Low Earth Orbit Satellite (LEOS)-Assisted Integrated Access and Backhauling in xG Wireless Communication: A Generalizable RL Framework
Bibliographic record
Abstract
In next-generation (xG) wireless communication networks, developing generalizable learning models that inherently adapt to diverse conditions is crucial. This paper proposes a reinforcement learning (RL) framework for subchannel (SC) allocation in low Earth orbit satellite (LEOS)-assisted integrated access and backhauling (IAB) networks. We consider an integrated terrestrial-satellite network, where a LEOS provides backhaul services to cellular base stations (BSs) in remote areas while forwarding data from mobile user equipments (UEs) to the core network. The objective is to maximize the achievable rate for UEs while satisfying demand requirements and backhaul constraints. To ensure generalizable and efficient SC allocation across environments, we formulate the resource management problem as an invariant policy learning framework, which is decomposed into two subproblems: state representation learning and policy optimization. Our approach learns state representations that remain invariant across diverse environments. Additionally, the invariant policy, obtained from the hypergraph output layer, captures the fundamental causes of successful actions, enabling robust decision-making. By embedding problem constraints into both the model architecture and the training objective, the framework enhances the transparency of the invariant policy optimization process. Furthermore, we derive a data-dependent generalization bound that characterizes the policy’s performance in unseen environments. Simulation results demonstrate that the proposed policy consistently outperforms traditional methods across multiple environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".