Optimal Receiver Placement in Distributed Passive Sensor Networks: A DRL Approach
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Bibliographic record
Abstract
This paper addresses the optimal placement of Receivers (Rxs) in Distributed Passive Sensor Networks (DPSNs) to enhance the received Signal-to-Noise Ratio (SNR) in critical sub-regions within the surveillance area. We aim to maximize the average weighted SNR while ensuring a high minimum SNR across the entire area. We formulate a bi-objective optimization problem, which is solved numerically using Multi-Objective Genetic Algorithms (MOGA). Additionally, we develop a Deep Reinforcement Learning (DRL) framework using Proximal Policy Optimization (PPO) that learns optimal Rx placement strategies by balancing the dual objectives. Numerical simulations demonstrate that our approach effectively determines optimal Rx placements, enhancing SNR in high-priority areas while maintaining robust coverage and a uniform SNR distribution across the surveillance area.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it