Dynamic Spectrum Management through Resource Virtualization with M2M Communications
Bibliographic record
Abstract
Wireless spectrum licensing has increased due to the continuous evolution and use of cellular technology. The increase in the number of mobile-connected devices and global data traffic demand has led to a significant increase in demand for spectrum access with studies showing that there is unexploited capacity in the spectrum. This is especially critical for 5G networks where the service requirements are extremely stringent. To that end, this article proposes dynamic spectrum management through the combination of two innovative architectures, wireless resource virtualization (WRV) and machine-to-machine (M2M) communications. WRV allows for better utilization of the spectrum, while reducing both the capital and operational expenditures. On the other hand, M2M communications can help boost capacity and improve quality of service by leveraging spectrum access across multiple radio technologies. In this article, a brief discussion of multi-radio access technology heterogeneous networks is given. Then the problem of dynamic spectrum management through resource virtualization with M2M communications is described. To the best of our knowledge, such a combined framework has not been previously proposed. Two different algorithms are proposed to evaluate the performance of the considered architecture, namely a decomposition- based algorithm and a greedy-based algorithm. Simulation results show that such architecture can boost the overall capacity of the system by achieving higher data rates. Moreover, it is shown that the number of possibly supported M2M pairs is increased while using the same spectrum.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".