showing the call dropping ratio and the bandwidth utilization. We compare the .... tion with variable mean arrival rates and that the holding time of each call is ...
A New Call Admission Control Scheme Using the User Conformity for Non-uniform Wireless Systems* Minhee Chot, Jae-Man Kim$, and Hyunsoo Yoont +Divisionof Computer Science Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology E-mail: {mhcho,hyoon} @calab.kaist.ac.kr $Core Network Research Lab. LG Electronics Inc. E-mail : j aeman @ 1ge.com Absfracf- Next generation wireless communication systems are expected to satisfy quality of service (QoS) requirements by preventing call droppings which are caused by user mobility. Call admission control (CAC) is becoming more important in guaranteeing the QoS of calls. Since existing CAC schemes estimate future requirements and reserve bandwidth to guarantee a certain level of QoS, the bandwidth estimation method is very important in CAC schemes. Most conventional schemes use the movement information of individual mobile terminals (MTs) to estimate the bandwidth requirements. While these schemes estimate the future bandwidth usage relatively well, it requires high computational overhead and lots of memory space to keep and update the movement information of each mobile terminal, and therefore is hard to implement. In this study, we propose a call admission control scheme, which estimates the future handoffs using the handoff histories between BSs and the MT corformify. The proposed scheme achieves low dropping ratio and high bandwidth utilization keeping low computational overhead. The performance of the proposed scheme is evaluated through simulations. Keywords-Call admission control, quality of service, wireless system
I. INTRODUCTION Next generation wireless communication systems are expected to satisfy quality of service (QoS) requirements by preventing call droppings which are caused by user mobility. Call admission control (CAC) is becoming more important in guaranteeing the QoS of calls since more handoffs and more call droppings will occur due to the smaller cell size and increased multimedia applications in future wireless systems. Most CAC schemes try to prevent call droppings by reserving channels which will serve handoff calls, and they reject call requests if channels cannot be reserved. Since existing CAC schemes estimate future requirements and reserve bandwidth to guarantee a certain level of QoS, the bandwidth estimation method is very important in CAC schemes. Most conventional schemes such as the Shadow Cluster [ 11 and the Most Likely Cluster [ 2 ] , etc. use the movement information of individual mobile terminals (MTs) to estimate the bandwidth requirements. While these schemes estimate the future bandwidth us*This work was supported by the Korea Science and Engineering Foundation (KOSEF)through the Advanced Information Technology Research Center (AITrc).
0-7803-7005-8/01/$10.00 0 2001 IEEE
age relatively well, they require high computational overhead and lots of memory space to keep and update the movement information of each mobile terminal, and therefore is hard to implement. Recently, [3] proposed Collective Handoff Probability (CHP)based call admission control strategy, which predicts future channel requests by using only handoff history data among base stations. This strategy assumes that some MTs have similar movement characteristics collectively, due to the objects surrounding the cells such as roads, buildings, or highways. This scheme has the merit that it does not need any complex method to predict each user’s mobility and therefore has low computational overhead. However, in real environments, while some users show the collectively similar pattern, there exist other users that do not conform to the collective pattern. In the CHP strategy, these non-conforming users cause false handoff prediction, and thus lead to unnecessary call droppings and bandwidth waste. In this study, we propose a call admission control scheme, which estimates the future handoffs using the information of user conformity. A user conformity reflects how strongly a user conforms to the collective handoff probabilities between BSs. Using this information, we can prevent unnecessary bandwidth reservation for non-conforming users and thus get the lower dropping ratio and higher bandwidth utilization. The proposed scheme has low computational overhead because it is simple to keep and update the value of user conformity for each user. The performance of the proposed scheme is evaluated by showing the call dropping ratio and the bandwidth utilization. We compare the performance with two other schemes, the LOCAL and the CHP scheme. In the LOCAL scheme, each base station locally adjusts the amount of reservations needed to achieve the target dropping ratio. The simulation results show that the proposed scheme achieves the lowest dropping ratio and the highest bandwidth utilization for all traffic load ranges. We also examine the effect of different methods for determining the value of user conformity.
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This paper is organized as follows. In Section 2, we describe the system model of the mobile system. In Section 3, we provide the simulation results to evaluate the performance of the proposed scheme. The conclusion is presented in Section 4. 11. SYSTEMMODEL (3)
A. User Conformity Concept In [ 11, the MT handoff probability was defined as the probability that an M T will leave the current cell to go into an adjacent cell, UJ, given that it enters the cell from a different adjacent cell, ‘U. Although in [ I] it is assumed that the movement information of specific MT, such as the velocity, direction and current location, can easily be obtained, it is known that such information is difficult to obtain in practice [4]. we propose a call admission control scheme, which estimates the future handoffs using the information of user conformity. A user Conformityreflects how strongly an M T conforms to the collective handoff probabilities between BSs. Using this information, we can prevent unnecessary bandwidth reservation for non-conforming MTs and thus get the lower dropping ratio and higher bandwidth utilization. The equations (1)-(3) show our handoff probability estimation, by which we estimate future bandwidth and perform call admission control. Pi,j in equation (1) is defined as the probability that an M T entering the current cell c from the adjacent cell i will leave the current cell to go into the adjacent cell j . P0,j is the CHP for the MTs which originate in the current cell and handoff to cell j . We also define the handoff history, H i , j , as the number of MTs which have entered the current cell from cell i and left the current cell for cell j during a time interval [T,,, - Th, Tcu,].T,,, is the current time and Th is the history collecting duration. The future P,,j is estimated from Hi,j in equation (I).
where is the previous value of Pi,j, N is the number of neighboring cells, and a and p are constants in [0,1]. The user conformity fk and the collective handoff probability Pi,j are used for calculating Q i , j ( k ) in equation (2). The more likely the MT k conforms to the collective handoff pattern, the larger value f k has. P i , j ( k ) ,the normalized value of Qi,j ( k ) in equation (3) is the handoff probability actually used for M T k . Pi,j( k ) represents the probability that an M T k entering the current cell c from the adjacent cell i will leave the current cell to go into the adjacent cell j .
f k , the user conformity of MT k , is updated whenever M T IC handoffs We propose two methods for updating the user conformity.
A.0.a User conformity update method I. In method I, f k is updated as equation (4), where fk is the previous value of f k and d is the incremenddecrement for fk as defined in equation ( 5 ) . If the handoff direction of M T k conforms to the collective handoff pattern, its conformity 6 increases by d. Otherwise, its conformity decreases by d. d has different values according to the level of conformity.
fk =
d=
{
{
+
min( f; d , 1) in the conforming case, m a ( & - d, 0) in the non-conforming case
81 if Ri,j 2 el (strongly conform) 6 2 if 1 6 Ri,j < 01 (weakly conform) 82 if 02 6 Ri,j < 1 (weakly unconform) 61 if Ri,j < 02 (strongly unconform)
The level of conformity is determined using the value of Ri,j in equation (6). 1/N is the probability that an M T entering the current cell from the adjacent cell i will handoff to the adjacent cell j when all the handoffs between cells occur with the same probability. Ri,j represents how high the collective handoff tendency is. Ri,j 2 1represents that the probability of handoff to cell j is larger than the average, while Ri,j < 1 means the tendency to handoffs toward cell j is lower than the average. Assume the M T k entered the current cell c from the adjacent cell i. When it moved to the adjacent cell j where Ri,j 2 1, the MT is said to conform to the collective handoff pattern and f k is increased. On the contrary, f k is decreased when the M T k moved to cell j with Ri,j < 1. Equation (5) shows four conformity levels, where el and 02 (0, > 1 > 02 > 0) are used as thresholds to determine the conformity levels. 81 and 6 2 (61 > 62 > 0) are incremenMdecrernents for strongly and weakly conforming/unconforming case, respectively.
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// Pseudo code for new call admission
I 2 3 4 5 6 7
NewCallAdmit(B, z) begin if B BE(t0) > BT then return false for dl t E [to, t o Tmaz] begin if B ;t BE ( t ) BS(t) > BT then If
+
+
+
to-1 Ck=tO-H
to-1
8 9
IO II 12 13
14 15 16
Cs(k)+pE(t)
ci(k)fC:=tO
Ck=tO-H
> PQoS
then return false end
+
+
t i = t o Tmin,t 2 = t o 2Tmax for each cell j E N C begin if CheckNeighborBandwidth(c, t i , t z , B , p0,j (z), 1,z) = false then return false end return true end NewCallAdmit
// Pseudo code to check the bandwidth of neighboring cells
I 2 3 4
5 6 7 8
CheckNeighborBandwidth(c, t l , t 2 , B , p , s t e p , z) begin if BL ( t ) B > BT for any t E [ t l ,t z ] then return false for all t E [ t l ,t z ] begin if B B i ( t ) B: ( t ) > BT then if
+
+
+
to-1
C k = t O - H ci(k)+P to-1
Ck=tO-H
9 10 I1 12 13 14
15
> PQoS c~(k))+c:=to P;(')+P
then return false end
B!(t)+ = p . B f o r a l l t E [ t l , t 2 ] = tl Tmin,t 4 = tl 2Trnax if s t e p < M A X S T E P and CheckNeighborBandwidth(j, t3
+
+
t 3 , t4.
B , ~ ! , ~ .(p z, s)t e p
+I
z ) =true for all
k E Nj then
return true end CheckNeighborBandwidth Fig. 1. A detailed pseudo code for call admission control
A.0.b User conformity update method 11. In method 11, fk is updated as equation (7), where f; is the previous value of fk and T is the multiplier for updating f k as defined in equation (8). If the handoff direction of M T k conforms to the collective handoff pattern, its conformity fk increases by T . Otherwise, its conformity decreases by T . T has different values according to the level of conformity as equation (8), where y1 > 7 2 > 1 > 7 3 > 7 4 and 41 > 1 > 4 2 > 0.
B. CallAdmission Control When there is a new call request with a bandwidth requirement B in cell e, the proposed scheme entails the following steps: Step 1: Step 1 tests whether admitting this new call will still maintain the QoS of the current cell. Step 2-1: Step 2-1 tests whether admitting this new call will still maintain the QoS of the neighboring cells. N C is the set of neighboring cells of cell c. Then, if the neighboring cells are estimated to be able to maintain their QoS, they reserve bandwidth up to P ; , ~ ( X ) . B, where & ( E ) is the probability that M T z originating in the current cell c will handoff to cell j.
71 T-(
72
73 74
if Ri,j 3 41 (strongly conform) if 1 Ri,j < 41 (weakly conform) if 4 2 6 Ri,j < 1 (weaklyunconform) if Ri,j < 4 2 (strongly unconform)
