Resource reservation for mobile hotspots in vehicular environments with cellular/WLAN interworking
Bibliographic record
Abstract
Nowadays, the pervasive wireless networks enable ubiquitous high-rate wireless access from everywhere. There have been extensive studies on interworking of complementary wireless technologies in an indoor (residential or business) environment such as offices, hotels, and airport terminals. Nonetheless, there are ever-increasing demands for systematic deployment of moving networks in a vehicular environment such as public transits (e.g., a bus, train, or airplane). Due to the high mobility, it is very challenging to deliver smooth and high-quality video services for such a vehicular network. In this article, we focus on a two-hop moving network integrating both the cellular network and wireless local area network. By means of proper bandwidth reservation, we can satisfy the quality of service constraints of video applications with respect to data loss rate and packet delay. Analytical approaches are introduced to effectively estimate the achievable performance and derive the required bandwidth. To characterize video traffic, a sigmoid function is proposed to model video flows as a Markov-modulated process and fluid-flow analysis is feasible to evaluate data loss rate. At a finer packet level, the batch structure of packet arrivals is captured in the queueing analysis and packet delay is evaluated. On the other hand, for aggregate traffic multiplexed at a local gateway for the vehicular network, we use a fractional Brownian motion process to model the self-similar traffic and estimate data loss rate and packet delay. According to the performance evaluation, we can derive the required channel bandwidth for such a mobile hotspot. Numerical results are presented to demonstrate the application for bandwidth reservation, which is especially useful in case of handover.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".