Simulation Parameters for Graph-Guided Adaptive Companding in PD-NOMA Systems

Arun Kumar published simulation parameters for a novel Graph-Guided Adaptive Companding (GGAC) framework on figshare in May 2026. The dataset supports research into Peak-to-Average Power Ratio (PAPR) reduction in Power-domain Non-Orthogonal Multiple Access (PD-NOMA) systems for 5G and 6G networks. The parameters likely contain values used to model composite waveforms as graphs and evaluate PAPR, BER, SINR, and out-of-band radiation performance.

Use Cases

• Benchmarking PAPR reduction techniques based on the described gains of up to 10 dB over conventional PD-NOMA.
• Evaluating BER and SINR improvements in NOMA systems based on the described simulation results.
• Analyzing out-of-band radiation reduction based on the described improvement of more than 40 dB.
• Studying graph-based signal component modeling for adaptive companding based on the described node and edge representation.

Strengths

• Parameters are associated with a novel GGAC framework that demonstrated specific performance gains.
• The dataset is openly licensed under CC-BY-4.0, allowing for reuse and modification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small (5.5 KB), indicating a limited scope of parameters.

Provenance

Source

figshare

Freshness

Last updated 2026-05-21 17:37:56; freshness should be verified.