Dynamic Power Allocation in High Throughput Satellite Communications: A Two-Stage Advanced Heuristic Learning Approach
Bibliographic record
Abstract
The dynamic radio resource management technology is an essential technology in high throughput satellite (HTS) communications. Aiming at the problem that the traditional static radio resource allocation is difficult to meet the dynamic traffic, the dynamic radio resource allocation based on the meta-heuristic algorithm has been extensively studied. Since wireless channel conditions, differentiated services have stochastic properties, and the environment's dynamics are unknown in HTS, the dynamic radio resource allocation based on model-free deep reinforcement learning (DRL) method was used to learn the optimal policy through interactions with the situation. However, the generalization ability of the existing DRL cannot fully meet the great dynamic change in HTS. To address this issue, we explore an advanced heuristic learning approach that combines DRL with one heuristic algorithm. In the proposed approach, we apply the DRL to accelerate the convergence of the heuristic algorithm and verify the proposed method in the dynamic power allocation (DPA) in HTS. Compared with the traditional methods, the simulation results show that the proposed approach can accelerate the convergence of the heuristic method for the allocation of power in HTS and has a low time complexity of the decision. Compared with the traditional methods, our proposed method can reduce the computational overhead by 6.2–94.8%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".