Cooperation-Based Interference Mitigation in Heterogeneous Cloud Radio Access Networks
Bibliographic record
Abstract
In this paper, we propose a cooperation framework in heterogeneous cloud radio access networks (H-CRANs) to mitigate inter-tier interference. Specifically, small cell remote radio head (S-RRH) acts as the cognitive relay for multiple macrocell users (MUEs) which are primary users, and obtains a fraction of time slot from multiple MUEs as a reward. Through the cooperation, the S-RRHs can obtain extra spectrum resource for serving secondary users-small cell users (SUEs), while the MUEs can improve their transmission rates. Moreover, the inter-tier interference between macrocell networks and small cell networks can be mitigated via cooperation. The cooperation problem is formulated as a binary integer programming problem which is NP-hard. To solve this problem, we transform it to an equivalent many-to-one matching problem. Then, we achieve the near optimal solution by proposing a two-sided cooperator selection algorithm, which takes the benefits of both S-RRHs and MUEs into consideration. Simulation results show that the performance of the macrocell networks as well as small cell networks can be improved by adopting the proposed scheme, and the cooperator selection result is stable and close to the optimal solution.
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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 | 0.002 |
| 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 | 0.002 | 0.002 |
| 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".