Capacity-optimal relay and base station placement in wireless networks
Bibliographic record
Abstract
In this paper, we consider a finite geographic area with multiple mobile stations (MSs) uniformly distributed within the area and multiple candidate locations (CLs) for deploying base stations (BSs) and relay stations (RSs) to serve the MSs. For this network scenario, we study the joint optimal placement of BSs and RSs into those CLs, and MS and RS power allocations such that the sum-capacity of the network is maximized while the target data rate of each MS is achieved. In order to investigate the energy-efficiency trade-offs between deploying BSs and RSs, we provide an iterative algorithm which first maximizes the sum-capacity of the network by optimally deploying a certain number of BSs. Then, the algorithm decreases the number of BSs to be deployed optimally by one and continues deploying RSs until the same sum-rate is achieved. The process continues until the number of optimally deployed BS is 1 and the number of optimally deployed RSs is less than or equal to the total number of candidate RS locations. Our numerical results suggest that significant gains in terms of reduction of total transmitted power can be obtained by replacing BSs with RSs. However, this gain diminishes when the number of BSs became too small which makes the BS-RS and RS-MS distances too large for energy efficient communications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".