Using Bender’s Decomposition for Optimal Power Control and Routing in Multihop D2D Cellular Systems
Bibliographic record
Abstract
In this paper, multihop device-to-device (D2D) communications for cell coverage extension are studied. An in-band underlay D2D mode is considered, and the aim is to satisfy the signal-to-noise-ratio requirements for pre-allocated resource blocks (RBs) on the downlink connections, the signal-to-interference-plus-noise-ratio requirements on pre-allocated RBs for every D2D sidelink connection, and a maximum allowable interference at the base station receiver on all uplink RBs. Power control and routing are performed to minimize the expended user equipment energy in the system while meeting these requirements. An optimization problem is formulated that turns out to be a mixed-integer nonlinear program, which is solved using the generalized Benders decomposition (GBD). The GBD breaks down the formulation into a master sub-problem, an auxiliary sub-problem, and a feasibility sub-problem. In this paper, we focus on finding efficient solution methods for the relaxed version of the master sub-problem that is responsible for generating lower bounds on the optimal objective function. Also, an efficient solution technique for the feasibility sub-problem is proposed. Furthermore, a benchmark disjoint scheme for the same problem is proposed, which performs routing and power control separately. The simulations are conducted to compare the performance of both schemes, which show the superiority of joint routing and power control scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".