Resource Management and Admission Control for Tactile Internet in Next Generation of Radio Access Network
Bibliographic record
Abstract
Tactile Internet (TI) is one of the main service types in 5G, which requires tight delay preservation for each end-to-end (E2E) transmission link of TI pair of users. Consequently, it is required a highly sophisticated resource allocation (RA) algorithm over cloud radio access networks (C-RANs). In this paper, we aim to address this problem based on admission control (AC) and power allocation in orthogonal frequency division multiple access (OFDMA) mode with considering joint fronthaul and access delay in C-RAN. The objective of our proposed RA problem is to minimize a total transmit power subject to E2E delays and C-RAN limitations, e.g., a capacity limitation of fronthaul and power transmission links. Due to tight E2E constraints, the RA problems suffers from an infeasibility issue, i.e., the requirements of all TI users are not guaranteed, which leads to high degradation of services of TI users. The proposed AC of this paper can overcome this practical challenge while RA can help to use the resources in the best possible extent. Through simulation results, we investigate diverse service satisfaction parameters such as service acceptance ratio (SAR) and transmit power. Simulation results reveal that by dynamic adjustment of the access and fronthaul delays, transmit power can be saved compared to the case in which the delay is fixed over access and fronthaul links. Moreover, the number of rejected users in the network is significantly reduced, and more users are accepted, i.e., the SAR increases. Simulation results demonstrate that our dynamic delay adjustment and RA along with AC can outperform the benchmark algorithm in this context up to 3 dB.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".