Carrier Aggregation, Load Balancing, and Backhauling in Non-Terrestrial Networks: Generative Diffusion Model-Based Optimization
Bibliographic record
Abstract
The joint problem of carrier aggregation (CA), load balancing, and backhauling (JCALB) is studied in the context of non-terrestrial networks (NTNs) based on low-earth orbit satellites (LEOS). While CA can potentially enhance the communication capacity by dynamic selection of component carriers (CCs) or frequency bands for the LEOS, load balancing adjusts the portion of each CC utilized by individual LEOS in order to optimize resource utilization. Aiming to minimize the usage of each CC by individual satellites while maximizing their achievable total rate, we formulate the JCALB problem as a mixed-integer stochastic optimization problem involving both discrete and continuous decision variables, which is NP-hard. To solve the problem suboptimally, we divide it into two sub-problems: backhauling and activating/deactivating CCs for the satellites, and load balancing over the CCs. For the first subproblem, we develop a generative AI-based decision-making (GADM) algorithm based on a diffusion model. We apply the GADM algorithm to actor-critic and multi-arm bandit frameworks in reinforcement learning, in order to develop diffusion-based actor-critic CA and backhauling (DA2CAB) and diffusion-based upper confidence bound (UCB) CA and backhauling (DU2CAB) methods for LEOS-based NTNs. Finally, given the activated CCs for the satellites, we develop an iterative and distributed load balancing algorithm within NTNs. The simulation results demonstrate that our derived diffusion-based algorithms enable LEOS to achieve a higher transmission capacity while allocating fewer CCs and subchannels (SCs) compared to algorithms based on the double deep Q-Network (DDQN) and the traditional UCB approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".