Distributed clustering and interference avoidance in cognitive femtocell networks
Bibliographic record
Abstract
The concept of a cellular network overlaid with Femtocell Access Points (FAPs) has emerged as a new wireless architecture that ensures high data rates and reliable coverage for indoor users. However, the spatially random installation of FAPs and lack of coordination between the FAPs and cellular tiers results in co-channel interference which limit the system performance. This paper designs an interference avoidance scheme for cognitive femtocell networks based on a hierarchical architecture that exploits clustering. Each FAP senses the licensed spectrum to find “blank spaces”, measures its mutual similarity with only its neighboring FAPs, and forms simple messages which serve as incentives for active FAPs to partition into coalition clusters. Then, the chosen cluster head coordinates its members' transmissions. Cluster formation is updated on large time scales and is not susceptible to the instantaneous channel variations, thereby reducing the overhead in real-time communication. Our proposed scheme not only minimizes cross-tier interference, but also maximizes indoor data rates. Numerical results show that the proposed distributed scheme outperforms (centralized) spectral clustering algorithm for different number of FAPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".