A computing offloading algorithm for F-RAN with limited capacity fronthaul
Bibliographic record
Abstract
In order to alleviate heavy burden on the capacity-limited fronthaul of C-RAN, a fog computing based C-RAN(F-RAN) architecture has been proposed in recent years. In F-RAN, RRH receives the signal from mobile devices and decides part of tasks to be processed in fog part for computing offloading. With the purpose of offloading sampling signal in fronthaul and separating tasks from cloud part to be processed in fog part, we formulate the problem as a congestion game. And we propose a discrete distribution computing offloading algorithm (DDCO) for F-RAN to solve this game. The DDCO algorithm decides the latency-sensitive mobile to be computed in the fog part such that the fronthaul can be offloaded and Quality of Service (QoS) can be improved. The DDCO algorithm can also balance load in fronthaul in order to improve performance. With the DDCO algorithm, the network get larger throughput and reduce the burden in fronthaul when it achieve a Nash Equilibrium. Finally, we analyze the feasibility of the algorithm. Numerical result corroborate that the DDCO algorithm can well improve throughput compared with the traditional C-RAN network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".