AI-fuelled Dimensioning and Optimal Resource Allocation of 5G/6G Wireless Communication Networks
Bibliographic record
Abstract
The advent of 5G/6G broadband wireless networks brings several challenges with respect to optimal resource planning and allocation. In a heavily interconnected network of wireless devices, and users along with their equipment, all compete for scarce resources which further emphasizes the importance of fair and efficient allocation of those resources for the proper functioning of the networks. This paper tackles a crucial and timely topic, i.e., understand the various factors involved for optimizing network performance and ensuring fair access for different users, applications and devices. Integrating Machine Learning (ML) and Artificial Intelligence (AI) for predictive dimensioning and pattern mining over the network traffic can enable dynamic and intelligent resource allocation, increase network capacity, enhance the underlying capabilities between users and core network, and better correlate the Quality of Service (QoS). The scientific contribution of this paper entails novel AI models harvesting data from real-world 5G/6G testbeds offered through the AI as a Service (AIaaS) paradigm to enable model reuse and seamless exploitation for different 5G/6G application requirements and learning tasks.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".