Resource Allocation and Interference Management in Heterogeneous Wireless Networks
Bibliographic record
Abstract
Heterogeneous networks (HetNet) are a promising solution to improve network performance in terms of spectrum efficiency and energy efficiency. Nevertheless, HetNets suffer from two main sources of interference: mutual interference between macrocells and small cells (called cross-layer interference), as well as inter-cell interference among small cells themselves (called co-layer interference). In this thesis, we study the resource allocation and interference issues of HetNets. First, in HetNet systems with a moderate number of small cells, we integrate two popular approaches: spectrum avoidance and spectrum sharing, using optimization and game theory. In this solution, small base stations (SBSs) opportunistically avoid the parts of the spectrum that are used by macro BS (MBS), thereby controlling cross-layer interference, while the co-layer interference is controlled using a spectrum sharing technique. We then exploit recent advances in the mean-field game theory in order to control the co-layer interference between a large number of small cells. Meanwhile, a spectrum avoidance technique is applied to control the cross-layer interference. Next, we design a full spectrum sharing technique based on the mean-field game theory for interference management in hyper dense HetNet systems. The joint cross-layer and co-layer interference management issue is formulated as two nested problems, which are solved via distributed algorithms. Furthermore, tools from the optimization theory are employed to enhance the performance of cell edge users in open access HetNets. Simulation results are presented to show the effectiveness of the proposed schemes. education. Special thanks goes to my sisters who supported me throughout my life.
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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".