A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks

Pujitha Mamillapalli; Pankaj Singh Rathour; Abhinav Kumar

cs.DBSep 30, 2026

A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks

Authors: Pujitha Mamillapalli, Pankaj Singh Rathour, Abhinav Kumar

Organizations: Department of Artificial Intelligence Indian Institute of Technology Hyderabad, India · Department of Electrical Engineering Indian Institute of Technology Hyderabad, India

Abstract

Reconfigurable Intelligent Surfaces (RIS) are emerging as a key technology for programmable wireless environments in the beyond the fifth generation (B5G) networks. However, data-driven RIS research remains bottleneck by the lack of standardized, high-fidelity and open-source datasets. In this paper, we introduce a large-scale 3GPP TR 38.901-compliant dataset for RIS-aided millimeter wave (mmWave) networks, that considers severe path loss, blockage sensitivity, and spatial channel sparsity make the RIS assistance more impactful. The dataset spans various canonical 3GPP deployment scenarios across 20 controlled variants, capturing diverse user densities, fading conditions, and blockage regimes. Uniquely, every sample includes oracle RIS phase configurations obtained via a globally optimal brute-force codebook search, providing gold-standard supervision labels that are absent from any existing public dataset. Rich multi-task annotations comprising full channel state information (CSI), per-link channel decomposition, optimal phase matrices, and channel quality index (CQI) labels support a broad range of machine learning paradigms and downstream tasks, including phase optimization, channel estimation, and interference management. As the primary benchmark task, we introduce a novel CSI-to-CQI mapping that frames RIS-aided link-quality prediction as a scalable scalar classification problem, thereby avoiding the exponential output complexity of the direct phase vector prediction. We have evaluated this mapping against state-of-the-art architectures under in-distribution, out-of-distribution, and real-world hardware measurement conditions. Our dataset provides a reproducible, extensible, and community-ready foundation to accelerate data-driven research in RIS-aided B5G networks.

Figures & tables

Work	Channel Model	Frequency	Multi- scenario	3GPP TR 38.901	RIS CM	Oracle Labels	RW Meas.	Dataset Avail.
Stochastic channel models
QuaDRiGa [ 21 ]	GBSCM	Sub-6/mmWave	✓	✓	✗	✗	✗	✗
COST 2100 [ 28 ]	GBSCM	Sub-6 GHz	✓	✗	✗	✗	✗	✗
Ray tracing frameworks
DeepMIMO [ 4 ]	RT	mmWave/sub-6	✓	✗	✗	✗	✗	✓
Raymobtime [ 24 ]	RT + traffic sim	60 GHz	✗	✗	✗	✗	✗	✓

Table 1: Comparison of related works on RIS datasets and simulation frameworks. GBSCM = geometry-based stochastic channel model, RT = Ray Tracing, CM = Channel Modeling, RW Meas. = Real World Measurements and Avail. = Availability.

Figure 1: Overview of the proposed CSI-to-CQI mapping framework for RIS-aided mmWave networks.

Figure 2: Cumulative variance explained as a function of the number of principal components (PCs). The solid curve denotes the average across all considered variants, while dashed horizontal lines indicate variance thresholds (

50\%,75\%,90\%~\text{and}~95\%

). The intersection points highlight the minimum number of principle components (PC) required to achieve each threshold.

Model	Train acc $\uparrow$	Val acc $\uparrow$	Params $\downarrow$	MAE $\downarrow$	GPU $\downarrow$	Time/epoch $\downarrow$
FNN [ 31 ]	85.6%	85.4%	0.45 M	0.277	1.2 GB	0.5 min
CNN [ 26 ]	83.86%	82.54%	2.8 M	0.295	1.8 GB	0.4 min
XGBoost [ 7 ]	89.9%	86.7%	–	0.2456	–	20 min
LR [ 12 ]	68.43%	68.60%	$\sim$ 5 K	0.412	–	1 min
SVM [ 39 ]	81.92%	81.17%	–	0.310	–	25 min
Transformer [ 27 ]	84.8%	84.2%	33.3 M	0.309	3.9 GB	1.2 min

Table 2: Training and validation performance comparison. Best values per column are highlighted in bold . Acc = Accuracy, MAE = Mean Absolute Error, Params = Number of trainable parameters, GPU = GPU memory usage, Time/epoch = Training time per epoch.

Set	Description	$D_{\mathrm{KL}}$ $\uparrow$	$\mathcal{W}_{1}$ $\uparrow$	MMD $\uparrow$
T1	AWGN	3.320	0.533	0.080
T2	Bursty	2.859	0.534	0.060
T3	Memory AR(1)	3.294	0.533	0.070
T4 [ 33 ]	Hardware imp.	8.741	1.872	0.284

Table 3: Train–test distribution shift via

D_{\mathrm{KL}}

\mathcal{W}_{1}

, and MMD.

\uparrow

indicates larger shift from the training distribution, imp. = impairments.

Model	T1	T2	T3	T4
LR [ 12 ]	25.2%	21.4%	23.1%	18.6%
SVM [ 39 ]	22.3%	28.7%	20.5%	12.4%
XGB [ 7 ]	39.5%	35.2%	37.8%	20.1%
CNN [ 40 ]	52.3%	55.4%	58.9%	28.2%
FNN [ 31 ]	61.4%	64.2%	66.8%	31.3%
Transformer [ 27 ]	11.9%	10.0%	10.6%	9.8%

Table 4: Test accuracy (%) across T1–T4. Best in bold , FNN+FT: fine-tuning of the trained FNN model for 100 epochs.

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