A Multi-Agent Deep Reinforcement Learning-Based Handover Scheme for Mega-Constellation Under Dynamic Propagation Conditions
Bibliographic record
Abstract
With the rapidly increasing number of satellites, the handover scheme design is critically important for the low Earth orbit (LEO) satellite networks, especially for the mega-constellations that include massive number of LEO satellites. However, the existing handover schemes for LEO satellite networks are designed based on the static propagation conditions, which cannot satisfy the dynamic feature of communication environment caused by the mobility of LEO satellites and users. To address this issue, a centralized adaptive intelligent handover scheme for mega-constellations is proposed, where the dynamics of the propagation conditions and limited LEO satellite capacity are taken into considerations. Specifically, we first use a three-state Markov model to characterize the dynamically varying propagation conditions between satellites and users. Then, the Loo model is employed to describe the dynamic land mobile satellite channels. By considering the user transmission rate requirement and the load-balancing demand of satellites, we design the user utility function and formulate an optimization problem that aims to maximize the overall long-term utility of the network. To reduce the handover decision-making complexity, a multi-agent successive hysteretic deep Q-learning algorithm is developed and it can efficiently solve the formulated problem by reducing the state and action space. To reduce the signaling overhead and the computation complexity of the proposed centralized handover scheme brought to the control center, a distributed intelligent handover scheme is further developed, where each user is enabled to independently make the handover decision only based on the local information. Simulation results show that both the proposed centralized and distributed approaches can efficiently improve the network performance over the existing schemes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".