Joint Optimization Algorithm for Adaptive Filtering and Dynamic Resource Allocation in 5G NTN Low Earth Orbit Satellite Communications
Bibliographic record
Abstract
With the deep integration of 5G and Non-Terrestrial Networks (NTN), Low Earth Orbit (LEO) satellites have emerged as key enablers for constructing integrated space-air-ground communication networks due to their wide coverage and flexible deployment.However, the high-speed movement of LEO satellites results in rapidly time-varying channels and multipath fading, causing significant signal degradation from noise and interference.Meanwhile, the surge in access devices demands urgent dynamic allocation of resources such as spectrum and power.Existing studies show that traditional fixed-parameter filtering algorithms struggle to track time-varying channels, static resource allocation schemes fail to fully exploit channel state information, and single-resource optimization neglects the coupling between multiple resources.Some joint optimization approaches suffer from poor dynamic adaptability and high computational complexity.To address these challenges, this paper proposes a joint optimization method for adaptive filtering and dynamic resource allocation tailored for 5G NTN LEO satellite communications.First, an improved adaptive filtering algorithm with dynamic parameter adjustment is designed to optimize filtering parameters in real time based on channel state estimation, enhancing signal interference resilience.Then, leveraging precise channel state information, a dynamic resource allocation algorithm considering service demands and resource constraints is developed to achieve cross-layer collaborative allocation of spectrum and power resources.A joint optimization framework aligned with the LEO satellite communication scenario is constructed, where the synergy between signal processing and resource allocation effectively improves system communication reliability and resource utilization efficiency.This provides theoretical and technical support for the practical deployment of 5G NTN LEO satellite systems.
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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 | 0.000 | 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) | 0.002 | 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".