Power-Aware Optimized RRH to BBU Allocation in C-RAN
Bibliographic record
Abstract
Wireless networks have faced increasing demand to cope with the exponential growth of data. Conventional architectures have hindered the evolution of network scalability. However, the introduction of cloud technology has brought tremendous flexible and scalable on demand resources. Thus, cloud radio access networks (C-RANs) have been introduced as a new trend in wireless technologies. Despite the novel advancements that C-RAN offers, remote radio head (RRH)-to-base band unit (BBU) resource allocation can cause significant downgrade in efficiency, particularly the allocation of computational resources in the BBU pool to densely deployed small cells. This causes an increase in power consumption and wasted resources. Consequently, an efficient resource allocation method is vital for achieving efficient resource consumption. In this paper, the optimal allocation of computational resources between RRHs and BBUs is modeled. This is dependent on having an optimal physical resource allocation for users to determine the required computational resources. For this purpose, an optimization problem that models the assignment of resources at these two levels is formulated. A decomposition model is adopted to solve the problem by formulating two binary integer programming subproblems; one for each level. Furthermore, two low complexity heuristic algorithms are developed to solve each subproblem. Results show that the computational resource requirements and the power consumption of BBUs and the physical machines decrease as the channel quality worsens. Moreover, the developed heuristic solution achieves a close to optimal performance while having a lower complexity. Finally, both models achieve high resource utilization, cementing the efficiency of the proposed solutions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".