NOMA-Based D2D-Enabled Traffic Offloading for 5G and Beyond Networks Employing Licensed and Unlicensed Access
Bibliographic record
Abstract
As the versatile applications emerge, traffic offloading is an urgent issue to improve the performance for the fifth generation (5G) and beyond networks. We focus on the scenario where a device is enabled to transmit to more than one device simultaneously. The device-to-device (D2D) enabled traffic offloading scheme is studied by employing non-orthogonal multiple access (NOMA) and unlicensed access technologies. Our target is to maximize the capacity of the D2D network by optimizing subchannel assignment and power control while guaranteeing the capacity of NOMA-based cellular links and the WiFi system. The formulated problem is a non-convex mixed integer programming problem, which is hard to solve within a rational time. The problem is decomposed into subchannel assignment and power control subproblems. A matching based licensed subchannel allocation algorithm and an unlicensed subchannel access mechanism are proposed. Furthermore, we propose a centralized power control algorithm and a distributed power control algorithm based on global and local information, respectively. Besides, the unlicensed resource management scheme based on Stackelberg game is proposed to achieve the near-optimal utility of both D2D links and the WiFi system. The simulations illustrate that the proposed scheme can increase the throughput of D2D networks efficiently compared with other works.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".