Placement Optimization and Resource Allocation in UxNB-Enabled Sliced 5G Networks
Bibliographic record
Abstract
5G is improving networks via numerous technologies such as network slicing. The latter introduces capabilities to cater to the needs of heterogeneous services. Although 5G ensures resource allocation to satisfy slices with different requirements, it is not straightforward when the cellular coverage is extended or strengthened using unmanned aerial vehicles (UAVs) as NodeB, a.k.a., UxNBs. Indeed, a UxNB can be used to extend the coverage on the edge of a terrestrial BS or to support the temporary dense traffic in a targeted urban area. When deployed, the UxNB must ensure that the 5G services are seamlessly supported without degradation. In this context, we study in this paper how a UxNB should behave in order to support ground users’ services within different 5G slices. Specifically, we formulate the maximization problem of the number of satisfied user requests within different 5G slices by the UxNB, by optimizing the UxNB location and the allocated resources, e.g., subchannels, for communication. Due to the problem’s NP-hardness, we propose a hybrid dueling deep Q-learning (DDQL)-heuristic solution, where a dueling deep Q-learning (DDQL) algorithm for 3D placement is combined with a heuristic resource allocation approach to satisfy the weighted number of service requests. Obtained results demonstrate the efficacy of the proposed method in achieving high satisfaction rates, which are superior to those of other benchmarks. Moreover, without any information about the users’ locations, it performs as well as the offline benchmark.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".