Spectrum sharing model for OFDMA macro-femtocell networks
Bibliographic record
Abstract
Enhancing the network throughput while supporting non-uniform user distribution and dense femtocell deployment is a challenge in OFDMA networks. Previous research works focus on spectrum partitioning or spectrum sharing with interference managements regardless the user distribution or mobility. In this paper, we propose a spectrum sharing approach that maximises the network throughput using Linear Programming. Power adaptation is performed to mitigate the interference and to guarantee QoS downlink transmissions. Our solution is able to: 1) fairly allocate resources to each macrocell zone taking into account the user distribution; 2) optimally reuse the bandwidth allocated to inner MC zones inside femtocells located in outer zones; 3) optimally determine the serving base station, subcarriers and transmitted power for downlink transmissions taking into account user locations and demands. Simulations are conducted to show a comparison of our solution with two SP approaches with and without partial bandwidth reuse incorporating user mobility.
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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".