COGNITIVE NETWORK ACCESS USING FUZZY DECISION MAKING
Nicola Baldo and Michele ZorziDepartment of Information Engineering –
University of Padova, Italy
Presented By: Andrew D’SouzaPetar Kramaric,Srdjan Lakovic
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• 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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Topic Problem:
• Several schemes have been put into practice:– 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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Previous Implementations
• 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
• 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 Implementation [2]
• 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
• 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 [2]
• To obtain an overall measure of the fitness of AP i to meet the users needs:
• Derives to:
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Proposed Methodology [3]
• Step 1:– Collect fuzzy performance metrics– Throughput, Delay and Reliability for radio link, core network, end-to-
end, 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
• 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 [2]
• 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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Algorithm [3]
• 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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Implementation
• 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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Future Work
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LA
• 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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Proposed Conclusion
Results from Study
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Questions?
