Coordinated channel selection in cognitive macro-femto networks
Bibliographic record
Abstract
In this paper, we study uplink channel selection in a system where a macro base station (MBS) and a number of cognitive femto base stations (FBSs) share the same spectrum to serve their intended users with quality of service (QoS) requirements. In this system, the MBS may experience significant aggregate interference when multiple FBSs select the same channel to serve their FUs. An FBS also experiences strong interference from nearby femtocells if the same channel is utilized by adjacent FBSs. We investigate how to coordinate the channel selection at FBSs to reduce the interference experienced at the MBS and FBSs. We propose a cluster-based coordination mechanism where the operator groups the set of FBSs into clusters, and FBSs in the same cluster can utilize a set of channels simultaneously without violating the QoS requirements. To find the desired clusters, we employ a graph-theoretic approach and propose an efficient FBS clustering scheme. Simulation results show that the proposed coordination mechanism achieves a better performance compared to the channel selection scheme without coordination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".