mmWave RAN Coverage Feasibility Reasoning Dataset
#logical reasoning #coverage feasibility assessment #link budget analysis #limiting factor identification #mmWave network planning #wireless coverage assessment #link budget analysis #network deployment decisions
- 6540
- 0.4GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-10-27
AI Analysis & Value Prop
This dataset presents mmWave RAN coverage scenarios as question-and-answer examples, with inputs such as carrier frequency, transmit power, antenna gains, propagation loss, obstruction, coverage distance, and coverage targets. It includes feasibility judgments, limiting factors, and expert reasoning grounded in link-budget and propagation analysis. Use it to train or evaluate models that quantify coverage requirements, identify constraints, and provide evidence-based conclusions.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| expert_answer | string | A concise final answer to the original coverage feasibility question. |
| frequency_ghz | number | The operating carrier frequency, measured in gigahertz. |
| link_margin_db | number | The predicted received power minus receiver sensitivity, measured in dB. |
| expert_reasoning | string | A step-by-step explanation linking the link budget, propagation conditions, obstruction, and coverage target to the feasibility judgment. |
| limiting_factors | array | The key factors that constrain or prevent the required coverage. |
| coverage_feasible | boolean | Whether the stated coverage requirement is feasible under the supplied assumptions. |
| scenario_question | string | The original question describing the mmWave RAN coverage scenario to assess. |
| transmit_eirp_dbm | number | The transmitter's effective isotropic radiated power, measured in dBm. |
| coverage_target_pct | number | The required percentage of locations or users that must receive coverage. |
| obstruction_loss_db | number | The additional loss attributed to blockage or other obstructions, measured in dB. |
| rx_antenna_gain_dbi | number | The receive antenna gain, measured in dBi. |
| tx_antenna_gain_dbi | number | The transmit antenna gain, measured in dBi, when supplied separately from EIRP. |
| estimated_path_loss_db | number | The estimated propagation loss between the transmitter and receiver, measured in dB. |
| propagation_conditions | string | The stated propagation context, such as line of sight, non-line of sight, indoor, or outdoor conditions. |
| receiver_sensitivity_dbm | number | The minimum received power required by the receiver, measured in dBm. |
| predicted_received_power_dbm | number | The received power estimated from the supplied link-budget inputs, measured in dBm. |
| required_coverage_distance_m | number | The distance the deployment is required to cover, measured in meters. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What does the dataset contain?
- Examples describe mmWave RAN coverage scenarios and include inputs such as frequency, transmit EIRP, antenna gains, receiver sensitivity, path loss, propagation conditions, obstruction loss, and coverage requirements. They also provide predicted received power, link margin, feasibility judgments, limiting factors, and expert reasoning.
- Which training and evaluation tasks does it support?
- The dataset supports logical reasoning, link-budget analysis, coverage feasibility assessment, and limiting factor identification. It can be used for reasoning SFT or to evaluate whether a model can draw sound conclusions from radio parameters and propagation conditions.
- How is data quality represented?
- Key inputs, calculated results, and final judgments are presented in a structured format, with expert reasoning that connects conclusions to link budgets, propagation conditions, and coverage targets. Refer to the dataset listing for specific details on volume, value ranges, and quality metrics.
- How can I access the data, and what are the licensing terms?
- Check the dataset listing for available formats, access methods, and licensing terms. Before use, confirm that the license covers your intended purposes, such as model training, internal evaluation, or commercial deployment.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={mmWave RAN Coverage Feasibility Reasoning Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/85b3644fc51073297c78a744cbf22095},
urldate={2026-10-26},
keywords={mmWave RAN dataset,mmWave coverage analysis,wireless coverage feasibility,link budget reasoning,propagation loss assessment,obstruction loss analysis,wireless network planning,telecom logical reasoning},
version={1.0}
}Using this in research? Please cite us.
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