Simulation Parameters for RIS-Assisted UAV THz Communication

Simulation parameters support research on Terahertz (THz) communication enhanced by reconfigurable intelligent surfaces (RIS) and unmanned aerial vehicles (UAVs). The data, authored by M. Rudra Kumar and shared under CC BY 4.0, models a reinforcement learning-based framework for optimizing RIS configuration and UAV deployment.

Use Cases

• Calibrate simulation models for THz path loss and molecular absorption using the provided parameters.
• Benchmark reinforcement learning strategies for RIS phase-shift adaptation and UAV positioning.
• Validate achievable data rate and user connectivity gains in RIS-assisted UAV network simulations.

Strengths

• Parameters are derived from a published research framework proposing a coordinated RIS-UAV optimization method.
• The dataset is openly shared under a permissive CC BY 4.0 license.
• It specifically models THz channel impairments like severe path loss and high sensitivity to blockage.

Limitations

• The dataset is very small at 95.3 KB, indicating limited scope and likely a parameter set rather than extensive simulation results.
• No sample data or column definitions are provided, making the exact structure and variables unknown.
• The data is simulated, not empirical, and its applicability to real-world deployments is untested.

Provenance

Source

M. Rudra Kumar via figshare

Collection Method

Simulation parameters generated for a proposed RIS-assisted UAV positioning (RAVP) framework.

Freshness

Last updated March 2026, indicating it is forward-dated and likely represents planned or in-progress research.