A Distributed Mode Selection Approach Based on Evolutionary Game for Device-to-Device Communications
Bibliographic record
Abstract
As one of the key technologies for the fifth generation (5G) mobile networks, device-todevice (D2D) communications offer promising benefits such as high spectrum efficiency, traffic offloading, and enhanced coverage. Depending on the resource sharing between D2D users and regular cellular users, a D2D user equipment (UE) can dynamically switch its communication mode to improve the quality of service (QoS) and user experience. Mode selection is an essential issue to ensure the QoS of D2D UEs while maximizing the system capacity. In this paper, we investigate the D2D mode selection problem from a novel perspective. In addition to the classic cellular mode and direct reuse mode, we further consider a relay mode for D2D UEs. Moreover, in order to address a potentially large population of D2D UEs, we propose an evolutionary game-based approach for D2D mode selection. The evolutionary game is formulated with a utility function that takes into account both the achievable throughput of D2D UEs and the radio resource consumption. Based on the evolutionary game formulation, we implement selection dynamics, i.e., replication by imitation, in a device-controlled mode selection algorithm. To evaluate the performance of the proposed mode selection algorithm, we conduct simulations to compare it with three baseline schemes, including an approach based on maximum signal-to-interference-plus-noise, a distance-based approach, and a random approach. As shown in the simulation results, the proposed approach achieves higher utilities than the baseline schemes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".