Distributed clustering and interference management in two-tier networks
Bibliographic record
Abstract
Employing centralized resource management schemes is generally infeasible in large-scale networks. The deployment of heterogeneous Femtocell Access Points (FAPs) over the cellular licensed spectrum is therefore challenging. In particular, the resulting inter-node interference inhibits the network performance. In this paper, we design a hierarchical, distributed, interference management scheme that exploits the benefits of clustering. First, in order to reduce the cross-tier interference, each FAP independently identifies vacant subbands for potential transmission. Then, by exchanging some simple messages with its immediate neighbors in an iterative fashion, coalition clusters are formed. Given the small population of each group, centralized resource management is subsequently performed to avoid intra-cluster interference. Different clusters, however, may still share a fraction of common idle channels, which degrades system performance. Therefore, this paper further considers inter-cluster interference management to determine the set of privileged FAPs that can share a subband via solving a binary power control optimization problem. While the optimal solution requires prohibitive complexity, this paper provides tight bounds on the sum rate of the binary power control problem. The simulation results show that, in a high interference regime, inter-cluster coordination provides a significant performance improvement compared to the case of no 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 | 0.001 |
| 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".