Simulated annealing based joint coverage and capacity optimization in LTE
Bibliographic record
Abstract
Self-Organizing Networks (SON) is an advancing technology in wireless communication systems, which employs various algorithms to improve the network performance and reduce the network maintenance cost. The SON is an integral part of the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) standard. The SON processing can be centralized, distributed or a hybrid of these two schemes. In this paper, we consider a centralized LTE SON and provide a mechanism to improve both the coverage and capacity of the LTE network by tuning two parameters at each base-station (eNB) - a) transmit power and b) antenna electrical downtilt. Since the process of finding the optimal values for these parameters is highly complex owing to the large solution space, we propose to use the popular machine-learning technique of simulated annealing to solve this problem. We show that our mechanism results in more than 34% increase in network coverage and 21% increase in network capacity when compared to the scheme of using average values for eNB transmit power and antenna electrical downtilt. Also, our simulated annealing based solution is 4dB more power efficient than the aforementioned scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".