Simulation Results for RIS-Assisted UAV THz Communication Optimization

Figshare hosts simulation results from a study proposing a RIS-assisted UAV positioning framework to enhance Terahertz communication. The work, by M. Rudra Kumar, demonstrates gains in link robustness and achievable data rate using a reinforcement learning strategy. The dataset is a 124.5 KB ZIP file containing simulation outputs.

Use Cases

• Analyze the impact of RIS configuration and UAV deployment on achievable data rate from simulation results.
• Benchmark link robustness and user connectivity metrics against conventional THz systems without RIS or UAV assistance.
• Validate the reinforcement learning strategy's performance in optimizing RIS phase-shift adaptation and UAV positioning.

Strengths

• Simulation results are validated against conventional THz systems, demonstrating consistent performance gains.
• The dataset is openly shared under a CC BY 4.0 license, facilitating reuse and verification.
• The underlying research proposes a novel, unified framework for RIS-UAV coordination in THz networks.

Limitations

• The dataset is very small at 124.5 KB, indicating limited scope and likely containing only summary or illustrative results, not raw simulation logs.
• The specific data structure, column names, and row count are unknown, complicating direct analysis without inspection.
• As simulation data, its applicability is limited to validating the proposed model and may not reflect real-world channel conditions.

Provenance

Source

M. Rudra Kumar via Figshare

Collection Method

Simulation results from a proposed reinforcement learning-based optimization framework.

Freshness

Last updated March 2026.