Joint Computing and Radio Resource Allocation in Cloud Radio Access Networks
Bibliographic record
Abstract
This paper considers the problem of joint radio and computing resource allocation in Cloud Radio Access Network (C-RAN) architecture. We develop a resource allocation scheme to maximize weighted sum-rate of the system, while minimizing total power consumption. For power consumption, we consider both static and dynamic power consumption in Remote Radio Heads (RRHs), fronthaul links, and Base Band processing Units (BBUs). Our model considers quality of service requirements, fronthaul capacity, maximum transmission power, and computing capacity constraints in a comprehensive formulation. The joint resource allocation problem is non-convex, which is shown to be NP-hard, and thus we apply a number of techniques to convexify the problem. Then, using the Karush-Kuhn-Tucker (KKT) conditions, we show that the problem can be decomposed into two sub-problems that can be efficiently solved using an iterative Quadratically Constrained Quadratic Program (QCQP) and a bin packing algorithm, respectively. The performance of the proposed scheme is evaluated through simulation studies, which shows the proposed scheme outperforms the existing approaches in BBU minimization, total power consumption, and system utility which is defined as the weighted sum-rate minus power consumption.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".