In recent years, there has been a rapid increase in wireless network deployment and mobile device market penetration. With vigorous research that promises higher data rates, future wireless networks will likely become an integral part of the global communication infrastructure. Ultimately, wireless users will demand the same reliable service as today's wire-line telecommuni?cations and data networks. However, there are some unique problems in cellular networks that challenge their service reliability. In addition to problems introduced by fading, user mobility places stringent requirements on network resources. Whenever an active mobile terminal (MT) moves from one cell to another, the call needs to be handed off to the new base station (US), and network resources must be reallocat?ed. Resource demands could fluctuate abruptly due to the movement of high data rate users. Quality of service (QoS) degradation or even forced termination may occur when there are insufficient resources to accommodate these handoffs.
If the system has prior knowledge of the exact trajectory of every MT, it could take appropriate steps to reserve resources so that QoS may be guaranteed during the MT's connection lifetime. However, such an ideal scenario is very unlikely to occur in real life. Instead, much of the work on resource reservation has adopted a predictive approach. One approach uses pattern matching techniques and a self-adaptive extended Kalman filter for next-cell prediction based on cell sequence observations, signal strength measurements, and cell geometry assumptions. Another approach proposes the concept of a shadow cluster: a set of BSs to which an MT is likely to attach in the near future. The scheme estimates the probability of each MT being in any cell within the shadow cluster for future time intervals, based on knowl?edge about individual MTs' dynamics and call holding patterns.
In the United States, the FCC recently man?dated that cellular service providers must be able to pinpoint a wireless emergency call's originat?ing location to within 125 m. This has spurred intensive research in mobile tracking techniques. One promising approach is the integration of a global positioning system (GPS) receiver in each MT. It is very reasonable to expect assisted GPS positioning methods to yield an accuracy of less than 20 m 67 percent of the time. During 2003-2009, a new batch of GPS satellites will be launched to include two addi?tional civilian carrier frequencies that could potentially yield positioning accuracy within 1 m for civilian users, even without the use of a ground-based augmentation system. As more breakthroughs in positioning techniques take place, fueled by the strong interest in location-based services from the industry, MTs are likely equipped with reasonably accurate location tracking capability in the near future. The time is thus ripe for active research into how such inherent tracking capability may be harnessed to bring about a leap in wireless network services.
One exciting research area in which mobile positioning is extremely valuable is mobility pre?diction. The use of real-time positioning infor?mation for mobility prediction could potentially give rise to better accuracy and greater adapt?ability to time-varying conditions than previous methods. The availability of practical and accurate mobility prediction technique could open the door to many applications such as resource reservation location management- location-based service and others that have yet to be identified. While there has been previous work that attempted to perform mobility prediction based on mobile positioning, none of the work has addressed the fact that the cell boundary is normally fuzzy and irregularly shaped due to terrain characteristics and the existence of obstacles that interfere with radio wave propagation. Instead either hexagonal or circular cell boundaries have been assumed for simplicity.
Our research seeks to develop mobility pre?diction techniques that utilize real-time mobile positioning information without the need for any cell geometry assumption. While the positioning accuracy of current commercially available GPS-based MTs is still poor, our work is built on the assumption that future MTs could achieve much better accuracy than today (say < 10 m). We have developed a decentralized prediction scheme in which individual MTs equipped with positioning capability shall perform mobility pre?dictions based on approximated cell boundary data that were downloaded from the serving BS. The approximated cell boundary is represented as a series of points around the BS; these points are computed based on the previous handoff locations reported by other MTs. In that scheme, road topology information has not been incorpo?rated. Since MTs that are carried in vehicles would encounter more frequent handoffs. They are the ones that would benefit most from mobil?ity predictions, and are therefore the main focus of our work. Because vehicles travel on roads, the incorporation of road topology information into the prediction algorithm could potentially yield better accuracy. In this article, we consider a centralized approach in which each BS shall perform mobility predictions for individual active MTs within its coverage area. Since a BS has more computational and storage resources than an MT does, we can afford to incorporate road information into our prediction scheme for better accuracy.
The remainder of this article is organized as follows. We first describe the mobility prediction technique we have developed. We then describe the application of the proposed prediction tech?nique for wireless resource reservation with the objective of handoff prioritization. Next, we describe the simulations that have been carried out for performance evaluation. Finally, we give our conclusions.