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 RYERSON UNIVERSITY

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 RYERSON UNIVERSITY 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. RYERSON UNIVERSITY 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. RYERSON UNIVERSITY 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. RYERSON UNIVERSITY 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 RYERSON UNIVERSITY Proposed Methodology

The network layer end-to-end performance for each AP i is determined by (1): Then, transport-layer performance is derived (2): RYERSON UNIVERSITY Proposed Methodology [2]

To obtain an overall measure of the fitness of AP i to meet the users needs: Derives to: RYERSON UNIVERSITY 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 RYERSON UNIVERSITY 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 RYERSON UNIVERSITY 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 RYERSON UNIVERSITY 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 RYERSON UNIVERSITY 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 RYERSON UNIVERSITY Future Work

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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 technologies that are in current use. RYERSON UNIVERSITY Proposed Conclusion

Results from Study RYERSON UNIVERSITY

Questions?