QoE-Aware User Allocation in NOMA-Enabled MEC Systems: A Distributed Game-Theoretical Approach
Bibliographic record
Abstract
In vehicular networks, mobile edge computing (MEC) allows service providers to allocate vehicle terminals (VTs) to edge servers, providing an effective paradigm for low latency computing services. In resources-limited edge computing scenarios, user allocation needs to achieve the goals of minimizing deployment costs and maximizing coverage density while ensuring service quality. However, the differentiated configuration of user power in non-orthogonal multiple access (NOMA) technology can cause intra-cell and inter-cell interference within the same frequency band, resulting in a significant decrease in transmission rate and a reduction in Quality of Experience (QoE). In this paper, we study the QoE-aware user and power allocation (QUPA) problem with multiple users in a NOMA-enabled MEC system aimed at maximizing users' QoE. We formulate this problem as a QUPA game and theoretically analyze its properties. To achieve the Nash equilibrium for each user, we propose a distributed game-theoretical user and power allocation (GUPA) algorithm that jointly optimizes server selection, channel selection, and transmission power allocation. Meanwhile, we theoretically analyze the convergence and performance by price of anarchy (PoA) of our GUPA algorithm. The experimental results show that the proposed GUPA algorithm can effectively reduce costs and improve user experience in this system.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".