Dynamic Network Selection using Kernels

1. Dynamic Network Selection using Kernels ICC 2007 E. V. D. Berg, P. Gopalakrishnam,B. Kim and B. Lyles Telcordia Technologies, Inc. [Bellcore] W.-I. Kim, Y. S. Shin and Y. J. K ETRI

2. Outline • Introduction • Preliminaries • Statistical Learning (SVM) • Vertical Handoff Algorithm • Experiments • Conclusion

3. Introduction • The vision of the 4G network is to integrate different access technologies for ubiquitous access services. • Mobile devices have multiple interfaces. • Mobile devices can access the best network at anytime and anywhere

4. Introduction (cont.) • Always Best Connected (ABC) • Application and user-dependent • Dynamic fusion of multiple attributes • Traditional RSS threshold based are not able to adapt to • Multiple criteria • Dynamic user preferences • Changing network availability

5. Introduction (cont.) • Several methods have been proposed • Cost function • Utility function • Fixed Weighting of different metrics • Manual configuration

6. Preliminaries • Prediction Horizon • Minimum time period for HF • Stability period= make-up time + handover latency= • Similar to Dwell-timer • U1>U2 for a period greater than stability period • Handoff to network1

7. Preliminaries (cont.) • Utility function • Map values of metrics and measurements to attribute preference values. • EX: AHP, Bayesian Network

8. Preliminaries (cont.) • Utility function • A linear combination of individual, single-attribute utility functions for the attributes: • Availability -> RSS • Quality -> packet delay • Cost ->Monetary cost, Energy cost

9. Utility function • Availability • Utility function UA(t) • Expected Utility

10. Utility function • Quality • Utility function • Expected Utility Simulation

11. Utility function • Cost • Utility function • Expected Utility Simulation 時,不考慮 Energy cost

12. Overall Utility function • Overall Utility function • Overall Expected Utility

13. Statistical Learning (SVM) • [SVM運算] • Model: • Goal: Learning a optimal handover decision • We have a sequence of examples • (measurement vector, utility outcome) • (x1, y1), … (xn, yn), xi in X and yi in Y. • We want to learn the decision HF or not HF, then yi in Y = {-1, 1} < SVM Model>

14. Statistical Learning (SVM) • Model (cont.) • Updating: stochastic gradient descent • The true gradient is approximated by the gradient of the cost function only evaluated on a single training example. • By ε-insensitive loss • Special Case: • Least Mean Squares (LMS) adaptive filter • Back-propagation algorithm

15. Statistical Learning (SVM) • Utility kernel

16. Vertical Handoff Algorithm • MN connects to a network • MN collects information from each of the Nt reachable networks, to learning • RSS, • Delay, • Cost, • Power consumption • Handoff delay

17. Vertical Handoff Algorithm (cont.) • Update / learn the current expected utility for each of the network i, i=1…N. • Estimate the utilities using a separate kernel regression fti • Average handover latency Ti • Handover cost γi • If handoff to network i, otherwisestay connected to current network Measurement vector Go to step 1 Mapping function, 將xt對應到某個U

18. Experiments • Network Type • 2 WLAN, 1 3G, 1 WiMAX • Utility function • RSS • Delay • Fixed monetary • Fading model • Simple path loss model • Availability • QoS • Cost

19. Experiments (cont.) • Handoff algorithm determines the network to be used at any given time. • Handoff delays are simulated • By OPNET

20. Experiments Result (cont.) • Average Achieved Utility • c1=0.6, c2=0.3, c3=0.1 • Scenario#3: closed to WiMAX AP (higher delay) • RSS -> WiMAX, E2E delay 1.24 sec • Utility -> WLAN, E2E delay 0.37 sec

21. Experiments Result (cont.) • Learning when user preference changed • User decides to give higher preference to cost than delay. • ISP Configured Cost: • 3G, WiMAX > WLAN • Initial preference on mobile device: • Delay

22. Experiments Result (cont.) Learning when user preference changed Weight reversed

23. Experiments Result (cont.) • User/Application requirement • Network Delay: • WiMAX/3G > WLAN • Network Cost: • WiMAX/3G < WLAN • Utility Function • 50% for Availability (delay) [fixed] • 50% for cost and QoS [by priority]

24. Experiments Result (cont.) • User/Application requirement

25. Conclusion • This paper proposed a dynamic network selection based on combination of multi-attribute utility theory, kernel learning and stochastic gradient descent. • The algorithm can learn utilities dynamically and select networks efficiently.