Hardware Resource Consumption for 64 Users in 6G AI-Native Receiver Research

A 5.5 KB Excel file containing hardware resource consumption data for a simulated 64-user scenario. The dataset supports research on a Deep Unfolding Successive Over-Relaxation (DU-SOR) paradigm for 6G wireless systems, authored by Emmanuel Ampoma Affum and last updated on April 24, 2026. It was shared on figshare under a CC-BY-4.0 license.

Use Cases

• Benchmarking computational efficiency of AI-based beamforming methods based on the described scalable complexity reduction.
• Analyzing performance trade-offs in CSI-free operation across different channel conditions like Rayleigh, Rician, and 3GPP UMi.
• Validating mutual information scores for signal estimation models at specific SNR levels, such as 0.98 at 20 dB.
• Studying the resource implications of sparse Graph Transformer and iterative residual refining architectures for MU-MIMO systems.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Explicitly licensed under CC-BY-4.0, permitting broad reuse with attribution.
• Supports a novel, mathematically proven method with reported performance metrics like a mutual information score of 0.98 at 20 dB SNR.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale training.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset scope is limited to a specific K=64 user simulation, which may not generalize to other scales.

Provenance

Source

figshare, authored by Emmanuel Ampoma Affum.

Collection Method

Likely generated from empirical analyses or simulations of the proposed DU-SOR framework.

Time Range

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Freshness

Last updated 2026-04-24 17:27:56; freshness should be verified.

Geography

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