CAC for Multimedia Services in Mobile Cellular Networks ： A Markov Decision ApproachSpeaker ： Xu Jia-HaoAdvisor ： Ke Kai-WeiDate ： 2004 / 11 / 18

Outline

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

Outline

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

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.

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

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.

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.

Outline

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

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.

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. 　

,n i

1i

ib

ir

,n ih

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.

1

2

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.

,n ix

Traffic Model in Our Cellular System

Outline

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

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.

SMDP in Here

For each state x, a set of actions is available. This controlled dynamic system is called an S

MDP 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.

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.

LP in MATLAB

”linprog” function

LP Example

SMDP Description

The decision epoch ： s = ( x , e ) ,

The action space B ：

, 0,1n ia

SMDP Description ( cont. )

The action space is actually a state dependent subset of B ：

The expected time until a new state is entered ：

SMDP Description ( cont. ) :

Transition probability ：

The total revenue rate for the cell ：

xayP

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 takenxaz

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.

iD

Optional Constraint ( cont. )

From outside ：

Internal ：

Outline

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

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)

it

Utilization vs. Erlang Load (N=1)

Utilization vs. Erlang Load (N=2)

Handoff Dropping Probability fromthe outside vs. Erlang Load (N = 1)

Handoff Dropping Probability fromoutside vs. Erlang Load (N = 2)

Handoff Dropping Probability betweenCells vs. Erlang Load (N = 2)

Revenue Ratio vs. Erlang Load

Outline

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

Conclusion

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.

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