Interference management for multimedia femtocell networks with coalition formation game
Bibliographic record
Abstract
Recently, the multimedia content delivery has replaced the traditional voice communication as the major source of traffic in wireless networks. The deployment of femtocells is promising in satisfying the requirements of these multimedia applications if the interference among the femtocell access points (FAPs) is well-managed. In this paper, we study the interference management problem of the FAPs in a cooperative multimedia femtocell network. We consider the network setting where the players (i.e., the FAPs) can coordinate their transmissions to reduce the level of interference within a coalition. We first formulate the interference management problem as a coalition formation game in partition form with negative externalities, where the payoff of a player depends on actions of other players in the same coalition and in different coalitions. Based on the solution concept of recursive core in coalitional games, we propose an efficient coalition formation algorithm, RECORD, to achieve a final stable coalition structure. Simulation results show that the RECORD algorithm results in a substantially higher flow throughput and aggregate utility than some previously proposed scheduling 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".