Application of Mobility Prediction in Wireless Networks Using Markov Renewal Theory
Bibliographic record
Abstract
An understanding of the network traffic behavior is essential in the evolution of today's wireless networks and thus leads to a more efficient planning and management of the network's scarce bandwidth resources. Prior reservation of radio resources at future locations of a user's mobile trajectory can assist in optimizing the allocation of the network's limited resources and sustaining a desirable quality-of-service (QoS) level. This can also help to ensure that the network service can be available anywhere and anytime, which is only possible if, at any time, we can predict from where a user is going to make its demands. In this paper, we apply Markov renewal processes for both mobility modeling and predicting the likelihoods of the next-cell transition, along with anticipating the duration between the transitions, for an arbitrary user in a wireless network. Our proposed prediction technique will also be extended to compute the likelihoods of a user being in a particular state afterNtransitions. The proposed technique can also be used to estimate the expected spatial-temporal traffic load and activity at each location in a network's coverage area. Using some real traffic data, we illustrate how our proposed prediction method can lead to a significant improvement over some of the conventional methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".