Completion Time Minimization in Fog-RANs using D2D Communications and\n Rate-Aware Network Coding
Bibliographic record
Abstract
The device-to-device communication-aided fog radio access network, referred\nto as \\textit{D2D-aided F-RAN}, takes advantage of caching at enhanced remote\nradio heads (eRRHs) and D2D proximity for improved system performance. For\nD2D-aided F-RAN, we develop a framework that exploits the cached contents at\neRRHs, their transmission rates/powers, and previously received contents by\ndifferent users to deliver the requesting contents to users with a minimum\ncompletion time. Given the intractability of the completion time minimization\nproblem, we formulate it at each transmission by approximating the completion\ntime and decoupling it into two subproblems. In the first subproblem, we\nminimize the possible completion time in eRRH downlink transmissions, while in\nthe second subproblem, we maximize the number of users to be scheduled on D2D\nlinks. We design two theoretical graphs, namely \\textit{interference-aware\ninstantly decodable network coding (IA-IDNC)} and \\textit{D2D conflict} graphs\nto reformulate two subproblems as maximum weight clique and maximum independent\nset problems, respectively. Using these graphs, we heuristically develop joint\nand coordinated scheduling approaches. Through extensive simulation results, we\ndemonstrate the effectiveness of the proposed schemes against existing baseline\nschemes. Simulation results show that the proposed two approaches achieve a\nconsiderable performance gain in terms of the completion time minimization.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".