COGNITIVE FREQUENCY AGILE DIRECTIONAL RADIOS FOR MOBILE AD HOC NETWORKS

COGNITIVE FREQUENCY AGILE DIRECTIONAL RADIOS FOR
MOBILE AD HOC NETWORKS
Harris Zebrowitz, Dan Conklin, Andrew Cortese, and Carl Hein, and Thad Konicki, Jr.
Lockheed Martin Advanced Technology Laboratories
3 Executive Campus, 6th Floor
Cherry Hill, NJ 08002
856 792-[9891, 9899, 9868, 9893, 9859]
[hzebrowi, dconklin, acortese, chein]@atl.lmco.com
ABSTRACT
The highly mobile and autonomous Future Force
(FF) depends on tight integration through wireless
networks operating in highly dynamic radio
frequency (RF) environments. Communication
network quality and survivability are paramount to
precision and lethality. Advanced, dynamic
spectrum techniques will be key technological
enablers for emerging Warfighter Information
Network-Tactical (WIN-T) and Joint Tactical
Radio System (JTRS) architectures that meet the
warfighter’s need for combat superiority. These
techniques depend on efficiently sharing spectrum
resources and mitigating interference using
frequency agile radios. Likewise they will depend
on emerging directional antenna technology to
enhance communications networks and provide
additional low probability of intercept/anti-jam
(LPI/AJ) benefits.
This paper describes research being done at
Lockheed Martin Advanced Technology
Laboratories to develop a cognitive radio network
architecture, which combines dynamic frequency
and spatial control. Lockheed Martin has
developed collaborative, agent-based control
mechanisms to dynamically adapt and optimize
interaction of radios. It has also investigated
combining this technology with techniques to
predict link quality in order to minimize the effects
of link degradation on network performance. The
addition of link prediction to the cognitive-radio
architecture enables predictive routing and
distributed control of space and frequency
resources in a proactive, rather than reactive,
fashion. This paper focuses on the architecture and
initial simulation results and presents simulation
results quantifying the performance improvements
provided by the architecture.
1. INTRODUCTION
Warfighters face severe limitations in accessing
the electromagnetic spectrum. These limitations
result from significant growth in spectrumdemands for deployed spectrum dependent
systems, increased demand for information and
advanced command-and-control (C2) concepts
associated with net-centricity, and increased
competition for spectrum resources from
commercial and civil interests. This adds up to
near complete spectrum saturation in areas of
potential Department of Defense (DoD)
deployment. The rapid pace of modern net-centric
warfare means that spectrum needs change
constantly as warfighters physically move their
network infrastructure in response to battlefield
dynamics, terrain, and logistics. Spectrum
requirements determination is fragmented,
resulting in extensive manual intervention for the
removal of spectrum conflicts. The present
frequency assignment process is incapable of
satisfying dynamic spectrum demands of the
highly mobile FF. Dynamic frequency-agile
systems that use directional-antenna technology
will allow much more efficient and accurate
spectrum use across the battlespace.
Spectrum-resource planning is a multidimensional optimization problem that involves
space, time, frequency, and other dimensions, such
as modulation and coding techniques. The
problem is non-polynomial (NP) hard (i.e., there is
no direct optimum solution) (Figure 1). As the need
for more mobile, ad-hoc networking technologies
techniques for abstracting network models while
maintaining significant accuracy and gaining
multiple orders of magnitude in speed. The
efficiency of CSIM enables rapid evaluation of
communication performance and evaluation of the
sensitivity of network configurations to
unanticipated variations in the specified scenario.
In addition, CSIM’s usability by network novices
in simulation has been demonstrated on many
projects. Lockheed Martin is using CSIM on the
Communications-Electronics Research Development and Engineering Center (CERDEC) Communications Planner for Operational and Simulation Effects with Realism (COMPOSER) Program
as the basis for the Communications Effects
Simulator component.
Figure 1. Depicts Frequency-Agile, Directional
technology as a key enabler of future netted
systems. Space and frequency are among the many
important dimensions that must be dynamically
optimized and controlled for netted systems to be
robust and resilient. Presently there is no efficient
means to investigate ad-hoc directional networks.
technologies increases in DoD operations,
distributed, intelligent, decision-making and
control techniques will be needed to adapt the
behaviors of wireless nodes. Ultimately, these
techniques will autonomously learn and improve
network performance. The ability does not exist to
simulate and test different control mechanisms,
architectures, and techniques to understand and
quantify their effects on reliable connectivity
within highly mobile on-the-move (OTM)
networks. It is urgently needed.
Lockheed Martin has developed a generalpurpose, cognitive, mobile-architecture evaluation
tool to simulate wireless, OTM, network-centric
architectures. The tool uses Lockheed Martin’s
discrete-event simulator, CSIM, as its simulation
engine to perform Faster Than Real-Time (FTRT)
modeling and visualization of these large,
complex, mobile, wireless systems. The
architecture-evaluation tool is also comprised of a
graphical user interface (GUI) to configure the
simulation and present results to the user in a
concise fashion. A key characteristic of CSIM is
its ability to abstract the network model to achieve
FTRT simulations. Lockheed Martin pioneered
and demonstrated on prior government projects
2. COGNITIVE RADIO NODE MODEL
The core functionality associated with the
cognitive, mobile-architecture evaluation tool is
encapsulated in a model of a generic, cognitiveradio node that is run within CSIM. Figure 2
depicts functional diagram of the model. The node
consists of a traffic generator, transmitter, receiver
and services, resource manager, and predictor. The
service model receives data flow from the local
traffic generator or the receiver as relay traffic. A
channel model is external to the node model and
accounts for propagation effects. The user can
select various propagation effects and model
configurations to perform studies.
The traffic generator models data sources, such as
sensors or users, by firing off a particular data
flow at planned times, which are skewed by some
randomness to simulate actual traffic deviations.
An exponential random variable is drawn to
determine when the next data flow will occur,
where the mean is determined from the arrival rate
of the modeled traffic source. The data source
models are based on predefined Information
Exchange Requirements (IER). An IER is a data
record that describes the resource requirements
and characteristics of a data flow. An IER sits at a
higher level of abstraction than a packet-based
model. The IERs are only concerned with the endpoints of a communication, not how individual
packets, which make up a flow, are constructed.
The IERs simply describe a resource allocation
contract between end-points on a circuit.
Trigger
• Sensor
• Burst
• Scenario Script
• IER Database
• Traffic Generator
— IER Object
— Periodicity
— Trigger
— Size, Destination
• Entity Attributes
— ID
— Type
— Relations
— Waypoints
— Velocity
• Cognitive
Resource Mgr.
— Frequency
— Power
— Antanna
— QoS
— Security
• Transmitter
— Location
— Antenna Pattern
— Modulation Type
— Frequency
— Transmit Power
• Services
— DiffServ
— Routing
• In-line Prediction
— Self Position
— Neighbor Position
— Network State
• Receiver
— Location
— Antenna Pattern
— Error Correction
— Processing Gain
— Receive SINR
— BER
• Channel Model
— Tx Location
— Rx Location
— Inter. Loss
— Terrain Loss
GPS
Policy
Control
Channel
Figure 2. The model of the cognitive radio node is a key aspect of the cognitive, mobile, architectureevaluation tool. The model can be used to study the effects of different radio configurations on network
performance.
The service model is a representation of the node’s
core differential services (DIFFSERV). It is a
collection of service queues that are serviced on a
differential basis as defined by a predetermined set
of service classes. Each queue is serviced in a
probabilistic basis, as a reflection of traffic
handling and shaping policy. There is generally
one queue per service class, where a service class
defines how a particular queue is to be serviced.
While routing is accomplished using Open
Shortest Path First (OSPF), other protocols are
available. The routes are determined via link costs.
There are several link costs that can be specified
by the simulation, including bandwidth used,
latency, and loss. The cost can be used directly or
weighted and combined. The routing algorithm is
run at regular intervals throughout the simulation.
Rather than modeling detailed, wireless-network,
link protocols and individual packet transactions,
DIFFSERV quality of service (QoS) models and
routing abstractions capture the behavior of the
protocols and the effect of the detailed, individual
transactions up to the IER data transactions to
significantly reduce complexity and execution
times. The non-linear effects of re-transmission
(due to Media Access (MAC) contention based on
the type of link-layer mechanism or congestion)
are modeled probabilistically. Overhead is
appended to the IER based on predefined
probability of error versus signal interference
noise ratio (SINR) curves.
Adaptive frequency and special assignment will
ideally be done on a service-class basis as a
function of the QoS requirements of each class.
Those service classes deemed appropriate to
accept higher levels of interference will permit
narrower, guard-banding, channel requirements,
thus, allowing closer packing of the assigned
channels. The amount of interference deemed
acceptable within a particular service class can be
addressed by varying queue service times and
assigned bandwidth of the guard bands. Trade-offs
arise when guard bands begin to narrow and
interference levels increase in the channel. Higher
interference levels cause higher probability of
re-transmission. Queue saturation comes into play
as rates of re-transmission increase. The
evaluation of queue statistics, such as queue length
and service-time attributes in the differential
services model, is another topic.
The core transmitter and receiver of the cognitiveradio node model are consistent with typical
transmitter/receiver models. These components are
composed of adjustable attributes, including
modulation type and frequency, transmission
power, error correction schemes, antenna pattern
and height, and other characteristics.
At the heart of the cognitive-radio architecture is
the cognitive resource manager. The resource
manager is responsible for coordinating frequency
and spatial resources. Directionality and frequency
agility offer potentials for improved capacities but
present additional degrees of freedom that must be
coordinated over time-varying conditions. An
intelligent, agent-based approach, which is
scalable with low overhead is being explored to
network nodes that can exploit and adapt these
degrees of freedom. Centralized control methods
to address complex network topologies are
inherently unscalable. The centralized network
master eventually fails to efficiently control a
large network because of the high volume of link
metrics needed to be transmitted to the centralized
location and increased exposure to stale data. An
intelligent-agent approach offers many advantages
over centralized control methods. Intelligent
agents are persistent software processes with
goals, responsibilities, constraints, and state. Each
manages a specific physical entity or instance, and
in doing so, may negotiate with other agents to
cooperatively and optimally use resources.
Compared to traditional reflexive protocols,
mobile-agent approaches have been shown to
cooperatively improve global network operating
conditions across a wider variety of multiple
performance metrics. Being decentralized, agents
can avoid communication bottlenecks. By
eliminating central-point criticality, agent systems
are more robust and resilient to transient events.
A key aspect of the cognitive resource manager is
having a common control channel that shares
frequency and position information. Given that the
control channel may not always be present, the
resource manager’s command authority could
continually grow inconsistent with reality. The in-
line prediction component of the cognitive-radio
node architecture provides a means of inferring
this information during the control channel’s
absence. In-line prediction is spectrum and
spatially aware and accounts for nodal position,
trajectory, neighbor spatial state, RF conditions,
and antenna directionality to infer forthcoming
network-configuration changes. GE-GRC developed the in-line prediction component of the
cognitive-radio node architecture.
The cognitive, mobile-architectures evaluation
tool models propagation and radio interference
effects on IERs as they are routed through the
network. These effects provide bit error-rate
predictions based on modulation types and errorcorrecting coding schemes that predict the
effective bandwidth of the links. A matrix
laboratory (MATLAB™) based Space-TimeModel (MATLAB STM) is leveraged to predict
bandwidth. It was developed by Lockheed Martin
to provide physical-layer fidelity in modeling
interference impact onto legacy systems from
dynamic spectrum allocations. Several channel
models are supported in the MATLAB-based
STM. Simple Square-Fourth and arbitrary powerloss models are available along with the Hatta
model. The STM links to the National
Telecommunications & Information Administration (NTIA) irregular terrain model (ITM) codes
and supports point-to-point and area-prediction
modes of operation. The ITM library is based on
the Longley Rice model. The model supports
simple functions for additive white gaussian noise
(AWGN), Interference Temperature, Rayleigh
fading, or Rayleigh-Multipaths with arbitrary
Jake’s model, where one defines the number of
mulitpaths, delay vectors, and the power vector.
Both Rayleigh fading models include variable
Doppler spread. For point-to-point modeling any
terrain model with any resolution can be used
(Digital T errain Evaluation Database (DTED),
Global Land One-kilometer Base Elevation
(GLOBE), or other) because the terrain is defined
as a two-dimensional array. Terrain is interpolated
along the path of interest.
The operation of cognitive, mobile-architecture
evaluation tool begins with loading the scenario
inputs. The inputs are (1) an IER file: userselectable file of all the IERs for a particular
scenario; (2) scenario file: user-selectable file of
the trajectories of the nodes; (3) map file: userselectable file of a map and terrain; and (4)
equipment file: user-selectable file of a list of node
configuration for the specified scenario. After
these files are loaded into CSIM, nodes are
instantiated, IERs are assigned to each node, and
the simulation executes. A GUI controls the CSIM
simulation (Figure 3). Lockheed Martin used the
National Instruments’ LabVIEW tool to develop
the GUI. The GUI clearly displays network status
throughout each run. It also collects statistical
information during the simulation and generates
corresponding data files that can be read as input
by analysis routines.
3. EXPERIMENTS AND RESULTS
The cognitive, mobile-architecture evaluation tool
evaluated a network of 30 nodes of radios.
Homogeneous radio nodes were used for the initial
experiments (i.e., all nodes used directional
antennas or all nodes used omni antennas).
Movement trajectories of the radios were also
specified along with traffic-communications
requirements. The simulation enabled
parameterized runs. Metrics collected included
signal quality, latency, and loss. Baseline results
were collected with no frequency agility using an
omni antenna. The performance improvement
provided by dynamic frequency control was then
measured, again using omni antennas. The
experiments evaluated the benefits of using
directional antennas without any form of
frequency management. A four-beam-switched
beam antenna was used in these experiments
(Figure 4). Finally, a simulation configuration that
combined directional and frequency-agile capability was evaluated.
An experiment that measured relative network
throughput versus radio-node capacity (Figure 5)
was run. The purpose was to investigate how the
network behaves as a function of available radio
capacity under the different configurations
described. The simulation was instrumented to
collect the accumulated bandwidth (BW) used
across the entire network during the course of a
run. This accumulated BW includes multi-hop
relay traffic and traffic generated by each node.
The relative network throughput value was
obtained by dividing the collected values on each
run by the peak value obtained across all runs. The
normalized node capacity is the capacity of the
node for each run divided by the largest node
capacity tried. In this experiment, the radio
Unit ID and Node Type
Focus Node
Connections
Bandwidth
Utilization
Bandwidth Channel
Breakdown
Figure 3. The GUI allows network and node-level statistics to be collected and displayed. It shows
bandwidth use through the course of the Bandwidth Channel Breakdown simulation. Here we see that a
significant amount of relay traffic (blue bar) is causing the node to fail.
4 Beam
Separation = 45Deg
Figure 4. Directional antenna pattern used in
experiments (one of four switched-beam antennae
shown).
capacity was increased while the traffic volume on
the network was fixed. The relative network
throughput was then measured. The CSIM
simulation was run from start to finish for each
capacity value. In this experiment, node positions
were known by neighboring nodes and the antenna
beam steering was coordinated on a per hop basis
through a control channel based on SINR, i.e., the
link with the highest SINR was chosen. The
frequency-control algorithm also used SINR. If
this fell below a set value, then a new frequency
from the pool of available frequencies was chosen.
Interference from neighboring nodes was modeled.
The interference seen by each node accounted for
propagation effects as well as the antenna gain of
the interfering transmitters and its own antenna
gain. The gains were based on transmitter and
receiver steering angle for directional nodes. A
multi-hop network was simulated. An OSPF
routing algorithm was used to periodically
compute the routes. All the radios had the same
power level. Power control was not included in
this experiment.
The results were as expected. When the radio
capacity was low there were many IERs
(messages) dropped because the message queues
overflowed in the service model. Under these
conditions, all the experimental configurations
Figure 5. Relative throughput versus node capacity across different configurations: omni, directional
antenna (Dir), omni with frequency agility (FA), directional antenna with frequency agility (FA Dir). The
number of nodes N=30. The number of frequencies available for the frequency-agile radios to use was
N/2.
show the throughput to be low. As the capacity of
the nodes increases, so does the relative network
throughput. When the capacity crosses a threshold,
the throughput climbs. This occurs when
congestion causing the queues to overflow is
alleviated due to sufficient radio capacity to
service the traffic demand. After this point, the
throughput continues to climb because the
probability of a burst in traffic causing a queue to
overflow continues to decrease as the capacity
continues to increase. As capacity increases, the
average amount of data filling the queues shrinks.
Eventually, as capacity increases, the performance
of the different configurations converge and level
off. All the data gets through. Prior to this point,
the combined frequency-agile directional
configuration has the highest and omni has the
lowest throughputs. Frequency-agile performs
better than directional because throughput is
directly proportional to the link SINR ratios. The
SINR is a function of propagation loss and
interference from neighboring nodes. Low SINR
causes higher overhead due to retransmissions.
This reduces throughput, which is just measuring
actual payload, not retransmission overhead. The
frequency-agile directional configuration has the
lowest interference level (highest SINR) and the
omni configuration has the highest interference
level (lowest SINR). However, the dominant effect
is frequency agility. Directional nodes are not as
effective decoupling signals as frequency-agile
nodes, so the throughput of the directional network
is lower than that of the frequency-agile and
frequency-agile directional network.
4. SUMMARY
Operational requirements for spectrum use of a
given force structure and interaction between
battlefield systems lack definition, are incomplete
or unknown. Spectrum-use processes also lack
prior consideration of potential impacts on
spectrum due to environment, geographical area of
operations, force locations, potential adversaries,
physical environment, or other simultaneous
operations. The effort described in this paper
produced a cognitive, mobile-architecture
evaluation tool that begins to consider these inputs
while evaluating future cognitive, mobile-network
architectures. The tool uses CSIM as its core
simulation engine. A primary element of the
cognitive, mobile-architecture evaluation tool is
the cognitive-radio node model. The node consists
of a traffic generator, transmitter, receiver, and
services, resource manager, and predictor, which
can be configured through programmable
attributes. The simulation is managed through
GUIs.
The present study investigated the use of mobile
radios that were frequency agile and directional.
Two preliminary experiments were performed that
investigated the effect of varying node capacity on
relative network throughput. The simulation
results indicated that the configuration that
combined frequency agility and directional control
was superior, providing greater throughput than
the other configurations (omni, directional antenna
only, omni with frequency agility). Frequency
agility appeared to the dominant feature that
provided improved network performance. The
simple directional antennas provided additional
improvements but was a secondary contributor.
There is more work to be done to understand
cognitive, frequency-agile, directional networks.
Many different scenarios need to be run. Varying
network size, node density, and terrain type are
just some of the external variables that effects
need to be investigated. Different traffic
distributions and relative trajectories must also be
considered. Power control must be activated.
Improved MAC layer protocols need to be
developed and incorporated into the network
model. These areas of research are being pursued
at Lockheed Martin and are expected to
dramatically improve performance. From a
cost/performance standpoint, understanding the
performance of different antenna types is perhaps
the most important and interesting feature of
cognitive, frequency-agile, directional networks to
be investigated. The antenna characteristics that
need to be investigated include beamwidth and
sidelobe level, the number of beams (switched
beams), and the use of adaptive arrays. CERDEC
has formed a partnership with Lockheed Martin to
establish a working environment for the
cooperative development of an antenna-modeling
library for ad-hoc, mobile, wireless networks. The
work described in this paper lays the foundations
for this research. The ongoing Cooperative
Research and Development Agreement (CRADA)
is investigating how best to use directional arrays
in mobile, ad-hoc networks. This CRADA will
yield significant insight into how antenna effects
impact link performance and overall operation of
the OTM network. This will assist the government
in its ability to design and evaluate future antenna
designs for OTM networks and in performing
antenna optimization analysis.
5. BIBLIOGRAPHY
[1] Lockheed Martin Advanced Technology
Laboratories CSIM - http://www.atl.lmco.
com/proj/csim
[2] “Virtual Network Simulator Feasibility
Study,” Tactical C2 Protect ATD Task C2DD06-FY01, US Army Communications Electronics Command REDEC, February 12,
2001.
[3] “Smart Antenna Technologies for Future
Wireless Systems: Trends and Challenges,”
IEEE Communications Magazine, September
2004