Joint PRB and Power Allocation for Slicing eMBB and URLLC Services in 5G C-RAN
Bibliographic record
Abstract
Efficient allocation of resources (i.e., physical resource blocks (PRBs), transmit power) for remote radio heads (RRHs) in the fifth generation (5G) cloud radio access network (C-RAN) is crucial for the mobile network operators (MNOs) to support different use cases with diverse quality of service (QoS) requirements. In this paper, we study the resource allocation of enhanced mobile broadband (eMBB) and ultra-reliable lowlatency communications (URLLC) network slices in a 5G C-RAN. We formulate the resource allocation problem as a mixedinteger nonlinear program. We address the isolation between eMBB and URLLC network slices and the uncertainty in the traffic load by using the chance constraint. We consider short packet transmission to enable URLLC data transmission with low latency and high reliability. We propose an algorithm based on penalized successive convex approximation to determine a suboptimal solution of the formulated problem. The proposed algorithm has a polynomial time complexity. Simulation results show that the proposed algorithm on average achieves 30% higher throughput when compared with a baseline scheme that only optimizes the transmit power of users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".