COGNITIVE NETWORK ACCESS
USING FUZZY DECISION MAKING
Nicola Baldo and Michele Zorzi
Department of Information Engineering –
University of Padova, Italy
Presented By: Andrew D’Souza
Petar Kramaric,
Srdjan Lakovic
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Topic Problem:
• To achieve maximum performance or
throughput for connecting to a wireless
network.
• To identify a solution which is able to work
well and adapt to various scenarios
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Previous Implementations
• Several schemes have been put into practice:
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Highest RSSI Scheme
Linked Capacity Scheme
Network Capacity Scheme
Low-Delay Scheme
• Problem: these schemes consider specific
wireless technologies (802.11).
• Problem: these schemes target scenarios in which
the wireless link is the bottleneck.
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Proposed Implementation
• The approach proposed: cognitive network
access using fuzzy decision making.
• Fuzzy arithmetic is used to evaluate the
communication quality from each access point
(AP).
• From this the most suitable access point is
selected.
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Proposed Implementation [2]
• Concentrate specifically on solving
communication performance issues.
• Specifically throughput, delay, and reliability.
• The proposed scheme can adapt to various
technologies.
• Cognitive because it makes use of Fuzzy Decision
Making.
• The type of network model being used is a
cognitive network model.
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Proposed Methodology
• Different components of communication
performance:
– Radio link performance
– Transport layer performance
– Core network performance
– User application requirements
• Using known eqn’s to find the above
components, the paper produces the
following formulas
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Proposed Methodology [2]
• The network layer end-to-end performance
for each AP i is determined by (1):
• Then, transport-layer performance is derived
(2):
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Proposed Methodology [3]
• To obtain an overall measure of the fitness of
AP i to meet the users needs:
• Derives to:
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Algorithm
• Step 1:
– Collect fuzzy performance metrics
– Throughput, Delay and Reliability for radio link, core network, end-toend, transport and application requirements
– Application requirements produced by the application
– Radio Link metrics provided by the AP
– Transport Layer Performance (end-to-end) collected in two ways:
• Direct measurement
• Estimates calculated by the cognitive engine
– Core Network Performance measured by all peers
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Algorithm [2]
• Step 2:
– Process the the metrics collected using proposed
formulas
– The network layer performance for each AP is
determined by combining Radio Link and Core
Network performance
– The transport Layer is derived by applying an
extension principle
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Algorithm [3]
• Step 3:
– The fuzzy metrics calculated provide an estimate
of the communication performance
– In this step we compare them with the estimates
of the application requirement
– The degree of fitness for a particular AP is defined
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Implementation
• Set two Access Points
– Two mobile device (N95) acting as AP using 3G
connection
• Java program:
– Runs on the client and gathers data from our
cognitive network database
– Process data using proposed formulas
– Display the suitability of both nodes
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Future Work
• How to deal with users that maliciously
provide wrong information to influence other
nodes decisions
• Identification of effective means and
strategies to achieve information sharing in
Cognitive Radio Networks
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LA
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Proposed Conclusion
• Numerical results show that the proposed
(cognitive network) scheme performs
significantly better than state of the art
solutions, in terms of both overall
performance and fairness.
• This scheme is suitable for multi-technology
scenarios, not just the 802.11 technologies
that are in current use.
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Results from Study
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Questions?
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