Real-Time PIM Mitigation in Cellular Networks: A Reinforcement Learning Approach
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Bibliographic record
Abstract
Passive intermodulation (PIM) is a persistent issue in cellular networks, impacting uplink (UL) frequencies in multicell systems. As next-generation networks advance with higher frequencies and increased complexity, mitigating PIM becomes essential to maintain signal integrity. This paper introduces a reinforcement learning-based approach to PIM avoidance that dynamically allocates downlink (DL) resources to reduce selfinterference. By adapting to dynamic traffic conditions and incorporating buffer status into the decision-making process, the proposed method effectively balances UL and DL traffic priorities. Numerical results demonstrate the method's ability to mitigate PIM without impacting overall traffic performance, making it a robust solution for both current and future network deployments.
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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 | 0.000 | 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 | 0.001 |
| 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 it