Backhaul-aware multicell beamforming for downlink cloud radio access network
Bibliographic record
Abstract
This paper considers a heterogeneous downlink cloud radio access network (C-RAN) where all the base stations (BSs) in the network are connected to a central processor (CP) via capacity-limited backhaul links. Under this model, we investigate the message-sharing transmission strategy where the CP shares each user's message with a fixed subset of BSs, which then serve the user through joint beamforming. In this setting, although the overall long-term average backhaul consumption is limited by the fixed cluster size, the instantaneous backhaul consumption at each BS may vary significantly depending on the data rates of the scheduled users at each time slot. To avoid such large fluctuations in backhaul consumption, this paper proposes a backhaul-aware multicell scheduling and beamforming strategy that explicitly accounts for backhaul consumption. Specifically, a beamforming design algorithm is proposed to maximize the network utility for a downlink C-RAN under both per-BS power constraints and per- BS backhaul constraints in each time slot. Although this problem has already been considered in our previous work, this present paper proposes a new beamforming design algorithm that not only has guaranteed convergence but also achieves better system performance. This paper also shows that the performance of the proposed algorithm can be further improved by iterating with an additional power minimization step.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".