Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution
Bibliographic record
Abstract
Heterogeneous networks (HetNets) with end-to-end (E2E) network slicing are regarded as effective approaches to meet diverse service requirements from vertical industries. Due to the dense deployment of base stations (BSs) and the complicated associations between BSs and E2E network slices (NSs) in the scenario, the handoff problem faces challenges of the huge system state space and handoff action space and the considerable communication overhead. In this paper, we take these issues into account and consider a distributed E2E NS handoff decision framework in the HetNet. A decentralized Markov decision process (DEC-MDP)-based model is formulated for the distributed E2E NS handoff problem, and the jointly observable and random characteristics of the DEC-MDP are analyzed. To obtain a theoretical performance reference, the original distributed E2E NS handoff problem is simplified, and a Nash equilibrium-based performance bound is given. More practically, the multi-agent double deep Q-network-based distributed handoff (MA-DDQN-DH) algorithm with the centralized training and decentralized executing framework is proposed. Simulation results show that the Nash equilibrium-based performance bound is reasonable, and the proposed MA-DDQN-DH algorithm performs well in the comparison.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".