Efficient user and power allocation in femtocell networks
Bibliographic record
Abstract
In this paper we consider the problem of user assignment and power allocation in a small cell environment which is one of the most important problems in present wireless cellular network research. We consider a two-tier cellular network where randomly dispersed overlay femtocell base stations (FBSs) coexist with a macrocell. Our objective is to maximize the total number of users served by the FBSs while satisfying their signal to noise and interference (SINR) requirements. This problem is known to be NP-Hard and hence there is no known optimal solution to solve it in polynomial time. First we formulate the problem of maximization of allocated users under SINR constraints with constant transmit power as an integer programming problem. We provide two heuristic polynomial time algorithms. Then we propose a third algorithm for joint power and user allocation. We evaluate the complexity of the proposed algorithms and furthermore compare the results against the brute force optimal solution and a basic random user assignment through simulations. The results demonstrate the performance and the efficiency of the proposed algorithms. We see in the simulation that the best proposed heuristic for maximizing the number of assigned users is only 3% less than the optimal while reducing the power consumption below that of the optimal user assignment algorithm.
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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".