Joint FSO Fronthaul and Millimeter-Wave Access Link Optimization in Cloud Small Cell Networks: A Statistical-QoS Aware Approach
Bibliographic record
Abstract
We investigate resource optimization for the downlink cloud small cell network, where the baseband unit pool communicates with the buffer-aided small remote radio heads (SRRHs) through free space optical fronthaul, and SRRHs transmit to the user equipments (UEs) by using time division multiplexing-based millimeter wave access links. Our objective is to maximize the supportable aggregate data arrival rate in the network by exploiting the inter-dependence of fronthaul and access links. Toward this objective, we consider maximum acceptable end-to-end queue-length bound violation probability constraints, load-balancing constraints in the access link, fronthaul link selection constraints, and transmit power budget constraints of fronthaul and access links. Since the joint fronthaul and access link optimization is a non-convex and combinatorial problem, we develop an iterative solution by decomposing the original optimization problem into two sub-problems. The first sub-problem optimally obtains fronthaul and access link power allocation and fronthaul link selection by using Lagrangian dual decomposition and canonical one-to-one matching techniques. By employing the Lagrangian dual decomposition and alternating optimization techniques, the second sub-problem obtains near optimal data arrival rate for each UE, UE-SRRH associations, fronthaul rate allocation among the transmitted data for the UEs, and the transmission duration scheduling in millimeter wave access link. An algorithm of polynomial complexity is developed in order to determine the supportable aggregate data arrival rate by considering the statistical quality-of-service requirements, and its convergence is proved. The simulation results depict that the proposed scheme significantly improves the aggregate data arrival rate over several benchmark schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".