Smart Dynamic Pricing and Cooperative Resource Management for Mobility-Aware and Multi-Tier Slice-Enabled 5G and Beyond Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a novel cooperative resource sharing technique in multi-tier edge slicing networks which is robust to imperfect channel state information (CSI) caused by user equipments’ (UEs) mobility. Due to the mobility of UEs, the dynamic requirements of their tasks, and the limited resources of the network, we propose a smart joint dynamic pricing and resources sharing (SJDPRS) scheme that can incentivize the infrastructure provider (InP) and mobile network operators (MNOs). Aiming to maximize the profits of UEs, MNOs and the InP under the task fulfillment constraints, we formulate an optimization problem by deploying the multi-objective optimization method where in addition to the resource allocation variables, the price values are also the optimization variables. To solve the problem, we adopt a new deep reinforcement learning (DRL) method based on a carefully designed reward function. The simulation results indicate that the proposed resource sharing scenario can increase total profits for the UEs, MNOs, and InP in comparison to non-cooperative case, while also providing almost complete fairness among the players. In particular, as compared to the baselines and benchmarks, the profits for each network component (MNO, InP, and UEs), under fairness considerations, are enhanced by 75%, 79%, and 76%, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it