ANNUAL REPORT 2012

comparatively more frequent instances where
there is a negative mismatch between demands
for and supply of communication resources. It is
expected that resources can be allocated in a
manner where some users and services, that have
been identified as particularly important,
experience less service disruptions in these
situations.
Traditional network management systems are based on
a manager-agent architecture where each network element
contains an agent that provides software component
interfaces, which allow for remote configuration and
extraction of variables. A centralized manager
periodically polls the agent of a network element to get or
set a value of different variables’. An agent of a network
element can also asynchronously send a message to the
manager, e.g. reporting the occurrence of a fault.
Traditional network management systems are managed
manually by a network system operator via some
graphical interface. The wireless networking
environments that are of primary interest in this research
project, characterized by the properties described earlier,
bring several added challenges to traditional, centralized
network management paradigms. Such traditional
network management paradigms often rely on centralized
and human-controlled management decisions propagated
to network elements, which are clearly not adequate as a
centralized entity can never be expected to have and
analyze the network state information that is necessary to
make informed network management decisions. This
proposed research primarily addresses two management
aspects, performance management and security
management, coupled to a support tool that is used during
the provisioning, operation and evaluation of a cognitive
network on a per-mission basis. We will here and in the
following research areas use the term Cognitive Network
Management System as an abstraction for this software
tool.
3. Goal
The goal is to give an architectural description of a
cognitive radio network management system in the
context of network and spectrum management of next
generation military tactical communication system. The
main capabilities that are focused on are: simplify and
shorten the mission preparation and configuration phases
for national and multi-national deployments of tactical
networks, improve tactical network performance by
enabling dynamic spectrum and network management,
and improve spectrum utility to maximize the use of
military spectrum allocations. Some identified important
aspects are: security aspects of cognitive network
management, capabilities for local processing of goals,
monitoring of the local environment, reaction to
contextual events by self-configuration and information
propagation for interactions with and between CNMS
components and network elements, objective functions
and their interrelations to measuring and control
parameters. As a reference point for describing the
architecture a waveform is described and analyzed from
the perspective of measures, control parameters, behavior,
and their relation to objective functions and mission based
utility. Identification of important parameters, their
behavior and relations are conducted by smaller
simulation (small world isolation) and implementation
studies included in the project. The approach for
designing these experiments is based on requirement
extraction from available scenarios and user cases. The
main focus is directed towards the goal of indentifying a
feasible architecture for cognitive network management
system, i.e. its structural design and its behavior..
4. Accomplishments
The proposed system architecture is a multi-tier structure,
where individual tiers can operate autonomous without
overlaying tiers. These tiers reflect the horizontal structure
in a network of networks. It includes everything from
individual parameters of a protocol executing on a
platform to higher level policy respiratory. The
management interfaces, figure 1, towards the waveform is
inherited from architecture derived in the ARAGORN
project [3]. The low level control and learning, blue box
in figure 1, is representing all algorithms that control and
optimize the operation of the system in short term. It is a
very big challenge to truly understand all these control
parameters in waveform, how they are dependent on each
other and their time constant. In order to get a better
understanding of such control parameters, mechanisms,
and their behavior the project especially have looked at
evolutionary algorithms for resource assignment [4]-[6]
(typically NP hard problems), were algorithmic stability
(robustness), algorithmic convergence and time
complexity especially been studied for centralized
algorithms. The next step is to look at the same algorithms
with decentralized implementation.
Figure 1. Functional modules and interfaces.
Furthermore, to understand and to be able to model the
behavior of the mechanisms a theoretical benchmark
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