Coalitional Games for Joint Co-Tier and Cross-Tier Cooperative Spectrum Sharing in Dense Heterogeneous Networks
Bibliographic record
Abstract
With the dense deployment of small cells in the next generation of mobile networks, the users from different tiers suffer from high downlink interferences. In this paper, we propose a game theoretic approach for joint co-tier and cross-tier collaboration in heterogeneous networks and analyze the relevance of the proposed scheme. First, we propose a coalition structure game with a weighted Owen value as imputation, where the small-cell base stations (SBSs) and their connecting macrocell user equipments (MUEs) form a priori union. We prove that the proposed framework optimizes the users profit. As an additional global benefit, the SBSs are encouraged to host the harmed public users in their vicinity. Second, we propose a canonical game with a weighted solidarity value as imputation to allow cooperation among SBSs and MUEs when they fail to connect to nearby SBSs. We prove that the weak players are protected in this scheme and that a high degree of fairness is provided in the game. We compare through extensive simulations the proposed frameworks with state-of-the-art resource allocation solutions, access modes, and legacy game-theoretic approaches. We show that the proposed framework obtains the best performances for the MUEs and small-cells user equipments in terms of throughput and fairness. Throughput gain is in order of 40% even reaching 50% for both types of users.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".