Joint location and power optimisation of femto base stations to improve indoor coverage: a geometric approach
Bibliographic record
Abstract
Abstract Femto base station (FBS) deployment with existing Macro Base Stations improves Quality of Service of end users in dead‐zone while simultaneously increasing co‐channel femto–femto interference. This necessitates judicious planning before FBS deployment. In this paper, a novel geometric approach to model any 3‐D deployment region along with two particle swarm optimisation based joint location and power optimisation algorithms: LOA‐POA and LPOA are proposed. The geometric plan of the deployment region (with multiple multistoried buildings) with 3‐D coordinates of serving‐MBS has been generated and provided as input to LOA‐POA and LPOA, to identify positions and transmission powers of the FBSs required to maximise coverage. For a significantly large deployment region of 1600 sq. m., the LPOA identifies locations and transmission powers of four co‐channel FBSs within 140 s while LOA‐POA identifies the same for five FBSs within 40 s. Simulation exhibits requisite coverage after FBS installation. Comparison of these algorithms has been carried out with existing works considering different wall materials. LPOA is observed to provide most economic solution but with higher convergence time than LOA‐POA. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| 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".