An MDP-Based Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks
Bibliographic record
Abstract
The architecture for the Beyond 3rd Generation (B3G) or 4th Generation (4G) wireless networks aims at integrating various heterogeneous wireless access networks. One of the major design issues is the support of vertical handoff. Vertical handoff occurs when a mobile terminal switches from one network to another (e.g., from wireless local area network to code-division multiple-access 1x radio transmission technology). The objective of this paper is to determine the conditions under which vertical handoff should be performed. The problem is formulated as a Markov decision process with the objective of maximizing the total expected reward per connection. The network resources that are utilized by the connection are captured by a link reward function. A signaling cost is used to model the signaling and processing load incurred on the network when vertical handoff is performed. The value iteration algorithm is used to compute a stationary deterministic policy. For performance evaluation, voice and data applications are considered. The numerical results show that our proposed scheme performs better than other vertical handoff decision algorithms, namely, simple additive weighting, the technique for order preference by similarity to ideal solution, and Grey relational analysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".