On Power-Line-Based Front-Hauling for IoT Cellular Indoor Communications
Bibliographic record
Abstract
This paper explores the usage of low-voltage Power-Line Communication (PLC) links for Enhanced Common Public Radio Interface (eCPRI)-based front-hauling in 5G Internet of Things (IoT) indoor mobile coverage environments, using a split Centralized Radio Access Network (C-RAN) architecture. This research aims to analyze how parameters such as wireless IoT device count, bandwidth, and transmission technology affect the delay performance of the proposed system. To achieve this goal, we develop detailed mathematical models that draw insights from queuing theory, stochastic geometry, and Markov models. Extensive system-level simulations verify these models’ accuracy, and the analytical results cover radio and access delay performance. We validate the system’s efficiency in supporting IoT indoor cellular applications and assess the feasibility of the proposed PLC-based front-hauling system, considering the strict delay requirements of the eCPRI standard.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 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 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".