Jihyuk Choi; Taekyoung Kwon; Yanghee Choi; Naghshineh, M,

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 Page(s):594 - 599

Outline • Introduction • Model Description • SMDP Approach in Our CAC • Numerical Results • Conclusion

Introduction • There is a growing interest in deploying multimedia services in mobile cellular networks (MCN) • Call admission control(CAC) is a key factor in quality of service(QoS) provisioning for these services • Connection-level QoS in MCN is expressed in terms of • Call blocking probability • Call dropping probability: is handoff dropping probability • The goal of this paper is to find out optimal CAC for maximize the revenue • semi-Markov decision process is employed to model the cellular system

Model Description • We model a one-dimensional cellular network and describe how to find out optimal admission decisions • Suppose that there are Kclasses of calls in an MCN (mobile cellular networks) • Call requests of class-i(i = 1,2, ..., K) in cell-n (n =1,2, ..., N) are assumed to form a Poisson process with mean arrival rate λn,i

Model Description (cont’d) • The call holding time (CHT) of a class-i call is assumed to follow an exponential distribution with mean l/μi • The rate of class-i calls that depart from a cell due to service completion is denoted by μi • The number of channels required to accommodate the call, is denoted by bi • The revenue for each on-going class-i call is accrued at rate ri

Model Description (cont’d) • The following simple model is a mobile terminal (MT) moves through the whole cellular system • The cell residence time (CRT), i.e., the amount of time that an MT stays in a cell before handoff, with mean l/η • ηrepresentsthe handoff rate

Model Description (cont’d) • In our 1-D cellularnetwork, the probability that an MT will handoff to one ofits adjacent cells is 0.5 • The rate that a call in a given cell will handoff to one of its adjacent cells is η /2 • The total bandwidth in each cell is the same and denoted by C • The rate of class-i calls that handoff to our system from outside is denoted by hn,i(n = 1 or N)

Model Description (cont’d) • The current state of our cellular system is represented by the vector: • where xn,idenotes the number of class-i calls in cell-n • The set Λof all possible states is given by

SMDP Approach in Our CAC • The original semi-Markov decision process (SMDP) model considers a dynamic system • It is observed and classified into one of several possible states at random points in time • The SMDP state of the system at a decision epoch is given by the vector s = (x, e)

SMDP Approach in Our CAC (cont’d) • The variable e represents the event type of an arrival and is given by • When i <= K • the an,i (an,i {0, 1})denotes the origination of a class-i call within the cell-n • When i >=K+1 • it denotes the arrival (event) of a class-i call due to handoff from adjacent cells

SMDP Approach in Our CAC (cont’d) • The action spaceBcan be expressed by • For example, when N = 2, K = 2 and • The action space is actually a state dependent subset of Bdenoted by en,iis a vector of zeros, except for an one in the (n*(k-1)+i)-th position

SMDP Approach in Our CAC (cont’d) • If the system is in state xΛand the action a Bxis chosen • The expected time (sojourn time), (x, a), until a new state is entered is given by

SMDP Approach in Our CAC (cont’d) • The transition probability Pxayfrom the state x to any next state y Λwith action a takes one of the expressions in Table 1

SMDP Approach in Our CAC (cont’d) • Let r(X, a)be the revenue rate when the cell is in state x and action a has been chosen • If ri is the revenue rate of class-i call, then the total revenue rate for the cell is calculated by

SMDP Approach in Our CAC (cont’d) • The decision variable zxa, represents the system is in state x and action a is taken

Numerical Results • For numerical results, we simulated one-cell model (N = 1) and two-cell model (N = 2) • We compare our SMDP CAC with the upper limit (UL) CAC policy that has a threshold ti for a class-i call originating in a cell • The UL policy with threshold (2,l) blocks a new class-1 call originating in a cell if there are already at least two class-1calls in the cell

Numerical Results (cont’d) • We let C = 5, K = 2, b1 = 1, b2 = 2 , D1 = 0.02 and D2=0.04 • Simulations are carried out as the Erlang load (λn,i/ μi) of every class increases

Numerical Results (cont’d)

Jihyuk Choi; Taekyoung Kwon; Yanghee Choi; Naghshineh, M,