Name
Abstract | ||
|---|---|---|
The essence of designing a good paging strategy is to incorporate the user mobility
characteristics in a predictive mechanism that reduces the average paging cost with
as little computational effort as possible. In this scope, we introduce a novel paging
scheme based on the concept of reinforcement learning. Learning endows the paging
mechanism with the predictive power necessary to determine a mobile terminal's position,
without having to extract a location probability distribution for each specific user.
The proposed algorithm is compared against a heuristic randomized learning strategy
akin to reinforcement learning, that we invented for this purpose, and performs better
than the case where no learning is used at all. It is shown that if the user normally
moves only among a fraction of cells in the location area, significant savings can
be achieved over the randomized strategy, without excessive time to train the network.
Copyright © 2003 John Wiley & Sons, Ltd.
|
Year | DOI | Venue |
|---|---|---|
2003 | 10.1002/wcm.120 | Wireless Communications and Mobile Computing |
Keywords | DocType | Volume |
tracking mobile users,paging cost,location area,movement area,reinforcement learning,online algorithm | Journal | 3 |
Issue | ISSN | Citations |
8 | 1530-8669 | 1 |
PageRank | References | Authors |
0.36 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Z. Koukoutsidis | 1 | 15 | 8.16 |
| Panagiotis Demestichas | 2 | 736 | 142.82 |
| Michael E. Theologou | 3 | 76 | 16.96 |