Throughput Maximization of Network-Coded and Multi-Level Cache-Enabled\n Heterogeneous Network
Bibliographic record
Abstract
One of the paramount advantages of multi-level cache-enabled (MLCE) networks\nis pushing contents proximity to the network edge and proactively caching them\nat multiple transmitters (i.e., small base-stations (SBSs), unmanned aerial\nvehicles (UAVs), and cache-enabled device-to-device (CE-D2D) users). As such,\nthe fronthaul congestion between a core network and a large number of\ntransmitters is alleviated. For this objective, we exploit network coding (NC)\nto schedule a set of users to the same transmitter. Focusing on this, we\nconsider the throughput maximization problem that optimizes jointly the\nnetwork-coded user scheduling and power allocation, subject to fronthaul\ncapacity, transmit power, and NC constraints. Given the intractability of the\nproblem, we decouple it into two separate subproblems. In the first subproblem,\nwe consider the network-coded user scheduling problem for the given power\nallocation, while in the second subproblem, we use the NC resulting user\nschedule to optimize the power levels. We design an innovative\n\\textit{two-layered rate-aware NC (RA-IDNC)} graph to solve the first\nsubproblem and evaluate the second subproblem using an iterative function\nevaluation (IFE) approach. Simulation results are presented to depict the\nthroughput gain of the proposed approach over the existing solutions.\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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".