Optimized WiMAX Profile Configuration for Smart Grid Communications
Bibliographic record
Abstract
Worldwide interoperability for microwave access (WiMAX) is one of the wireless communication technologies adopted for communication in smart grids. Due to the inherent differences between smart grid and mobile broadband applications, it is important to adjust planning and deployment of wireless technologies, including WiMAX. To this end, WiMAX is being amended to feature a smart grid system profile known as WiGrid. In this paper, we investigate the optimized configuration of this WiGrid profile, i.e., the choice of frame duration, type-of-service to traffic mapping, scheduling strategies, as well as the system architecture, such that smart grid communication requirements are met. The simulation-based evaluation of WiGrid networks with optimized configurations is facilitated through a newly developed WiGrid module for the network simulator-3 environment. Our results indicate that a priority-based scheduler is an appropriate solution for scheduling time-critical smart grid applications. Furthermore, schedulers should be implemented in such a way that grant sizes smaller than the packet size are avoided, and adjusting the uplink/downlink bandwidth ratio to favor uplink traffic is important to achieve the required latency defined for smart grid applications.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".