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

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

arXiv:2609.39058v1 [cs.DB] 30 Sep 2026

Pujitha Mamillapalli Department of Artificial Intelligence Indian Institute of Technology Hyderabad, India

Pankaj Singh Rathour Department of Electrical Engineering Indian Institute of Technology Hyderabad, India

Abhinav Kumar 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.

1

Introduction

The convergence of massive device proliferation, immersive applications, and stringent latency requirements is pushing wireless infrastructure past its conventional limits in beyond the fifth generation (B5G) networks [1]. Reconfigurable intelligent surfaces (RIS) — planar arrays of independently tunable, passive or semi-passive electromagnetic elements — have emerged as a key technology. Unlike traditional wireless equipment, it can program how signals travel in real time — without needing power amplifiers or other complex radio hardware. [2]. By programming the signals direction, a properly set up RIS can reach areas with poor coverage, reduce signal interference between users and improve overall signal strength. Thus the RIS can transforms an otherwise uncontrollable stochastic Preprint.

channel into a deterministic and programmable resource at a fraction of the energy cost of active alternatives [3, 4]. However, optimizing RIS configurations remains computationally challenging, particularly in large-scale deployment scenarios, where the high-dimensional, non-convex nature of the problem significantly increases complexity [5]. Classical model-based iterative solvers — alternating optimization [6], successive convex approximation [4], and manifold optimization [7] — offer convergence guarantees but scale poorly with high computation cost rendering them impractical under blockage. These limitations have catalyzed a growing research on machine learning (ML)-driven approaches, spanning deep reinforcement learning [8, 9], supervised codebook prediction [10, 11], and unsupervised learning [12], all promising real-time inference and robustness to model mismatch [12]. However, the progress is severely hampered by a fundamental bottleneck: the absence of realistic, standardized and large-scale datasets [13]. Existing works predominantly rely on privately generated, scenario-specific datasets built on simplified channel models that neither conform to 3GPP propagation standards [14] nor provide sufficient diversity to support generalization. Furthermore, the absence of quality labels foreclose rigorous evaluation of supervised and imitation-learning approaches, collectively imposing a substantial hindrance on the field. To address these limitations, we introduce a large-scale 3GPP TR 38.901-compliant dataset for RIS-aided mmWave networks as a community benchmark. Our dataset spans three canonical deployment scenarios: Urban Macro (UMa), Urban Micro (UMi), and Indoor Hotspot (InH), across 20 controlled variants capturing diverse user densities, fading levels, and blockage probabilities per 3GPP TR 38.901 [14]. Each data sample carries channel state information (CSI), per-link channel decomposition and most distinctively, RIS phase labels obtained via a globally optimal brute-force codebook search [5, 15]. These labels provide standard supervision targets which are not available in many existing dataset. We benchmark our dataset on a novel CSI-to-channel quality indicator (CQI) mapping task using a suite of state-of-the-art architectures under in-distribution, out-of-distribution, and real-world measurement conditions [16, 17], establishing reference performance numbers for the community. The contributions of this work are as follows: • Large-scale 3GPP-compliant dataset. A dataset generated using TR 38.901 [14] that spans various scenarios across 20 controlled variants with diverse fading models, user densities, and blockage regimes. • Richly annotated dataset. Optimal RIS phase configurations obtained via exhaustive codebook search provide gold-standard supervision, complemented by comprehensive multi-task annotations—including full CSI with per-link decomposition, beamforming vectors, optimal phase matrices, and CQI labels—supporting a wide range of supervised, unsupervised, and reinforcement learning tasks. • Novel CSI-to-CQI mapping framework. We introduce a novel framework that models RIS phase optimization as a direct mapping from CSI to a scalar CQI, avoiding the exponential output complexity of phase vector prediction while yielding an operationally meaningful link-adaptation target. • Benchmarking and generalization analysis. Comprehensive evaluation of diverse learning paradigms—including deep neural networks, sequence modeling approaches, and classical machine learning methods—with a focused analysis of model generalization under indistribution, out-of-distribution, and real-world hardware measurement conditions.

2

Related Work

In this section, we survey the prior literature on RIS-aided systems, including simulation datasets, real-world measurements, and learning-based phase optimization, and highlight limitations in existing datasets that motivate the proposed approach. Machine learning (ML) methods are increasingly replacing classical iterative solvers for real-time RIS phase configuration. Prior work spans supervised codebook prediction [10, 11], convolutional neural network (CNN)-based feature learning [27], deep reinforcement learning (DRL) [8, 9], and transformer architectures [28, 29]. Classical optimization methods are theoretically grounded but suffer from high computational cost and large inference latency due to iterative updates [5, 6]. This limits their use in large-scale real-time systems. Learning-based approaches enable fast inference but introduce new challenges. DRL methods are sample-inefficient and often unstable in highdimensional action spaces [9]. Unsupervised methods remove the need for labels but lack guarantees 2

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. Work

Frequency

Multiscenario

3GPP TR 38.901

RIS CM

Oracle Labels

RW Meas.

Dataset Avail.

GBSCM GBSCM

Sub-6/mmWave Sub-6 GHz

✓ ✓

✓ ✗

✗ ✗

✗ ✗

✗ ✗

✗ ✗

RT RT + traffic sim RT (commercial) RT RT + calibration RT + 3GPP

mmWave/sub-6 60 GHz Configurable mmWave mmWave Sub-6/mmWave

✓ ✗ ✓ ✗ ✗ ✓

✗ ✗ ✗ ✗ ✗ ✓

✗ ✗ ✗ ✓ ✓ ✗

✗ ✗ ✗ ✗ ✗ ✗

✗ ✗ ✗ ✗ ✗ ✗

✓ ✓ ✗ ✓ ✗ ✓

sub-6 sub-6/mmWave sub-6

✗ ✗ ✗

✗ ✗ ✗

✓ ✓ ✓

✗ ✗ ✗

✓ ✓ ✓

✓ ✗ ✓

mmWave

✓

✓

✓

✓

✗

✓

Channel Model

Stochastic channel models QuaDRiGa [18] COST 2100 [19] Ray tracing frameworks DeepMIMO [13] Raymobtime [20] Wireless InSite [21] Bayraktar et al. [22] From Lab to DT [23] SionnaRT [24, 25]

Real-world RIS measurements Tewes et al. [26] Mamillapalli et al. [16] BRISC [17]

Measured Measured Measured

Ours

3GPP TR 38.901

of convergence to globally optimal solutions in non-convex settings [30, 31]. Supervised methods are stable and efficient but depend on high-quality oracle or near-optimal labels. Despite this progress, most works rely on privately generated single-scenario datasets without standardized channel models, 3GPP compliance, or oracle labels. This limits fair comparison and weakens conclusions on generalization to realistic deployments. Existing Datasets: Datasets such as DeepMIMO [32], Raymobtime [20], Wireless InSite [2], and the RT dataset [22] are typically tied to specific scene geometries, often generating deterministic channel realizations for fixed environments rather than samples from calibrated stochastic models. Geometry-based stochastic channel models (GBSCMs), including QuaDRiGa [18] and COST 2100 [19], provide greater statistical diversity and partial alignment with 3GPP models, but are primarily designed for conventional multiple-input multiple-output (MIMO) and focus on composite end-to-end channels, with limited support for explicit transmitter–RIS and RIS–receiver decomposition. Standards-aligned simulators such as Sionna [24] and SionnaRT [25] implement TR 38.901 and move toward 3GPP-compliant data generation; however, as summarized in Table 1, publicly available datasets based on these tools remain limited in multi-scenario coverage, RIS specific channel decomposition, and oracle labels. Real-world RIS measurement datasets remain limited in scope and scale. Prior works report indoor and outdoor measurements across sub-6,GHz and mmWave bands, demonstrating measurable SNR and coverage gains under practical hardware conditions [26, 17, 33–38, 16]. However, these datasets are typically small, limited to a few scenarios and frequencies, and show limited alignment with 3GPP standards. They also lack oracle phase labels, restricting their use for systematic machine learning benchmarking and generalization studies. Table 1 highlights three recurring limitations in existing datasets: (i) scenario diversity—most datasets focus on a single environment, limiting systematic evaluation; (ii) 3GPP compliance—few RISspecific datasets are generated under TR 38.901-aligned propagation settings at 28,GHz; and (iii) RIS channel modeling—limited availability of datasets with explicit per-link transmitter–RIS and RIS–receiver decomposition required for phase optimization. The proposed dataset is designed to address these aspects while additionally providing oracle phase labels and standardized open-source splits for reproducible benchmarking.

3

Preliminaries

System Model. In this paper, we consider a downlink MIMO RIS-aided wireless system comprising a base station (BS) equipped with N antennas, a passive RIS with L2 reflectingelements, and K single-antenna users. The RIS is modeled by a diagonal phase-shift matrix θ = ejθ1 , . . . , ejθL2 , 3

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

where θl (∈ [0, π]) ⊆ S. Here, S denotes a finite discrete phase codebook shared across all elements. 2 2 Let HBR ∈ CL ×N and gk ∈ CL ×1 denote the BS-to-RIS and RIS-to-user channels, respectively, and hd,k ∈ C1×N denote the direct BS-to-user link. The received signal at the k th users is given by   yk = gk† diag(θ)HBR + hd,k wk sk + IK + n. (1) where wk ∈ CN ×1 is the k th user beamforming vector , sk is the transmitted symbol vector to the k th user, Ik is the interference from other users, and nk ∼ CN(0, σn2 ) denotes additive white Gaussian noise (AWGN); where CN(·, ·) represents the complex Gaussian distribution with mean 0 and noise variance σn2 . Performance Metrics: In this paper, α-mean throughput is considered as the performance metric, which is expressed as follows [39]  1 ! 1−α K   1 X   (B ∗ Rk )1−α , α > 0, α ̸= 1,   |K| k=1 Tα (k, θ, H) = (2) 1 ! |K|  K  Y    B ∗ Rk , α = 1,  k=1

where α is the fairness parameter, B is the bandwidth, K is the total number of UEs, and Rk ≜ f (H, θ) is the achievable rate (Refer to supplementary material). RIS Phase Optimization Task and Label Generation. Given the composite channel state information (CSI) H = {HBR , G, Hd }, comprising the BS-to-RIS, RIS-to-UE, and direct BS-to-UE links respectively, the optimal RIS phase configuration is obtained by solving: θ ∗ (H) = arg max2 θ∈S L

K X

Tα (k, θ, H) ,

(3)

k=1

where Tα (k, θ, H) denotes the α-mean throughput of the k-th user (as in (2) under phase configuration θ [39], S is the discrete phase codebook, and L2 = φ is the number of RIS elements. The exhaustive search over S φ guarantees global optimality but incurs complexity O(|S|φ ), which grows exponentially with the number of elements. Beyond computational intractability at inference time, directly predicting θ ∗ ∈ S φ as a supervised learning target imposes an exponentially large structured output space, making the learning problem itself statistically and computationally prohibitive. To resolve both issues simultaneously, we propose mapping the oracle-optimal configuration to a scalar Channel Quality Indicator (CQI). Specifically, for each CSI realization H, the oracle first computes the globally P optimal phase vector θ ∗ (H) via ∗ exhaustive search, evaluates the resulting sum throughput Tα = k Tα (k, θ ∗ , H), and assigns a discrete CQI label via the standardized quantization function [40, 14]: c∗ (H) = Q(T∗ (H)) ,

c∗ ∈ {1, . . . , C},

(4)

where Q(·) : R+ → {1, . . . , C} partitions the throughput axis into C discrete levels aligned with 3GPP CQI table definitions [14, 40], and C denotes the total number of CQI levels. This formulation 4

2

collapses the output space from |S|L to C classes, reducing exponential structured prediction to efficient scalar classification while producing a label that is directly actionable by the BS scheduler without post processing. The resulting supervised learning problem is defined over the dataset D = {(Hi , c∗i )}m i=1 , where a model fΩ : H 7→ {1, . . . , C} with parameters Ω learns the CSI-to-CQI mapping from oracle-generated labels. The overall pipeline is illustrated in Fig. 1, where a ML model learns the CSI-to-CQI mapping from generated oracle labels. Learning Framework. The CSI-to-CQI mapping is formulated as an empirical risk minimization (ERM) problem [41]. Given m independently and identically distributed (i.i.d.) samples D = {(Hi , c∗i )}m i=1 drawn from an unknown channel distribution PH , the predictor fΩ : H → {1, . . . , C} minimizing: m 1 X fˆ = arg min ℓ(f (Hi ), c∗i ) , (5) f ∈F m i=1 where ℓ(·, ·) is the loss function [41, 42]. Since oracle labels c∗ are globally optimal by construction, any residual risk L(fˆ) = E[ℓ(f (H), c∗ (H))] reflects genuine model capacity or distributional mismatch rather than label suboptimality, providing a noise-free measure of the true performance ceiling.

4

Dataset Generation Pipeline

In this section, we mention the detail pipeline for Algorithm 1 Proposed Dataset Generation the dataset generation. The pipeline is identical Pipeline across all 20 variants; only the channel configu- Require: Configuration cfg (fading, user density, ration cfg varies between runs, ensuring that all topology, blockage, bandwidth, carrier freinter-variant differences arise solely from conquency), codebook S trolled changes in propagation parameters. Al- Ensure: Channels {HBR , G, HD }, optimal gorithm 1 summarizes the end-to-end procedure; phase θ ∗ , CQI label c∗ full implementation details, 3GPP TR 38.901 1: Sample UE positions and blockage states acmodel parameters [14], are provided in supplecording to cfg mentary material. 2: Compute large-scale fading (3GPP TR 38.901) Topology sampling: For dataset generation, we assumed the system model as mentioned in 3: Generate small-scale channels HBR , G, HD Section 3. The BS and RIS are fixed at prede- 4: Initialize T∗α ← −∞ termined locations. UEs are distributed as a ho- 5: for each θ ∈ S N do mogeneous Poisson point process (PPP) of den- 6: Compute effective channel: Heff = sity λk (UEs/km2 ) on a disc of radius R centred G diag(θ) HBR + HD at the BS, yielding K ∼ Poisson(λk A) users 7: Compute beamforming matrix W using within the user deployment area A. Inter-cell MMSE-ZF beamforming interference is modeled via a single-tier hexago- 8: Calculate the aggregate throughput of the nal grid with inter-site distance (ISD) per 3GPP system Tα as in 2 TR 38.901 [14]; each interfering BS transmits 9: if Tα > T∗α then at full power with path-loss computed under the 10: T∗α ← Tα (θ) same TR 38.901 model as the serving link. 11: θ∗ ← θ Large-scale fading: LOS/NLOS states are as- 12: end if signed via distance-dependent probability func- 13: end for tions and path-loss with log-normal shadowing, 14: Compute CQI label: c∗ ← Q(T∗α ) and is computed as per 3GPP TR 38.901 [14]. 15: Store sample Channels HBR , G, and HD are generated per 3GPP TR 38.901 [14] under the variant-assigned 2 fading model and stored in explicit per-link form. For each θ ∈ S L , the effective channel Heff (θ) = G diag(θ) HBR + HD is formed, a zero forcing (ZF) beamforming [43] W(θ) is calculated and Tα (k, θ) is evaluated [44]. The optimal θ ∗ is obtained via (3), and three oracle quantities are stored per sample: the optimal phase vector θ ∗ , the CQI label c∗ ∈ {1, . . . , C}, and the optimal aggregate α-mean throughput T∗α . Reproducibility. All random number generators are explicitly seeded, and the pipeline executes on deterministic GPU kernels using 64-bit complex arithmetic throughout. System Parameters. The dataset targets mmWave RIS deployments at 28,GHz over a 100×100, m2 area, with a BS equipped with N = 36 antennas, a 30 × 30 RIS, and K = 100 users. UE locations are 5

Cumulative Variance of the PCs (%)

100

PC93 PC86

PC65

75

PC35

50

Average 50% Variance 75% Variance 90% Variance 95% Variance

25

0

0

5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Number of the PC

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% and 95%). The intersection points highlight the minimum number of principle components (PC) required to achieve each threshold.

uniformly sampled within scenario-specific bounds to ensure spatial diversity. It comprises 200,000 channel realizations across 20 deployment variants (10,000 each), with each sample represented by a 250,000-dimensional feature vector (real and imaginary parts separated). Detailed dataset visualizations and statistical summaries of the generated dataset are presented in the supplementary material. Implementation. All experiments were run on 2 Nvidia-A6000 GPUs, each with 48 GB RAM. The dataset size is around ∼ 300 GB. Dataset and code are available online12 . Limitations. Our dataset is simulation-based and omits hardware impairments such as phase noise and quantization errors, which may impact real-world performance. It further assumes static user locations and fixed blockage, excluding mobility and time-varying propagation dynamics. Future extensions will incorporate hardware-in-the-loop measurements with real RIS prototypes, mobility-aware channel evolution via 3GPP TR 38.901, and dynamic blockage modeling under user mobility.

5

Dataset Analysis

This section provides a quantitative analysis of the dataset, focusing on its structure, variance distribution, and implications for learning. The dataset contains 200,000 channel realizations across 20 deployment variants, each represented by a 250,000-dimensional feature vector. We analyze eigenvalue spectra to characterize variance concentration across principal components and its impact on learning and compression. 5.1

Feature Variance Distribution via PCA

To quantify intrinsic dimensionality, we perform the principal component analysis (PCA)Pon the r λi ; sample covariance matrix and examine the cumulative explained variance ratio as V(r) = Pi=1 d i=1 λi where λ1 ≥ λ2 ≥ · · · ≥ λd are the ordered eigenvalues. Fig. 2 shows the cumulative explained variance averaged across all variants. The curve increases smoothly without a clear elbow, indicating that the variance is not concentrated in a small subset of dominant components. The first 35 components capture approximately 50% of the total variance, representing the most significant large-scale channel variations. A substantial fraction of the variance is distributed across a wide range of components, with 65 and 86 components required to reach 75% and 90% variance, respectively. This near-linear accumulation reflects a broad spectrum of moderately informative 1 Dataset is available at https://www.kaggle.com/datasets/pujitham/3gpp-ri-dataset 2 Codes are available on https://github.com/wirelessai941-web/Benchmarking_3GPP_RIS_mmWave_dataset

6

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.

Model FNN [45] CNN [46] XGBoost [47] LR [41] SVM [48] Transformer [29] Mamba [49]

Train acc ↑

Val acc ↑

Params ↓

MAE ↓

GPU ↓

Time/epoch ↓

85.6% 83.86% 89.9% 68.43% 81.92% 84.8% 85.5%

85.4% 82.54% 86.7% 68.60% 81.17% 84.2% 85.0%

0.45 M 2.8 M – ∼5 K – 33.3 M 55.8 M

0.277 0.295 0.2456 0.412 0.310 0.309 0.287

1.2 GB 1.8 GB – – – 3.9 GB 4.2 GB

0.5 min 0.4 min 20 min 1 min 25 min 1.2 min 1 min

directions, rather than a rapidly decaying eigenvalue profile. The remaining components contribute marginally, with a 95% variance achieved at 93 components. Implication—No Spectral Gap: The gradual decay of the eigenvalues indicate the absence of a spectral gap, with variance distributed across many components rather than concentrated in a low-dimensional subspace. As a result, truncation-based dimensionality reduction is inherently lossy. This finding motivate models that preserve high-dimensional structure or learn task-aware compression. This behavior reflects realistic multi-cluster propagation and provides a challenging benchmark for evaluating robustness and generalization in data-driven RIS optimization.

6

Evaluation Experiments

In this section, we benchmark the dataset on the task defined in Section 3: supervised CSI-toCQI classification using oracle-optimal labels. The benchmark is designed to evaluate not only in-distribution generalization but also robustness to realistic distribution shifts — including noise model variation and real-world hardware-measured channels. Benchmarking Models. We evaluate a diverse set of models that span classical machine learning, deep learning, and sequence modeling paradigms, including feedforward neural networks (FNN) [45], CNN [46], eXtreme Gradient Boosting (XGBoost) [47], logistic regression (LR) [48], support vector machines (SVM) [48], Transformer encoders [28], and Mamba state space models [49]. These models are selected for their complementary inductive biases: linear and tree-based methods provide strong baselines on compressed representations, FNN captures global feature interactions, CNN model local structure, and Transformer/Mamba architectures capture long-range dependencies—well aligned with our dataset distributed variance and absence of a spectral gap. Hyperparameters and Architecture. Raw CSI representations (H) are extremely highdimensional (∼ 2.5 × 105 features per sample), making direct end-to-end learning computationally expensive and prone to overfitting given 2 × 105 training samples [41]. To mitigate this, we extract 12 statistical descriptors—mean, standard deviation, maximum, minimum, median, energy, skewness, kurtosis, 25th/75th percentiles, range, and log-energy—from each of the three sub-channels, yielding a compact 36-dimensional feature vector. All features are standardized using training-set statistics. The FNN consists of four fully connected layers (512–512–256–128) with layer normalization and GELU activations, and a dropout of 0.2 is applied to the first three layers. A final linear layer maps to the output space. Training uses AdamW (learning rate 10−3 , weight decay 10−4 ) with step decay (factor 0.7 every 10 epochs) for 100 epochs and batch size 64 under an 80:20 split. Kullback-Leibler (KL) divergence loss with batch-mean reduction is used for soft-label supervision, improving calibration over hard cross-entropy [50]. All models are trained under a standard empirical risk minimization (ERM) setting and are uniformly evaluated to establish consistent baseline performance. Table 2 highlights clear trade-offs between accuracy, training time, and model complexity. XGBoost achieves the best predictive performance, with the highest validation accuracy and lowest mean absolute error (MAE) [27], but at the cost of significantly higher training time and no explicit control over model size. Among neural models, the FNN offers the most efficient balance, achieving 7

competitive accuracy with low parameter count and fast training . The CNN trains fastest but delivers lower accuracy, indicating limited benefit from its convolutional structure. Transformer and Mamba models provide comparable accuracy but incur substantially higher parameter counts and GPU memory usage, resulting in diminishing returns. Classical methods show mixed behavior: Logistic Regression is lightweight but under performs, while SVM improves accuracy at the expense of long training time. Overall, the results suggest that while XGBoost maximizes accuracy, compact neural models such as FNN offer a more favorable accuracy–efficiency trade-off for practical deployment. 6.1

Test Datasets

We evaluate generalization under four progressively challenging distribution shifts. T1 is a held-out variant from the same generative process with a modified proportional fairness parameter α and increased noise variance, testing sensitivity to within-distribution parameter shifts. T2 introduces impulsive bursty noise modeled as a Bernoulli-Gaussian mixture, evaluating robustness to heavytailed interference unseen during training [46]. T3 corrupts channels with temporally correlated auto regression AR(1) noise, introducing memory effects absent from the i.i.d. training distribution [46]. T4 uses hardware-measured RIS-aided channels from the BRISC dataset [17] at 5 GHz, representing a joint shift in frequency, channel statistics, and hardware noise characteristics — the most severe sim-to-real evaluation. Train–Test Distribution Shift. We quantify Table 3: Train–test distribution shift via DKL , W1 , and the distributional distance between the train- MMD. ↑ indicates larger shift from the training distribuing set and each test set using KL divergence tion, imp. = impairments. DKL [50], Wasserstein-1 distance W1 [50], and Set Description DKL ↑ W1 ↑ MMD ↑ Maximum Mean Discrepancy (MMD) with an RBF kernel [46]. As shown in Table 3, T1–T3 T1 AWGN 3.320 0.533 0.080 exhibit comparable and moderate shift: T2 in- T2 Bursty 2.859 0.534 0.060 curs slightly higher DKL due to the heavy tail of T3 Memory AR(1) 3.294 0.533 0.070 the impulsive noise distribution, while T3 shows T4 [17] Hardware imp. 8.741 1.872 0.284 marginally lower MMD reflecting the smoother marginal perturbation of AR(1) correlated noise. T4 exhibits the largest shift across all three metrics by a substantial margin — more than 2.6× that of any simulated test set — confirming that BRISC real-world 5 GHz measurements occupy a fundamentally different region of feature space from the 3GPP-simulated 28 GHz training distribution. The ordering T1 ≈ T2 ≈ T3 ≪ T4 directly predicts the difficulty ranking observed in Table 4. Results and Analysis. Table 4 shows the test Table 4: Test accuracy (%) across T1–T4. Best in bold, performance of the trained models across all FNN+FT: fine-tuning of the trained FNN model for 100 conditions, along with an additional fine-tuning epochs. (FT) step applied to the FNN for 100 epochs. T1 T2 T3 T4 Classical models (LR, SVM, XGBoost) perform Model poorly, indicating limited capacity to capture the LR [41] 25.2% 21.4% 23.1% 18.6% complex, high-dimensional structure of the RIS SVM [48] 22.3% 28.7% 20.5% 12.4% channels. Neural models significantly improve XGB [47] 39.5% 35.2% 37.8% 20.1% 52.3% 55.4% 58.9% 28.2% performance, with FNN achieving the strongest CNN [12] 61.4% 64.2% 66.8% 31.3% baseline results across simulated settings, sug- FNN [45] gesting that the extracted features are well-suited Transformer [29] 11.9% 10.0% 10.6% 9.8% 25.6% 26.7% 25.1% 18.4% to dense representations. CNN shows moderate Mamba [49] FNN+FT 90.8% 91.3% 87.8% 78.4% performance, while Transformer and Mamba under perform, indicating that higher model complexity does not guarantee better generalization without task-aligned inductive biases. Under distribution shift, all models degrade, particularly in the sim-to-real setting (T4), confirming a substantial domain gap. Fine-tuning proves highly effective in mitigating this gap: FNN+FT achieves the best performance across all conditions, with significant gains on T4 using only 100 labeled samples. This improvement arises because the pretrained FNN captures general feature representations, which are efficiently adapted to target-domain statistics through fine-tuning. Detailed Discussion: Generalization, Failure Modes, and Adaptation. Table 4 reveals that model performance is limited not only under real-world shift (T4) but also across simulated conditions (T1–T3). Even without crossing the sim-to-real boundary, most models exhibit noticeable degradation 8

across T1–T3, indicating that they fail to generalize across variations in noise statistics and channel dynamics within the same simulation domain. This suggests that the learned representations are highly sensitive to distributional changes and do not capture invariant features of RIS channels. High-capacity architectures such as Transformer and Mamba perform particularly poorly, given the huge computation cost and lack of task-aligned inductive bias. Classical models (LR, SVM, XGBoost) also underperform due to limited representational capacity. While neural baselines such as FNN and CNN achieve relatively better results, their performance still drops consistently from T1 to T3 and further on T4, confirming that even these models struggle with cross-scenario generalization. Fine-tuning significantly improves performance across all test sets. The strong results of FNN+FT indicate that the pretrained model captures useful but non-invariant features, which can be adapted to new distributions with minimal supervision. Notably, fine-tuning improves not only T4 (sim-to-real) but also T2 and T3, demonstrating that even simulated domain shifts require adaptation. These findings are closely linked to the dataset design. Unlike prior datasets that focus on narrow, single-scenario settings, this dataset spans multiple deployment conditions with diverse channel characteristics. This diversity introduces substantial variability even within simulated data, making generalization inherently challenging. As a result, models trained on this dataset are forced to confront realistic distribution shifts rather than overfitting to a fixed environment. Overall, the results show that state-of-the-art models for RIS applications do not generalize well across heterogeneous conditions without adaptation—even within simulation. This highlights a fundamental limitation of current learning approaches and underscores the need for domain generalization methods that can learn invariant representations across diverse scenarios. The proposed dataset provides a suitable and challenging benchmark for developing such approaches, enabling more robust and deployment-ready RIS optimization.

7

Conclusion and Future Directions

In this paper, we introduced a large-scale 3GPP TR 38.901-compliant dataset for RIS-aided mmWave networks that addresses key gaps in standardized channel modeling, per-link decomposition, oracle phase labels, and multi-scenario coverage with reproducible splits, along with a CSI-to-CQI mapping that reformulates RIS phase optimization as a scalable classification task. Extensive benchmarking shows that existing models struggle to generalize across both simulated and real-world distribution shifts, with performance degradation even within simulated settings, while fine-tuning provides only partial mitigation. Overall, our dataset offers a more diverse and realistic benchmark than prior single-scenario datasets, making it well-suited for evaluating robustness and domain generalization in RIS learning. However, the dataset remains simulation-based and does not capture hardware impairments or temporal mobility dynamics; future extensions will incorporate hardware-in-the-loop measurements and time-evolving channels to further improve realism and deployment relevance. Boarder Impact. The proposed work can positively impact society by improving wireless coverage, energy efficiency, and reliability in future B5G networks, enabling applications such as smart cities, and autonomous systems. The dataset also supports reproducible research in RIS-aided learning systems. However, potential risks include misuse for enhanced surveillance or sensing, unequal access to advanced communication technologies.

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