1. CAC for Multimedia Services in Mobile Cellular Networks ： A Markov Decision Approach Speaker ： Xu Jia-Hao Advisor ： Ke Kai-Wei Date ： 2004 / 11 / 18

2. Outline Introduction System Model Description SMDP Approach in Our CAC Numerical Results Conclusion

3. Outline Introduction System Model Description SMDP Approach in Our CAC Numerical Results Conclusion

4. Introduction There is a growing interest in deploying multimedia services in mobile cellular networks. Call Admission Control (CAC) is a key factor in Quality of Service (QoS) provisioning for these services. We model a one-dimensional cellular network and describe how to find out optimal admission decisions.

5. Problems For mobile multimedia services, the existing MCN (mobile cellular network) for voice- oriented services, needs to be adapted in numerous aspects. The connection-level QoS in MCNs is usually expressed in terms of call blocking probability and call dropping probability (handoff). Multimedia calls belong to multiple and different types of class => multiclass calls

6. Typical CAC policies -- Coordinate- Convex policy Complete Sharing ( CS ) ： - Every class share the bandwidth pool. Complete Partitioning ( CP ) ： - Bandwidth for each class is exclusively reserved. Threshold ： - A newly arriving call is blocked if the number of calls is >= a predefined threshold.

7. Another Solution The coordinate-convex policy boasts of easy tractability. But in certain cases, it turns out strictly suboptimal. CAC using semi-Markov Decision Process (SMDP) can maximize the revenue for multi- class networks. We can use linear programming (LP) formulation to find out optimal decisions.

8. Outline Introduction System Model Description SMDP Approach in Our CAC Numerical Results Conclusion

9. Our System Model The cellular system under consideration is one-dimensional, which is deployed in streets and highways. Our system consists of N cells and we consider a general model of multiclass calls with mobility characteristics.

10. Notation ： Call requests of class-i in cell-n, a Possion distribution with mean arrival rate. ： The call holding time of a class-i call is assumed to follow an exponential distribution with mean. ： The number of channels required to accommodate the call of class-i. ： The rate of class-i call that handoff to our system from outside. (n = 1 or N) ： For each on-going class-i call, revenue rate.

11. Notation ( cont. ) The cell residence time (CRT), independent of class ： - The amount of time that an MT (mobile terminal) stays in a cell before handoff, is assumed to follow an exponential distribution with mean (the parameter represent the handoff rate). The rate that a call in a given cell will handoff to one of its adjacent cells is. The total bandwidth in each cell is the same and denoted by C, assuming a fixed channel allocation.

12. Notation ( cont. ) The current state of our cellular system ： denotes the number of class-i calls in cell-n All possible states ： For each state x, a CAC policy should find out an ”accept / reject” decision for all kinds of traffic.

13. Traffic Model in Our Cellular System

14. Outline Introduction System Model Description SMDP Approach in Our CAC Numerical Results Conclusion

15. SMDP Introduction The original SMDP model consider a dynamic system which, at random points in time, is observed and classified into one of several possible states. After observing the state, a decision has to be made and the corresponding revenue for each state is gained.

16. SMDP in Here For each state x, a set of actions is available. This controlled dynamic system is called an SMDP when the following Markovian properties are satisfied ： If at a decision epoch the action a is chosen in state x, then the time until, and the state at, the next decision epoch depends only on the present state x.

17. Linear programming ( LP ) It has an advantage that additional constraints can be easily incorporated. It can guarantee the upper bound of the handoff dropping probability. We use it to solve the SMDP-formulated CAC problem in our cellular system, which aims at both maximum revenue and QoS guarantee.

18. LP in MATLAB ”linprog” function

19. LP Example

20. SMDP Description The decision epoch ： s = ( x, e ), The action space B ：

21. SMDP Description ( cont. ) The action space is actually a state dependent subset of B ： The expected time until a new state is entered ：

22. SMDP Description ( cont. ) : Transition probability ： The total revenue rate for the cell ：

23. LP Formulation The LP associated with SMDP ： ： the long-run fraction of decision epochs at which the system is in state x and action a is taken

24. Optional Constraint We also need to consider the QoS requirements: - the upper bound of the handoff dropping probability. Let denote the maximum tolerable handoff dropping probability of a class-i call. - external handoff from outside and internal handoff between cells in our system.

25. Optional Constraint ( cont. ) From outside ： Internal ：

26. Outline Introduction System Model Description SMDP Approach in Our CAC Numerical Results Conclusion

27. Simulation Simulate one-cell model (N = 1) and two-cell model (N = 2). Compare our SMDP CAC with the upper limit (UL) CAC policy that has a threshold for a class-i call originating in a cell. ( threshold [2,1] ) C = 5 ; K = 2 ; (b1,b2) = (1,2) ; (D1,D2) = (0.02,0.04)

28. Utilization vs. Erlang Load (N=1)

29. Utilization vs. Erlang Load (N=2)

30. Handoff Dropping Probability from the outside vs. Erlang Load (N = 1)

31. Handoff Dropping Probability from outside vs. Erlang Load (N = 2)

32. Handoff Dropping Probability between Cells vs. Erlang Load (N = 2)

33. Revenue Ratio vs. Erlang Load

34. Outline Introduction System Model Description SMDP Approach in Our CAC Numerical Results Conclusion

35. Optimal CAC is essential for the efficient utilization of scarce radio bandwidth. By using SMDP, we can maximize the revenue while satisfying the QoS requirements.

36. Reference Call Admission Control for Multimedia Services in Mobile Cellular Networks: A Markov Decision Approach--Jihyuk Choi; Taekyoung Kwon; Yanghee Choi; Naghshineh, M.; Computers and Communications, 2000. Proceedings. ISCC 2000. Fifth IEEE Symposium on, 3-6 July 2000 Keith W. Ross and Danny H. K. Tsang, “Optimal Circuit Access Policies in an ISDN Environment: A Markov Decision Approach,” IEEE Transactions on Communications, Subir K. Biswas and Bhaskar Sengupta, “Call Admissibility for Multirate Traffic in Wireless ATM Networks,” INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE, Volume: 2, 7-11 April 1997 Pages:649 - 657 vol.2