Spectrum allocation techniques for industrial smart grid infrastructure
Bibliographic record
Abstract
5G research shows more potential attention to mobile communications in information intensive industrial sectors such as power utility. In smart grid context, employing licensed assisted access (LAA) allows smart grid operators to transfer utility data between different sites using the unlicensed and licensed bands. This can play a crucial role in improving efficiency, sustainability, stability, and to meet the quality of service (QoS) requirements of different smart grid consumer requests. Considering the unlicensed band, there is a strong need to develop new LAA unlicensed access technology that can improve spectrum acceptability compared to conventional Wi-Fi to meet the high volumes of information in smart grids. In this paper, we investigate the spectrum allocation techniques required to exploit smart grid requirements by setting a minimum bit error rate (BER) threshold while evaluating the availability of white holes in the unlicensed band. Simulation results confirm the advantages of the proposed scheme in allocating more resources to LAA unlicensed users subject to their load requirements. This paper provides a new method for intelligent spectrum allocation to support the communication requirements of smart grid networks.
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".