Virtualized allocation performance analysis in 5G two-tier cellular networks
Bibliographic record
Abstract
Enhancement of quality of services (QoS) parameters is a challenging requirement in the Fifth Generation (5G) networks. Delay sensitive applications such as voice over internet protocol, online gaming, and live video are easily affected especially in high data traffic with limited available resources. Moreover; virtualization in standard long term evolution (LTE) systems is considered as an integral promising solution in the next generation networking architecture. In this paper, we aim to enhance the delay performance of such applications by utilizing the extra resources available in two-tier cellular network architecture with macro- and micro-cells. Virtualization is considered by allowing users belonging to the macro-cell to be allocated resources of the micro-cell when located in the micro-cell range. The average delays for various traffic types are evaluated to verify the framework's performance effectiveness before and after resources sharing scenario. Simulation results show that the proposed framework considerably reduces UE's average delay when compared with the non-sharing scheme.
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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 | 0.001 |
| 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".