UPTIME: an IMS-based mobility framework for next generation mobile networks

Abstract

Seamless inter-technology mobility is one of the fundamental requirements of next generation mobile networks. For seamless mobility, handover delay and packet loss should be minimized. However, existing solutions suffer from a number of shortcomings in satisfying these requirements: first, handover preparation schemes fail to minimize the handover delay as much as possible. Second, minimizing packet loss which is usually using soft handover (SHO) schemes are excessively wasteful of scarce resources. In this paper, we propose the uninterrupted proactive connection transfer for IMS mobility enhancement (UPTIME) mobility framework which achieves seamless mobility while minimizing excessive power and radio resource consumption. UPTIME incorporates two mechanisms; a proactive handover preparation method and an optimized SHO technique for handover execution. We demonstrate the benefits of the proposed framework through both analysis and simulation. Our simulation results for typical LTE/WiMAX handovers show that the handover preparation delay can be reduced by 70 %, and good packet loss performance can be achieved whilst saving 43 % of radio resources and 48 % of battery power.

Appendix: Proof for given formulae

In this appendix we prove formula (20). The probability of being in state $S_{ij}$ and using IF1 at time k + 1, denoted by $IF_{1}[k+1],$ can be found as:

$$\begin{aligned} Pr\left\{ IF_{1}[K+1],\, S[k+1]=S_{ij}\right\}&= \sum _{S_{mn}\in N}Pr\left\{ IF_{1}[K],S[k]=S_{mn}\right\} T_{S_{mn}S_{ij}}\nonumber \\&\quad +Pr\{S[k]=S_{41}\}T_{S_{41}S_{ij}}\nonumber \\&\quad +Pr\{S[k]=S_{42}\}T_{S_{42}S_{ij}} \end{aligned}$$

(28)

In other words, the probability of using IF1 being in state $S_{ij}$ can be found by noting (1) The MS continues using IF1 if no handover happens in the previous state, and (2) A handover to IF1 only happens in states $S_{41}\hbox { and }S_{42}.$

Equation (28) can be rewritten as:

$$\begin{aligned} Pr\left\{{\text {IF1}}[K+1]|S[k+1]=S_{ij}\}Pr\{S[k+1]=S_{ij}\right\}&= \sum _{S_{mn}\in N}[Pr\{{\text {IF1}}[K]|\, S[k]=S_{mn}\}\nonumber \\&\quad \times Pr\{S[k]=S_{mn}\}T_{S_{mn}S_{ij}}\nonumber \\&\quad +Pr\{S[k]=S_{41}\}T_{S_{41}S_{ij}}\nonumber \\&\quad +Pr\{S[k]=S_{42}\}T_{S_{42}S_{ij}} \end{aligned}$$

(29)

In the steady state condition we can write:

$$\begin{aligned} Pr\{{\text {IF1}}|\, S=S_{ij}\}Pr\{S=S_{ij}\}&= \sum _{S_{mn}\in N}Pr\{{\text {IF1}}|\, S=S_{mn}\}Pr\{S=S_{mn}\}T_{S_{mn}S_{ij}}\nonumber \\&\quad +Pr\{S=S_{41}\}T_{S_{41}S_{ij}}+Pr\{S=S_{42}\}T_{S_{42}S_{ij}} \end{aligned}$$

(30)

Using the definition of $\psi _{1}^{ij},$ we have:

$$\begin{aligned} \pi _{ij}\psi _{1}^{ij}&= \sum _{S_{mn}\in N}\pi _{mn}\psi _{1}^{mn}T_{S_{mn}S_{ij}}\nonumber \\&\quad + \frac{\pi _{41}}{\pi _{ij}}T_{S_{41}S_{ij}}+\frac{\pi _{42}}{\pi _{ij}}T_{S_{42}S_{ij}} \end{aligned}$$

(31)

Using same approach we can derive (21) for IF2.