An Optimal Distributed Call Admission control for Adaptive Multimedia in Wireless/Mobile Networks Reporter: 電機所 鄭志川

Outline Abstract Introduction Adaptive multimedia and the Idea of CAC-BRA scheme Modeling CAC-BRA Linear programming and Simplex method Numerical results

Abstract There is a great demand on multimedia application with Quality of Service (QoS) in wireless/mobile networks. propose an optimal call admission control framework with bandwidth reallocation algorithm (CAC-BRA). We adopt semi-Markov Decision Process (SMDP) approach to model call admission control and bandwidth reallocation algorithm at the same time. Simplex method in linear programming is used to solve the optimal decision problem.

Introduction Non-adaptive situation Adaptive situation, where the bandwidth of an ongoing call is time varying during its lifetime. References give only a sub-optimal or near optimal solution, but we adopt semi- Markov decision process approach (SMDP), which gives us an optimal solution.

Adaptive multimedia and the Idea of CAC-BRA scheme The layered coding approach In order to simplify the problem, we assume only one class of users. Any customer uses one bandwidth among {b 1,b 2,…,b K } where b j <b j+1 for j = 1,2,…,K-1, If K=1, we have a non- layered coding traffic. Example: For a video stream coded with H.263, MPEG-2 I frames, and MPEG-2 whole (I,B and P) frames. Here K=3, b 1 :H.263, b 2: MPEG-2 I frames, b 3 :MPEG-2 whole frames.

Adaptive multimedia and the Idea of CAC-BRA scheme The idea of CAC-BRA CAC-BRA decides on not only whether an arrival will be accepted or not, but also which call will be changed to how much bandwidth. We don’t allow the forced-termination of existing calls in the cell. The arrival calls, new arrival calls and handoff arrival calls, could be blocked. Our goal is to maximize the revenue, and at the same time to satisfy the QoS parameters, which are an upper bounds on handoff blocking probabilities.

Modeling CAC-BRA Consider a dynamic system 1.Decision Epoch 2.State Space 3.Active Space 4.Reward Function 5.Transition Probability Linear Programming Maximum Revenue And QoS guarantee Uniformization In MDP Discrete time + Constraints

CAC-BRA :the new call arrival rate for customers (poisson distribution) :the handoff call arrival rate for customers h :the rate of handoff to other cells µ :the service rate for customers (exponential distribution)

CAC-BRA The state of the system is X=(x 1,x 2, …,x K ) Where x i stands for the number of users who is using bandwidth b i in the system for all 1 ≤ i ≤ K Decision Epoch V=(X, a) where X is the current state, and the variable a represents both the event type and the action.

CAC-BRA The State Space The Action New call Handoff call The action of bandwidth reallocation if it is a new call Arrival and the arrival is accepted to have bandwidth b f1 Handoff call accepted to have bandwidth b f2 Departure from the cells with b g

CAC-BRA New call or handoff arrival Departure from the cells

CAC-BRA The New State

CAC-BRA The New State

CAC-BRA The Action Space are defined and Constrained by (1)-(15); The expected sojourn time :the new call arrival rate for customers (poisson distribution) :the handoff call arrival rate for customers h :the rate of handoff to other cells µ :the service rate for customers (exponential distribution)

CAC-BRA The Transition Probability The Revenue Rate

Linear programming and Simplex method Constrain New state Dropping Probability

Numerical results Experimental parameters ValuesExperimental parameters Values C301/µ500sec K21/h200sec {b 1,b 2 }{10,20}P BH 1%

Numerical results

Conclusion and future work SMDP Mathematics Matlab