Local Predictive Resource Reservation for Handoff in Multimedia Wireless IP Networks

1. Local Predictive Resource Reservation for Handoff in Multimedia Wireless IP Networks Tao Zhang, Eric van den Berg, Jasmine Chennikara, Prathima Agrawal, Jyh-Cheng Chen and Toshikazu Kodama IEEE Journal on Selected Areas in Communications, October 2001

2. Outline • Introduction. • The proposed methods. • Wiener-based method. • Time-Series-based method. • Numerical analysis. • Comparison with a Collaborative Method. • Conclusion. Walt Xie-Kuan Huang, MNET Lab

3. Introduction • This paper presents two methods that predict the future resource demand for the handoff using only local information. • The proposed methods that model the instantaneousresource directly are different from those modeling the factors that impact the demands. • The proposed methods allow new and handoff calls to: • Follow non-Poisson resource demands. • Have arbitrary per-call resource demands. • Have arbitrarily distributed call and channel holding times. Walt Xie-Kuan Huang, MNET Lab

4. Introduction • A wireless mobile call in progress could be forced to abort during handoff if it can’t be allocated sufficient resources in the new wireless cell. • From a user’s perspective, forced termination of an on-going call due to handoff is more undesirable than rejecting a new call. Therefore, it’s important to reserve resources to reduce the handoff call-dropping probability. • Existing methods for predicting and reserving resource can be classified into: • Collaborative methods. • Local methods. Walt Xie-Kuan Huang, MNET Lab

5. Introduction • Existing local methods assume that every call requires the same bandwidth, the call arrival process is Poisson, and the call holding time and a call’s channel holding time in each cell is exponentially distributed. • Existing methods are not suitable for multimedia wireless networks, especially wireless IP networks because: • The amount of bandwidth required to successfully handoff a call may vary arbitrarily over a wide range in a multimedia wireless network. • Wireless IP networks may often consist of a large number of picocells( handoff call arrivals may be non-Poisson and nonstationary). • MS’s velocities may vary widely that is difficult to collect real-time information for collaborative methods. Walt Xie-Kuan Huang, MNET Lab

6. Introduction • The limitations of existing methods seem to be caused primarily by a fundamental principle they share: they model the factors that impact the resource demands and then derive the demands from the model of the impacting factors. • However, there are a lot of factors that impact the resource demands in multimedia wireless networks. This makes the derivation of demand resource is more difficult, especially using only local information. • The proposed methods model the instantaneous resourcedemand directly. Walt Xie-Kuan Huang, MNET Lab

7. The proposed methods • The proposed methods have the following characteristics. • Localized Prediction. • Modeling Instantaneous Demands Directly. • Multimedia Resource Prediction and Reservation: Not only can the proposed methods predict the future resource demands of each individual service class, it can also predict the total amount of resource required for handoff calls of all service classes without having to predict the demand for each service class separately. • Simplicity. Walt Xie-Kuan Huang, MNET Lab

8. The proposed methods • Define R(t) as the resources required by handoff calls in a cell at time t as a stochastic process. • R(t) can represent the number of radio channels, the amount of radio system capacity, bandwidth or the number of IP addresses required by handoff calls of each individual service class or a combination of service classes at time t. • Wiener based methods use only the present value of R(t) for predicting the future values of R(t). • Future resource demand may also be correlated with pas resource demand changes. So, the time series method is introduced. Walt Xie-Kuan Huang, MNET Lab

9. Methods Based on Wiener Processes • Basic Wiener Process model: • ΔR = R(t) – R(t-Δt) = α(Δt)1/2 • α is a standard normal random variable. • Another Wiener Process model: • ΔR = μΔt+αδ(Δt)1/2 • μ and δare constant parameters and referred as expected drift rate and standard deviationrate of ΔR. • We can compute the estimator μ-hat and δ-hatby ΔR of the k time intervals (Eq-3 and Eq-4). • For example, we can sample the R(t) every 1 min and estimate/update μ and δevery 10 minby R(t) of past k min. Walt Xie-Kuan Huang, MNET Lab

10. Methods Based on Time Series • This paper use autoregressive moving average models (ARMA) to model ΔRt (=R(t)-R(t-1)). • An ARMA(p,q) process {Xt} is a stationary process that for each t satisfies: • Xt-Φ1Xt-1-…-ΦpXt-p = Zt+Θ1Zt-1+…ΘqZt-q • The {Zt} are uncorrelated random variables (noise). • We can choose the best order p by using Akaike Information Criterion (AIC) that is a statistical measure of goodness-of-fit of an ARMA(p,q) model. • Wiener Process is a special case of ARMA(p,q) that both p and q are equal to zero. Walt Xie-Kuan Huang, MNET Lab

11. Numerical Analysis (Wiener Based) • CDP<5% • Poisson arrival with λh=5. • Exponential channel holding time (mean) μh= 5 min. Walt Xie-Kuan Huang, MNET Lab

12. Numerical Analysis (Wiener Based) • CDP<5% • Poisson arrival with λh=5(first 50 min) and λh=2(50th~100th min). • Exponential channel holding time (mean) μh= 5 min. Walt Xie-Kuan Huang, MNET Lab

13. Numerical Analysis (Wiener Based) • CDP<5% • Inter-arrival process:AR(1) with Φ1=0.8. • Exponential channel holding process:AR(1) with Φ1=0.5. Walt Xie-Kuan Huang, MNET Lab

14. Numerical Analysis (Time Series) Walt Xie-Kuan Huang, MNET Lab

15. Numerical Analysis • The Wiener-based method and the ARIMA-based method appear to generate almost identical results. • Comparing the two methods, Wiener prediction over-reserves 12kb/s on average, while achieving the same worst case handoff call dropping probability. • The ARIMA method has the potential to improve on Wiener prediction. Walt Xie-Kuan Huang, MNET Lab

16. Comparison with a Collaborative Method • The key terms for comparing are: • How accurately each method can predict future resource demands • Handoff call dropping probability (CDP). • New call blocking probability. Walt Xie-Kuan Huang, MNET Lab

17. Comparison with a Collaborative Method • In Wiener Model, a new call will be accepted if • The number of free channels is larger than(equal to?) the number of handoff calls that are predicted to arrive during the remaining time of the current prediction interval. • The number of predicted available free channels at the end of the current prediction interval is larger than(equal to?) the number of predicted handoff calls. This rule helps to maintain low handoff CDP even when handoff call arrivals are clustered. Walt Xie-Kuan Huang, MNET Lab

18. Comparison with a Collaborative Method Walt Xie-Kuan Huang, MNET Lab

19. Comparison with a Collaborative Method Walt Xie-Kuan Huang, MNET Lab

20. Conclusion • The proposed methods have a more global view (resource directly) than existing methods that use several factors to derivate the demands. • The proposed methods allow new and handoff call arrivals to be non-Poisson and nonstationary, handoff calls to have arbitrary bandwidth requirements, and arbitrary channel and channel holding time distributions. • Capabilities above are critical in future wireless networks that support multimedia applications with varying bandwidth requirements. • In my opinion, the main contribution of this paper is providing a new viewpoint when we are predicting the resource demand of the handoff calls in multimedia wireless networks. Walt Xie-Kuan Huang, MNET Lab