Definition
In all of the above programs and many others, the CU
chooses which frequency to transmit using frequency
policy rules based upon location and whether someone
is currently transmitting within the range of interest.
There are at least three issues with this approach. The
first is related to the sensing of the environment. What
happens if a nearby receiver is not transmitting but is
waiting to receive a signal at frequency f1, for
example a bistatic radar receiver or an electronic
warfare receiver? They don’t transmit, they just
receive. The second issue relates to the following
scenario. Let us assume that one decides to transmit
broadband signals below the sensitivity levels of any
nearby receivers. As the number of CR increases, the
signals within a nearby receiver’s passband may
exceed the noise floor and interfere with the
performance of the receiver [14]. The third issue
occurs when a CR decides to transmit at a particular
frequency because there are no signals present. The
chosen frequency is based upon a linear relationship
between the frequency chosen and the sensed
environment. The decision policy does not take into
effect the nonlinearities between the chosen frequency
and other nearby frequencies which can mix
nonlinearly and cause receiver intermodulation or mix
within the receiver’s frontend and cause spurious
responses. Most electromagnetic interference (EMI)
EM Compatibility Paradigm Shift
EM fratricide is the situation where we degrade the
performance of our own system(s) with our own
system(s), e.g. an onboard radar’s energy is received
by an onboard communication receiver and that
degrades the receiver’s performance. This is a serious
problem, since there are multiple sensor and
communication systems onboard platforms. Military
weapon systems are engineered to prevent such
phenomena between hardware located in close
proximity. The military has standards for describing
how to build and test hardware for EMC, and how to
test weapon system platforms for EMC, e.g. Military
Standards 461E and 464. The DOD has also developed
EMC prediction tools to assess the EMC of its weapon
systems. These tools were developed during the 1970s
and 1980s and have been enhanced and are used
today. They were developed according to military
standards to assure proper system’s testing was
performed, because most of the systems developed
then were deployed in space where fixing EMI
problems is not practical. Using software tools to
perform EM measurements in the 1970s was a major
paradigm shift for the EMC community.
Just as we needed a change by using software tools to
assess a system’s EMC in the 1970s, we now need to
rethink how to build complex systems that employ
waveform diversity and some of the proposed XG and
cognitive radio and radar spectrum management
concepts. Whereas in the 1970s we required software
tools to predict where to hone our measurements, we
now need to use software to help determine when EMI
may occur in real-time, and manage the EM spectrum
while the platform increases its total performance.
This performance gain is not related to just one system
onboard the platform, but to a system performance
measure of the total platform, where the platform may
contain communications, navigation, radar sensors,
etc. The EMC tools used today assess the performance
of an individual stovepipe system, e.g. the increase in
bit error rate of communications equipment and the
decrease in probability of detection for a radar. The
predictions made by these performance measures are
usually related to the signal to noise plus interference
ratios computed for each transmitter coupled to each
receiver. The tools also compute the sum or
integration of all transmitters’ coupling into a
receiver(s) along with a hypothesized EM spectrum, to
represent the environment, and to predict an integrated
or total EM ratio which can be related to a receiver’s
performance. This method identifies the performance
of each receiver, but it does not alert us to the
degradation of the total weapon system’s performance.
In addition, each computation is performed for a fixed
set of operating conditions for each transmitter and
receiver of EM energy. This approach is acceptable
when analyzing a weapon system with conventional
equipment, where each system’s performance is
assessed independent of all others. However, this is
not acceptable for a weapon system or platform with a
global performance requirement(s) or when the
waveform parameters of one or more of its systems are
changing in real-time e.g. a cognitive radio or radar.
Our methods of building EMC systems must change to
meet this dynamic environment.
4.0 Potential Solutions
To solve the issues discussed above some people are
looking to change the beam pattern of the transmitter
so that the power coupled to a victim receiver is
reduced, some wish to change the transmitted signal’s
polarization, and of course, there is the attenuation
gained by employing orthogonal waveforms. All these
solutions help reduce the amount of degradation
caused to a friendly receiver. However, these
techniques inherently are assuming that one knows
that the receiver is being degraded. How would a
cognitive radar, radio or a WNaN know about the
receiver?
There are currently two scenarios where one can
implement a capability to solve the fratricide issue.
One is on a single platform such as an aircraft, ship, or
a complex weapon system where multiple
conventional and cognitive EM equipment reside. The
second scenario is concerned with WNaN where we
propose to extend its capability and add a gateway to
communicate with non cognitive radios as developed
under another DARPA program. The EMC paradigm
shift for both scenarios requires that the equipment
report to a node that is managing the EMC of the
platform or the total network.
Let us consider the single platform scenario first. The
system strategy reasoner in Figure 1 and the strategy
creator in Figure 3 need to be extended to handle our
total platform with information being obtained from
all the non cognitive receivers on or near the platform.
We need a cognitive sensor platform network that can
create strategies, evaluate them, learn and modify
strategies as the platform sensor system operates. This
learning should also be transferrable to other
instantiations of the same type of platform.
The single platform scenario requires sharing
information among sensors. Each of the sensors has its
own signal and data processing capability. An
intelligent processor is needed to address fusion,
control, and communication between sensors. The
goal is to be able to build this capability so that it can
interface with any sensor and communicate using
ontological descriptions via an intelligent platform
network. The intelligent network will be able to
coordinate the communications between the on-board
and off-platform sensor systems. There are also
communications issues that need to be addressed for
the sharing of information and for minimizing the
potential of EM fratricide. The intelligent platform
should determine if there is EM interference potential
when a sensor varies its signal characteristics which
may cause interference to a receiving sensor. Rather
than have each sensor on a platform operate as an
independent system, one needs to design our platform
as a system of sensors with multiple goals managed by
an intelligent platform network that can manage the
dynamics of each sensor to hopefully meet the
common goals of the platform. This approach will
require modifying current platform weapon systems
which maybe very costly to implement.
Let us now consider the WNaN scenario of a Mobile
Ad Hoc Networks (MANET) [8]. How will we know
that the nodes on the network are causing fratricide to
a nearby non cognitive receiver? One method is to
communicate with friendly receivers similar to the
first scenario discussed above. The second method is
to use the research findings of another DARPA
program called the US Army’s Future Combat
Systems Communications (FCS-C). This program has
developed a Gateway [15] for conventional receivers.
“DARPA demonstrated that previously incompatible
tactical radios can communicate seamlessly by using
the network’s Internet protocol layer. This method
offers the potential for more affordable military
communications between legacy and coalition radios
in the future.” If we add this approach to the WNaN
system it will be possible to know when we are
interfering with conventional receivers. According to
[8], a capability to collect data as to the number of
packets sent for each node, priority type, emitted
frequency by each node, etc. has been built. There
exists over 300 statistics that can be gathered. If we
fuse the gateway from the FCS-C program with the
statistics gathering capability of the WNaN we may
be able to infer when there is EMI caused to
conventional receivers connected to either the WNaN
system or the FCS-C system.
One approach would modify the conventional
receivers to report when they are suffering EMI to the
cognitive networks via the gateway. Another, and
possibly less costly, approach is to infer when EMI
has occurred by monitoring statistics at the gateway
where the number of packet errors and resends are
requested by the conventional receivers. A smart node
could infer based upon the conventional receiver’s
tuned frequency and the nearby emitter frequencies,
power levels, antenna gain patterns, etc. which non
linear EMI situation is causing the fratricide. This
cognitive approach can learn on the fly and restrict
certain EM scenarios to alleviate the interference. If
either of these approaches are implemented then
another needed EMC paradigm shift will occur.
5.0 Conclusions and Future Work
Cognitive radios, radar and networks are a fascinating
area of research. Once they are fielded they will
unclutter the RF spectrum for future use. More
research is needed to make these systems compatible
with conventional transceivers. The potential solutions
presented herein should be pursued so that systems
and networks can self heal from any EM fratricide that
can occur. However, to do so the system must know
that a receiver is being degraded. To make this happen
one needs to study the FCS-C gateway approach, the
WNaN gathered statistics, the logic to process these
statistics to determine whether EM interference is
occurring, how it was caused, how to eliminate the
interference, learn from the process, and change the
strategy. We also need to study how we can easily
modify current systems such as an aircraft or ship and
add an intelligence capability that will allow cognitive
radios and radar systems to work compatibly with
conventional transceivers while maximizing the
performance of the total platform.
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