Optimization of Radio and Infrastructure Resources for Efficient Massive Data Transmission and Storage in Mobile Cloud over 5G HetNet
Bibliographic record
Abstract
In a dense HetNet enhanced with MCC and MEC technologies, an efficient usage of limited radio resources and augmented network infrastructure is critical to achieve the best possible user experience and network performance, while realizing an efficient and effective response to the big data requirements of mobile devices. Considering this 5G-based dense HetNet architecture, this thesis proposes: (1) a Redundant Array of Independent Disks (RAID) based data storage algorithm that minimizes the data transmission delay during transmission of a big data file from the UE to the cloud data centre; (2) a mechanism design based algorithm to efficiently transfer big data files with minimum transmission delay from the UE to the cloud data centre; and (3) an Ant Colony Optimization (ACO) based energy-aware algorithm to jointly allocate the radio resources and transmit the big data file from the UE to the cloud data centre. Simulation results are provided, demonstrating the effectiveness of the proposed algorithms in comparison to the Greedy and the Data Split Multiple UE (DSMU) algorithms in terms of transfer delay, time required by a UE to store the big data file, and energy consumed by the UE during transmission.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".