Dynamic Cell Association for Non-Orthogonal Multiple-Access V2S Networks
Why this work is in the frame
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Bibliographic record
Abstract
To meet the growing demand of mobile data traffic in vehicular communications, the vehicle-to-small-cell (V2S) network has been emerging as a promising vehicle-to-infrastructure technology. Since the non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) can achieve superior spectral and energy efficiency, massive connectivity and low transmission latency, we introduce the NOMA with SIC to V2S networks in this paper. Due to the fast vehicle mobility and varying communication environment, it is important to dynamically allocate small-cell base stations and transmit power to vehicular users with considering the vehicle mobility in NOMA-enabled V2S networks. To this end, we present the joint optimization of cell association and power control that maximizes the long-term system-wide utility to enhance the long-term system-wide performance and reduce the handover rate. To solve this optimization problem, we first equivalently transform it into a weighted sum rate maximization problem in each time frame based on the standard gradient-scheduling framework. Then, we propose the hierarchical power control algorithm to maximize the equivalent weighted sum rate in each time frame based on the Karush-Kuhn-Tucker (KKT) optimality conditions and the idea of successive convex approximation. Finally, theoretical analysis and simulation results are provided to demonstrate that the proposed algorithm is guaranteed to converge to the optimal solution satisfying KKT optimality conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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