B5G: Intelligent Coexistence Model for Edge Network
Bibliographic record
Abstract
While researchers are focusing on the fifth-generation (5G) cellular network, the network operators and standard bodies are discussing specifications for beyond fifth-generation (B5G) and 6G. Attributes of B5G include edge intelligence which involves artificial intelligence or machine learning (ML) in the architecture. Network edge servers, or base stations (BS), use edge computing to make time-critical decisions, especially in IoT devices while the data are being transmitted into the cloud. As the dynamic spectrum sharing continues in B5G, BSs implements the coexistence between Wi-Fi and cellular network. These exciting advances require energy efficiency to be considered as network operators pay the majority of the expenses to energy consumption. In this paper, different prediction models on traffic behaviour are computed to determine the lowest root mean square error. The best prediction model is used in the wake-up policy to consider the communication and computing times of the BS needed to return in service. Furthermore, a wake-up policy for the BS is introduced to maintain Quality of Service (QoS) while minimizing energy consumption. Particularly, a wake-up time threshold is set so that if the duration of the traffic prediction time does not cross this threshold, the decision will not be in favour to put it into sleep mode. This ensures that the QoS of the user is not compromised, as this threshold removes the unnecessary wasted time for BS to go to sleep and wake-up.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".