Performance of Service-Node-Based Mobile Prepaid Service

1. Performance of Service-Node-Based MobilePrepaid Service Ming-Feng Chang, Wei-Zu Yang, and Yi-Bing Lin, Senior Member, IEEE IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, MAY 2002

2. Outline • Introduction • Mobile Prepaid Service • Four billing technologies used in prepaid service • Service node approach • Analytic model • Numeric examples • Conclusion

3. Mobile Prepaid Service • Prospect of the mobile prepaid service • USA, 1998 grew 56% to 2 billion US dollars.will maintain a high growth rate to 2005 • Taiwan, FarEastone, more than 40% of their customer subscribed to prepaid service • What is the prepaid service • Prepaid card includes an directory number and the credit • Charge is decremented from the remaining prepaid credit • Whisper tone reminding this person to recharge • When zero balance, cannot originate calls, but may be allowed to receive phone calls for a period • top-up card(scratch card with a secret code inside it.) to recharge the prepaid credit

4. Mobile Prepaid Service • Customer’s point of view • immediate service without a long-term contract or regular bills • System provider’s point of view • reduces operation overhead • reduces the time of cost reclamation • Increases the capability of competition

5. Mobile Prepaid Service • Provider’s interest • Cost v.s. revenue • # of credit checks v.s. bed debt • Four billing technologies used in prepaid service • Hot billing approach • handset-based approach • intelligent network approach • service node approach

6. billing technologies(1/4) • Hot billing approach • uses call detail records (CDRs) produced by the wireless switch (i.e., mobile switching center) to process the prepaid usage • CDRs generated after call completionsan ,transported from the mobile switching center(MSC) to the prepaid service center. • cost effective ,not require major changes in the network infrastructure • One call exposure problem ,large loss, bed bedt

7. billing technologies(2/4) • handset-based approach • prepaid credit and balance information is stored in the SIM card of a handset.. • the MSC provides tariff parameters to the handset through the GSM phase 2 supplementary message AOC (advice of charge) • Handset modify the balance information in the SIM card • not incur major modification to the carrier’s infrastructure. • requires GSM phase-II-compliant handset • security is a serious problem, network needs to act as a backup to keep track of the prepaid credit usage.

8. billing technologies(3/4) • intelligent network approach • Complete solution for the prepaid service • service control(checking credit, timer…) ,not on MSCs, but on the prepaid service control point (P-SCP). • P-SCP contains service logic programs (SLPs) and associated data to provide IN services • prepaid call to MSC, MSC communicates with the P-SCP, P-SCP performs the service control and response message back to the MSC, MSC accept or reject the prepaid call • P-SCP is not on the voice path, low capacity expansion cost. • Investment on P-SCP and software modifications in all MCSs

9. billing technologies(4/4) • Service node approach(the paper’s focus) • most widely deployed prepaid solution today • viewed as a stepping-stone to the intelligent network approach • integrates the functions of the MSC and service control point (SCP) in a closed configuration • service node is on the voice path, capacity expansion cost is higher than the intelligent network approach. • No need of software modifications in MCSs

10. Service node approach service node activates a timer for charging and sets up a trunk back to the MSC 4 Negative credit !!! Terminate by the service node. IVR may be reminding 5 5 1 Call completes, credit updated 6 1 originates a prepaid call by dialing 5 5 2 routes the call to the service node 4 Route to destination 4 3 verify if the customer has sufficient credit

11. Service node approach • In real operation, a service node may process over 10 000 prepaid calls simultaneously. • service node need to permit a real-time credit monitoring and updating • the processing budget for a service node should be accurately planned in real world • “What is the credit checking frequency so that the sum of the credit checking cost and the bad debt is minimized?” • Min {checking cost + bad debt}

12. Analytic model • Assumption : a customer will consume all the prepaid credit before he/she gives up the prepaid service • E[N*ch] : expected number of credit checks • E[B*L] : expected bad debt • B : prepaid credit • K : number of calls • I : decremented amount periodically by service node during the conversation • Xi : the charge of the ith call, i = 1,2,…, k-1 • Xk : the charge of the last call if the service node would not terminate the call when the credit becomes negative • B*L : the loss of the service provider • BL : the corresponding value if the last call were allowed to complete • E[Nch] : expected number of credit checks assuming the total credit is B + B*L • E[nch] : the expected number of credit checks for a call • P : recharge probability

13. Analytic model • The last (i.e., the kth) call terminates in one of the two cases: • the last call is forced to terminate by the service node • the last call completes before the service node discovers that the credit becomes negative. E[N*ch] = E[Nch] - E[BL]/I = E[K]E[nch] - E[BL]/I

14. Analytic model • Derivation of E[N*ch] and E[B*L] , consider two cases for prepaid credit B • Fixed Credit Case

15. Analytic model • Recharged Credit Case

16. Numeric examples • the analytic results are consistent with the simulation results

17. A large Cx represents that there are more short calls and long calls. Numeric examples • Effects of the Variation of Call Charges • Cx : the coefficient of the variation of call charge For Cx < 5*10^-3, E[N*ch] and E[B*L] are sensitive to E[xi] But insensitive to Cx When Cx > 5, E[N*ch] increases sharply in both fixed credit And recharged credit cases---short call effect. billing policies to discourage short calls

18. Numeric examples • Effect of I onE[B*L]/I Intuition : E[B*L] = I/2, but wrong (Obs 1)Negative slope : as I increases, the probability that the Kth call terminates normally increases. Thus, the expected loss E[B*L] becomes smaller than I/2. (Obs 2) irregular pattern: When Cx is large, there are more short calls and long calls, the probability that the last call depletes all or most of the credit becomes large

19. Numeric examples • The Cost Function • C = E[B*L] + Ø E[N*ch] • Ø : the credit checking cost of the service node. • C : the net effect of credit checking cost and bad debt. Triangle:the cost for the optimal I Intuitive results: 1. as Ø increase, the value for the optimal I (triangle) increae 2. For the same Ø,as B increases, the value of optimal I increases.

20. Conclusion • study the service-node based approach • system architecture • The procedures for call origination. • An analytical model to analyze the performance • the fixed credit • the recharged credit cases. • Validate analytic results by simulation experiments • short call effect, suggest billing policies to discourage short calls • When Cx is high or I is large, E[B*L] = I/2 • A cost function was used to determine the minimal cost and the optimal checking interval I