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 telecommuniÃ‚Â¬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 reallocatÃ‚Â¬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 knowlÃ‚Â¬edge about individual MTs' dynamics and call holding patterns.
In the United States, the FCC recently manÃ‚Â¬dated that cellular service providers must be able to pinpoint a wireless emergency call's originatÃ‚Â¬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 addiÃ‚Â¬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 preÃ‚Â¬diction. The use of real-time positioning inforÃ‚Â¬mation for mobility prediction could potentially give rise to better accuracy and greater adaptÃ‚Â¬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 preÃ‚Â¬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 preÃ‚Â¬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 incorpoÃ‚Â¬rated. Since MTs that are carried in vehicles would encounter more frequent handoffs. They are the ones that would benefit most from mobilÃ‚Â¬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 techÃ‚Â¬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.
2. ROAD-TOPOLOGY-BASED (RTB) MOBILITY PREDICTION TECHNIQUE
In our proposed technique, we require the servÃ‚Â¬ing BS to receive updated information about each active MT's position at regular time interÃ‚Â¬vals (e.g., 1 s). This will consume several bytes per second of wireless bandwidth for each MT, which might be negligible for future wireless serÃ‚Â¬vices. In order to incorporate road information into the mobility predictions, each BS needs to maintain a database of the roads within its coverage area, We shall treat the road between two neighboring junctions as a road segment, and identify each segment using a junction pair (J1, J2), where a junction can be interpreted as the intersection of roads (e.g.- T-junction or cross-Junction). The approximate coordinates of each junction pair are to be stored in the database. Since a road segment may contain bends, it can be broken down further into piecewise-linear line segments. The coordinates defining these line segments, within each road segment are also recorded. All the above coordinates could easily be extracted from existing digital maps previously designed for GPS-based navigational devices. Infrequent updates to these maps are foreseen because new roads are not constructed very often, while existing road layouts are seldom modified.
The database also stores some important information about each road segment. Since two-way roads would probably have different characteristics for each direction. The database shall store information corresponding to oppoÃ‚Â¬site directions separately. Information stored in the database includes the average time taken to transit the segment, the neighboring segments at each junction, and the corresponding probability that an MT traveling along the segment would select each of these neighboring segments as its next segment- These transition probabilities could be computed automatically from the previÃ‚Â¬ous paths of other MTs. The database will be updated periodically every Tdatabase since many of its elements are dependent on current traffic conditions.
In reality, the transition probabilities between road segments would probably vary with time and traffic conditions. For stochastic processes whose statistics vary slowly with time, it is often appropriate to treat the problem as a succession of stationary problems. We propose to model the transition between road segments as a secÃ‚Â¬ond-order Markov process, and we assume that it is stationary between database update instances to simplify the computations. Based on this model, the conditional distribution of an MT choosing a neighboring segment given all its past segments is assumed to be dependent only on the current segment and the immediate prior segment.
(Download Full Report And Abstract)