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Cited by 558

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks

Tong Liu, Bahl, P., Chlamtac, I.·1998·IEEE Journal on Selected Areas in Communications

Abstract

Wireless ATM networks require efficient mobility management to cope with frequent mobile handoff and rerouting of connections. Although much attention has been given in the literature to network architecture design to support wide-area mobility in public ATM networks, little has been done to the important issue of user mobility estimation and prediction to improve the connection reliability and bandwidth efficiency of the underlying system architecture. This paper treats the problem by developing a hierarchical user mobility model that closely represents the movement behavior of a mobile user, and that, when used with appropriate pattern matching and Kalman filtering techniques, yields an accurate location prediction algorithm, HLP, or hierarchical location prediction, which provides necessary information for advance resource reservation and advance optimal route establishment in wireless ATM networks.

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Metadata

Authors
Liu, T., Bahl, P., & Chlamtac, I.
Venue
IEEE Journal on Selected Areas in Communications
Published
1998
Volume
16
Issue
6
Pages
922-936
ISSN
0733-8716 (ISSN-L)
OpenAlex
W2154818210
Cited by
558
References
7
FWCI
10.70
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. https://app.cochat.ai/papers/10.1109/49.709453
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Liu, T., Bahl, P., & Chlamtac, I. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/49.709453

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.