Conjoint Routing and Resource Allocation in OFDMA-Based D2D Wireless Networks
Bibliographic record
Abstract
In this paper, we develop a highly efficient two-tier technique for jointly optimizing the routes, the subcarrier schedules, the time-shares, and the power allocations in device-to-device communication networks with thousands of randomly dropped wireless nodes. The network is first divided into a set of non-overlapping sub-networks, each with its own regional controller. The role of such a controller is to optimize the sub-network within its region and to act as an interface between nodes communicating across regions. The first tier of the proposed technique uses a novel approach for splitting a set of highly non-convex constraints into effectively two sets of convex ones and optimization proceeds by using two loops: an outer loop for iterating between the power allocations and the subcarrier schedules, and an inner loop for iterating between the two sides of the split constraints. In the second tier, a technique analogous to the one used in the first tier is applied to the network composed of the regional controllers. Optimization in this tier is performed by a global controller. The proposed technique is capable of efficiently optimizing networks with tens of thousands of nodes and with significantly better performance than existing joint design techniques, which can only optimize networks with a few tens of nodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".