Performance Evaluation of IPTV Zapping Time Reduction Using Edge Processing of Fog RAN
Bibliographic record
Abstract
Internet Protocol Television (IPTV) is a promising technology that can provide TV broadcast services everywhere and anytime in next-generation wireless networks. However, channel zapping delay time between two successive channel switches is one of the key metrics that may hinder viewers' satisfaction with the IPTV system. Several factors are contributed to prolonging the switching delay such as the delay of the access link that could be generated by the underlying network. In this paper, the minimization of the zapping delay is investigated using the concept of Fog Radio Access Networks (F-RAN) architecture. F-RAN will bring the access points closer to end users (cloud edge). This merit can be utilized an advantageous aspect for minimizing the zapping time of IPTV system due to the low latency communication over F-RAN architecture. To testify the improvement in the IPTV system, an experimental investigation method is applied based on various simulation scenarios. This would be achieved via identifying the problem of the zapping time from the correlated literature, followed by examining the associated causes for this delay. Furthermore, the F-RAN architecture has been proposed as a solution to the part of Zapping Time (ZT) latency that originates from the communication architecture. Additionally, the simulation design is developed based on assessment of two types of cellular architectures, which are the full centralized processing C-RAN and the distributed edge processing F-RAN architecture. The performance evaluation is measured based on the comparison of zapping delay time in both of the F-RAN architecture with the corresponding full centralized C-RAN architecture. Simulation results demonstrate a noticeable reduction in the zapping time with the F-RAN compared to the virtualized C-RAN architecture. Hence, the zapping delay time can be optimized with the application of F-RAN architecture.
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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 | 0.002 | 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".