Power Minimization Via Rate Splitting in Downlink Cloud-Radio Access Networks
Bibliographic record
Abstract
Minimizing the power consumption in mobile communication networks while guaranteeing quality of service (QoS) requirements is essential in light of the unprecedented increase in the number of connected devices and the associated data traffic. Conventional cloud-radio access networks (C-RAN) assume treating interference as noise (TIN) while dealing with the provisioning of wireless interference. This paper considers a C-RAN system, where a central processor (CP) at the cloud is connected to several base-stations (BSs) via limited capacity fronthaul links. The paper then assumes a communication scheme where the CP applies rate splitting (RS) to the messages requested by the users, and enables common message decoding (CMD) at the receivers side. The paper addresses the problem of minimizing the weighted transmit power subject to minimum QoS and fronthaul capacity constraints, which is generally a non-convex and difficult problem to solve. We, therefore, propose an iterative approach based on inner-convex approximations (ICA) to obtain a stationary point of the optimization problem. The simulations show the power savings achieved by the proposed RS-CMD scheme as compared to the conventional TIN, especially at high QoS requirements.
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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 | 0.001 |
| 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".