A Local Search Algorithm for Resource Allocation for Underlaying Device-to-Device Communications
Bibliographic record
Abstract
Resource allocation for Device-to-Device (D2D) communication underlaying cellular network poses new challenges in terms of interference while at the same time provides increased system sum rate. In this paper, we propose a local search based resource allocation algorithm (LORA) for allocating resource blocks to D2D devices that are shared with Long Term Evolution (LTE) cellular users. We first formulate the problem of downlink resource block (RB) allocation to D2D users from cellular users as a computationally expensive mixed integer nonlinear programming (MINLP) problem. However, as the optimal solution of an MINLP can take exponential time to compute, we propose a local search based algorithm to compute a locally optimal solution based on an initial feasible solution. We compare the obtained system sum rate from this local search algorithm with a well-known greedy heuristic based resource allocation algorithm and a random resource allocation algorithm. The simulation results show that LORA achieves an overall better system sum rate compared to the other algorithms for RB allocation while maintaining the signal quality at the cellular users and the D2D receivers.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".