Simulated THz Network Performance With and Without RIS Assistance

Simulation data from a study on enhancing terahertz (THz) communication networks using Reconfigurable Intelligent Surfaces (RIS) and Unmanned Aerial Vehicles (UAVs). The work, by M. Rudra Kumar, proposes a reinforcement learning-based framework for joint RIS configuration and UAV deployment, showing gains in link robustness and data rate. The dataset, last updated in March 2026, is a 34.6 KB ZIP file containing results from this simulation study.

Use Cases

• Compare link robustness metrics between RIS-assisted and conventional THz network configurations.
• Analyze achievable data rate improvements from optimized UAV deployment and RIS phase-shift adaptation.
• Validate reinforcement learning models for user grouping and UAV coordination in simulated wireless environments.
• Benchmark path loss and propagation conditions under different simulated network topologies.

Strengths

• Data originates from a peer-reviewed simulation study explicitly modeling THz, RIS, and UAV interactions.
• Results are recent, with the dataset last updated in March 2026.
• The work demonstrates consistent gains in key performance indicators like link robustness and achievable data rate.

Limitations

• The dataset is very small at 34.6 KB, indicating limited scope and likely a summary of results rather than raw simulation logs.
• As simulation data, its applicability to real-world THz channel conditions and hardware constraints is unverified.
• Specific data formats, column definitions, and the volume of individual data points are not described.

Provenance

Source

Figshare, authored by M. Rudra Kumar.

Collection Method

Generated from simulations of a proposed RIS-assisted UAV positioning (RAVP) framework.

Freshness

Last updated 2026-03-23.