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[3] Jorge Martinez-Bauset, David Garcia-Roger, Ma Jose Domenech-

Benlloch and Vicent Pla, “Maximizing the capacity of mobile cellular networks with heterogeneous traffic,” Elsevier Computer Networks, vol. 53, 2009, pp.973–988.

[1] Y. Zhang and D. Liu, “An adaptive algorithm for call admission control in wireless networks,” in: Proceedings of the IEEE Global Communications Conference (GLOBECOM), 2001, pp. 3628–3632.

[2] X.-P. Wang, J.-L. Zheng, W. Zeng, G.-D. Zhang, “A probability-based adaptive algorithm for call admission control in wireless network,” in: Proceedings of the International Conference on Computer Networks and Mobile Computing (ICCNMC), 2003, pp. 197–204.

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Introduction Introduction • Call admission control (CAC) schemes are critical to the

success of wireless networks.– the decision making part with the objectives of providing

services to users with guaranteed quality– achieving as much as possible resource utilization

• the forced termination probability– in mobile networks, it is related to the blocking probability of

handover requests

• The new and handover call blocking probability is major QoS parameters– Handoff calls should be considered to have higher priority than

new call arrivals

IntroductionIntroduction (cont’d) (cont’d)

• optimization techniques – such as linear programming, – to optimize certain QoS measures

• e.g., to minimize the call blocking probabilities.

• call admission policies through resource allocation– based on certain estimates or measurements of channel

characteristics • such as traffic rates, signal-to-interference ratios, resource

requirements, and overload probabilities

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An Adaptive CAC Algorithm An Adaptive CAC Algorithm [1][1]• C: the total number of available channels

• CH: reserved for handling handoff calls

• CA: used for handling admitted calls.

– CA = C - CH

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• Adjusting CH

• By choosing αu < 1, our algorithm will most likely keep the handoff call blocking rate below TH

• by waiting for N consecutive handoff calls before increasing the number of guard channels, the system performance is kept from oscillating

• If TH is small, τ should be large– For more accurate DH/H

• In the simulation, τ = 2 hr. (7200 sec.) is used

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• Simulation– C = 50– the new (handoff) call arrivals are modeled by a Poisson process with

mean λ (γ), λ/γ = 5– Channel holding times of both types of calls follow an exponential

distribution with mean 1/ μ (=180 sec.)– TH = 0.01

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the percentage of decrease in the blocking rate of handoff calls is greater than the percentage of increase in the blocking rate of new calls

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A Probability-based Adaptive A Probability-based Adaptive Algorithm for CAC [2]Algorithm for CAC [2]• In [1]

– too many parameters to set, • namely αu, αd, τ and N

• these parameters must be set before running, and cannot be modified in the process.

– takes a long time to achieve the steady state

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Solution 1 is quite unsteadythe threshold (Ch) is adjusted frequently.

Compared to [1], as for solution 1, HBP (handover-call blocking probability) increases greatly, while System Utilization decreases little. ?

Solution 1

0.2

0.2

0.8

0.8

TH

ru

rd

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Solution 2

0.2

0.8

TH

ru

rd

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Adaptive AC scheme [3]Adaptive AC scheme [3]• Applications expected to produce the bulk of traffic in the

multiservice Internet can be broadly categorized as streaming or elastic

• it seems natural to give priority to streaming traffic and leave elastic traffic use the remaining capacity

• If the total traffic demand of elastic flows exceeds the available capacity, some flows might be aborted due to impatience. – human impatience or– TCP or higher layer protocols interpret.

• Abandonments are useful to cope with overload and serve to stabilize the system but has a negative impact on the efficiency– capacity is wasted by non-completed flows

• AC should also be enforced for elastic traffic.

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• The adaptive scheme can be perceived as composed of one individual adaptive scheme per arrival class.

• When one of the arrival classes si is suffering from congestion, the adaptive schemes of lower-priority classes become under control of the adaptive scheme of si.

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• R different streaming services– 2R arrival classes (new + handover) si, 1 <= i <= 2R– priority: s1 > s2 > … > s1+R > s2+R

• ci : the amount of resource units that one quest of si requires– cr = cr+R, 1 <= r <= R

• Bi : the QoS objective (target blocking probability) of si

– Bi = bi/oi, ex. If Bi = 0.01, then bi = 1 and oi = 100

• li : the amount of resources that si has access to. (= CH)

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16[35] R. Ramjee, R. Nagarajan, D. Towsley, On optimal call admission control in cellular networks, Wireless Networks Journal 3 (1) (1997) pp.29–41.

6 12 18 24 30 36 42

1) minimizing the new call blocking probability with a hard constraint on the handoff call blocking probability2) minimizing the number of guard channels with hard constraints on both of the blocking probabilities

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conclusion and commentsconclusion and comments• Three adaptive admission control algorithm are introduced.

– Find the balance between new and handover call blocking probabilities, while maximizing the system utilization and user satisfaction.

• The convergence time should be taken into consideration.– At system initialization and when traffic characteristic has changed.

• Multiple thresholds of guard channel and target blocking probabilities for multiple service classes should be considered.

• The MR should adopt Admission Control (monitoring the resource in car) to satisfy the passengers, and should request for more resource at some utilization levels.

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