Distributed heuristic adaptive power control algorithms in femto cellular networks for improved performance
Bibliographic record
Abstract
Abstract Adaptiveness of femto base station's (FBS's) transmission power (TP) is crucial in determining Quality of Service and ecofriendliness of a network. Joint power optimization and admission control problems have been reformulated to identify the TP of v already deployed FBSs, and the problem is shown to be computationally hard. Accordingly, an adaptive distributed heuristic called power search algorithm (PSA) is proposed. A learning algorithm analytically identifies the received signal strength‐based coverage of each FBS in all directions during planning. A 3‐dimensional reference matrix, ie, REF , for each FBS, is thereby formulated and stored in the corresponding FBSs. Power search algorithm handles call admission/termination at run time. For call admission, PSA identifies the serving FBS and the required minimum TP. A suitable data structure is also maintained by PSA for efficient call handling. A new call is dropped if its admission degrades the Quality of Service of existing end‐users. For call termination, change in TP is triggered if the highest power level of the FBS was being exercised to serve this particular user. The worst‐case run‐time complexity of PSA turns out to be O(log 2 N ), where N is the number of TP levels of a particular FBS. Comparing with existing heuristics, improved performance of PSA includes highest throughput and signal‐to‐interference‐plus‐noise ratio while minimizing starvation and cumulative TP with lower complexity even for high end‐user density. Exhaustive simulation reveals that the probability of an end‐user having signal‐to‐interference‐plus‐noise ratio above 0 dB is 0.83 even in the worst case scenario. Accordingly, the proposed PSA is claimed to be superior.
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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 | 0.001 |
| 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 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".