D2D Relay Selection and Fairness on 5G Wireless Networks
Bibliographic record
Abstract
Wireless cellular networks aim to operate efficiently in low and high SINR regions while maximizing the overall network capacity. Nevertheless, achieving high capacity in all regions is opposed to being fair with all mobile devices. i.e., it is well known that, due to the nature of wireless communications, the effective channel capacity at the cell edge is significantly smaller than at the cell center. We consider a wireless cellular network where mobile devices have multiple network interfaces and are capable to relay traffic. In this paper we investigate: 1) How the use of a mobile device as a relay can improve the traffic fairness, 2) How to maximize the overall network capacity, and 3) How to devise the best relay selection strategy. We propose a greedy algorithm that overcomes the aforementioned challenges by appropriately selecting the mobile devices that will act as relays. Our simulation results indicate that using mobile devices as relays not only helps to improve the Jain's fairness by around 8% but also increases the average network capacity by 20% while reducing the transmission power consumption by 80%.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| 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".