Performance-cost trade-off of joint beamforming and user clustering in cloud radio access networks
Bibliographic record
Abstract
Cloud Radio Access Network (CRAN) is a promising network architecture for 5G to address the increasing demand for mobile data traffic. We consider a joint beamforming and clustering (user-to-Remote Radio Head (RRH) association) issue for downlink CRAN to solve the sum-rate maximization problem under fronthaul link capacity and per-RRH power constraints. The main objective is to investigate and analyze the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of imperfect Channel State Information (CSI). We propose a hybrid algorithm which periodically activates dynamic and static clustering strategies to manage the allocation process over time. This algorithm has the benefit to approach the optimal performance while being aware of practical system constraints. Furthermore, we present an analysis of major cost metrics for the proposed and reference dynamic algorithms. The simulation results show that our proposed algorithm reduces significantly the complexity and signaling costs while approaching the performance of the optimal solution.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".