Resource Allocation for Green Cloud Radio Access Networks With Hybrid Energy Supplies
Bibliographic record
Abstract
In this paper, we study sustainable resource allocation for cloud radio access networks (CRANs) powered by hybrid energy supplies (HES). Specifically, the central unit (CU) in the CRANs distributes data to a set of radio units (RUs) powered by both on-grid energy and energy harvested from green sources, and allocates channels to the selected RUs for downlink transmissions. We formulate an optimization problem to maximize the net gain of the system which is the difference between the user utility gain and on-grid energy costs, taking into consideration the stochastic nature of energy harvesting process, time-varying on-grid energy price, and dynamic wireless channel conditions. A resource allocation framework is developed to decompose the formulated problem into three subproblems, i.e., the hybrid energy management, data requesting, and channel and power allocation. Based on the solutions of the subproblems, we propose a net gain-optimal resource allocation (GRA) algorithm to maximize the net gain while stabilizing the data buffers and ensuring the sustainability of batteries. Performance analysis demonstrates that the GRA algorithm can achieve close-to-optimal net gain with bounded data buffer and battery capacity. Extensive simulations validate the analysis and demonstrate that GRA algorithm outperforms other algorithms in terms of the net gain and delay performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".