QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach
Bibliographic record
Abstract
This paper studies spectrum sharing for providing better quality of experience in 5G networks, which are characterized by multidimensional heterogeneity in terms of spectrum, cells, and user requirements. Specifically, spectrum access, power allocation, and user scheduling are jointly investigated and an optimization problem is formulated with the objective of maximizing the users' satisfaction across the network. In order to reduce the complexity and overhead, decentralized solutions with local information are required. To this end, we employ game-theoretic approach and interference graph to solve the problem. The proposed game is proved to have at least one Nash Equilibrium (NE), corresponding to either the globally or locally optimal solution to the original optimization problem. A concurrent best-response iterative algorithm is first devised to find the solution, which can converge to an NE, but may not be globally optimal. Therefore, a spatial adaptive play iterative (SAPI) learning algorithm is further proposed to search the global optimum. Theoretical analysis demonstrates that the SAPI algorithm can guarantee to find the globally optimal solution with an arbitrary large probability, when the learning step is set to be sufficiently large. Simulation results are provided to validate the performance of the proposed algorithms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".