Introduction

1. [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. [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.

2. 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

3. Introduction (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

4. An Adaptive CAC Algorithm [1] • C: the total number of available channels • CH: reserved for handling handoff calls • CA: used for handling admitted calls. • CA = C - CH

5. 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

6. 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 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

8. A Probability-based Adaptive 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

9. Solution 1 0.2 rd 0.8 ru Solution 1 is quite unsteady the threshold (Ch) is adjusted frequently. Compared to [1], as for solution 1, HBP (handover-call blocking probability) increases greatly, while System Utilization decreases little. ? TH 0.8 0.2

10. Solution 2 0.8 rd ru 0.2 TH

12. 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.

13. 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.

14. 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)

16. 6 12 18 24 30 36 42 1) minimizing the new call blocking probability with a hard constraint on the handoff call blocking probability 2) minimizing the number of guard channels with hard constraints on both of the blocking probabilities [35] R. Ramjee, R. Nagarajan, D. Towsley, On optimal call admission control in cellular networks, Wireless Networks Journal 3 (1) (1997) pp.29–41.

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