Guest Editorial: Smart, Optimal, and Explainable Orchestration of Network Slices in 5G and Beyond Networks
Bibliographic record
Abstract
Network slicing is a much discussed topic in fifth generation (5G) and beyond (B5G) networks. The network slice feature differentiates 5G and B5G networks from the earlier generations since it replaces the conventional concept of quality of service (QoS) with end-to-end multi-service provisioning and multi-tenancy. A diverse set of resources for computing, networking, storage, and power need to be smartly assigned in network slices. Traditional optimization/resource scheduling techniques are typically one-dimensional and may not scale well in large-scale 5G/B5G networks. Therefore, there is a pressing need to smartly address the orchestration and management of network slices. Since beyond 5G networks will heavily use embedded intelligence, how to leverage AI-based techniques, such as machine learning, deep learning, and reinforcement learning, to address and solve the various complex network slicing problems is emerging as a challenging problem. The Guest Editors worked hard to reach out to researchers from academia and industry to address these points in this Special Issue in search of a genuinely intelligent B5G network rollout that could be both smart and practical.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".