Interference management using cognitive base-stations for UMTS LTE
Why this work is in the frame
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Bibliographic record
Abstract
In this article we demonstrate the benefits of developing cognitive base-stations in a UMTS Long Term Evolution (LTE) network. Two types of cognitive base-stations are considered: the macro-cell evolved-NodeB (eNB) and the femtocell Home evolved NodeBs (HeNB). In the context of an isolated cell or a multi-cell LTE network, the insufficiency of traditional interference management schemes is shown. Implementation of cognitive tasks such as radio scene analysis and dynamic resource access are then introduced. We argue that such cognitive basestations can exploit their knowledge of the radio scene to intelligently allocate resources and to mitigate prohibitive Co-Channel Interference (CCI). Given the distributed architecture of LTE networks, we will elaborate on cognitive interference mitigation solutions and further propose two different Game Theoretical mechanisms to achieve CCI mitigation in a distributed manner.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it