Handover for Multi-Beam LEO Satellite Networks: A Multi-Objective Reinforcement Learning Method
Bibliographic record
Abstract
In multi-beam low-earth orbit (LEO) satellite networks, frequent handovers between intra-satellite and inter-satellite beams are inevitable. In this letter, we design a beam handover strategy based on the multi-objective reinforcement learning (MORL) method to achieve seamless and effective handover between multiple beams of LEO satellites. We first model the handover optimization problem of the multi-beam LEO satellite networks as a multi-objective optimization (MOO) problem to jointly maximize throughput, minimize the handover frequency, and keep the network load balanced. On this basis, we convert the MOO problem into a multi-objective Markov decision process (MOMDP), and utilize an MORL method, called multi-objective deep Q-learning network (MODQN), to learn and achieve the optimal solution. Simulation results show the effectiveness and superiority of the proposed handover scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".