# Why RF Fingerprinting Needs Better Data, Not Bigger Models

Emma Bothereau, Robin Gerzaguet, Matthieu Gautier, Alice Chillet, Olivier Berder

► To cite this version:

Emma Bothereau, Robin Gerzaguet, Matthieu Gautier, Alice Chillet, Olivier Berder. Why RF Fingerprinting Needs Better Data, Not Bigger Models. IEEE Access, 2025, 13, pp.171348-171355. 10.1109/ACCESS.2025.3614459. [hal-05304180](https://hal.science/hal-05304180v1)

HAL Id: hal-05304180

https://hal.science/hal-05304180v1

Submitted on 8 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Why RF Fingerprinting Needs Better Data, Not Bigger Models

Emma Bothereau, Robin Gerzaguet, Matthieu Gautier, Alice Chillet, Olivier Berder

**Abstract**—Recent advances in Radio Frequency Fingerprint Identification have demonstrated impressive classification performance, often relying on deep neural networks. However, such approaches remain too computationally intensive for deployment on resource-constrained IoT devices. In this work, we revisit Fully Connected Neural Networks (FCNNs) and show that a simple one-hidden-layer FCNN can achieve competitive performance, requiring at least 5x fewer FLOPs and inference time than state-of-the-art architectures, with only a marginal drop in F1-score (0.02% to 0.21% depending on the dataset, for 16 transmitters or fewer). More importantly, our results reveal that despite their simplicity, our FCNNs exhibit comparable resilience to channel and device variability comparable to more complex architectures. This suggests that the current emphasis on increasing model size may be misguided. Rather than continuously increasing model depth or sophistication, our results highlight the importance of reexamining how datasets are designed and used in evaluation.

**Index Terms**—Radio Frequency Fingerprint, Deep Learning, Lightweight ML, Fully Connected Neural Networks.

## I. INTRODUCTION

ADIO Frequency Fingerprint Identification (RFFI) has emerged as a promising method for enhancing physical layer security by leveraging the unique imperfections inherent in hardware emitters. These imperfections, though not impeding data transmission, act as unique identifiers due to their distinctive characteristics. The development of Machine Learning (ML) in the last few years has revitalized RFFI, offering more robust and flexible recognition capabilities than traditional approaches.

Deep learning applied to RFFI presents a set of unique challenges [1]. One of the first concerns relates to signal representation [2], where the choice between time-domain, frequency-domain, and time-frequency representations must be made to maximize the extraction of stable, discriminative features. Closely connected to this is the selection of the network architecture and learning methodology [3], since both directly influence performance, particularly in the presence of noisy or heterogeneous data. Another important challenge is the scarcity of representative samples, which makes data augmentation strategies indispensable for improving robustness and generalization [4], [5]. Beyond training, deployment issues arise as well, including resilience to channel variations [6],

[7], adaptation to the communication protocol [8], and the ability to meet real-time inference constraints on resource-constrained devices [9]. In such cases, trade-offs between accuracy and computational efficiency become critical. Indeed, mostly based on Convolutional Neural Networks (CNN), RFFI methods applied can be computationally demanding. This is an issue, especially for Unmanned Aerial Vehicle (UAV) and Vehicle-to-everything (V2X) technologies, as they require fast inference and low computational complexity because they need to process signals in real-time.

To address the complexity of ML, lightweight methods have been developed to reduce computational costs, memory costs, and/or training or inferencing times. There are multiple lightweight approaches for RFFI. Firstly, prior knowledge of signal characteristics may be used, either through initial preprocessing of the signals associated with a smaller classifier [10]–[12], or using Tangled Program Graph (TPG) [13], presenting little memory and computational requirements. The use of Recurrent Neural Networks (RNN), such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU) blocks [14] can also be utilized to construct a more lightweight network. These methods leverage the temporal information inherent to the raw signals. Recurrent units have the advantage of needing less parameters than traditional neural networks. However, they require a greater quantity of data for training and are much more sensitive to hyperparameters than CNN. Furthermore, to be lightweight, CNN structures can be strategically designed to be less complex [15]. They also can use optimized layers such as space-separable convolutions [16], which reduces the number of multiplications and parameters, the primary sources of computational complexity associated with convolutions. Finally, the complexity of the networks can be reduced using pruning methods [17], [18], especially in terms of parameters. However, the lack of a benchmark for RFFI makes it difficult to compare the results of different studies.

Many publicly released RFFI datasets [19]–[23] are accompanied by a reference CNN that achieves high accuracy on that dataset, but often without discussing the architectural complexity, parameter count or nature of layers. This trend may lead to overcomplex models. This raises two fundamental questions: are convolutional layers truly necessary for RFFI? And what level of model complexity is actually justified given the characteristics and limitations of real-world RF signal datasets? CNNs are principally composed of convolutional layers, which contribute significantly to computational cost due to their high number of Floating-Point Operations (FLOPs) and long inference times. These are typically followed by a

Manuscript received X; revised Y.

This work is funded by the French National Research Agency (ANR) under the grant number ANR-22-CE15-0007-01 (RedInBlack project)

Emma Bothereau (email: emma.bothereau@irisa.fr), Robin Gerzaguet (email: robin.gerzaguet@irisa.fr), Matthieu Gautier (email: matthieu.gautier@irisa.fr), Alice Chillet (email: alexc.chillet@irisa.fr) and Olivier Berder (email: olivier.berder@irisa.fr) are with University of Rennes, CNRS, IRISA, Lannion, France.
few Fully Connected (FC) layers. Removing the convolutional layers could drastically reduce complexity.

Fully Connected Neural Networks (FCNNs), or Multi-Layer Perceptron, which rely solely on FC layers, offer an appealing alternative in this context. Yet, they remain largely underexplored in the RFFI literature. When FCNNs are used, they often rely on preprocessed features as inputs, resulting in lightweight models with few parameters [24]. In other cases, raw signal data is used directly, but this leads to high-dimensional inputs and, in turn, increased memory requirements [25]. While these works indicate that FCNNs can approach CNNs in terms of performance, they have not received much attention in terms of structural analysis or broader evaluation.

To thoroughly evaluate an RFFI solution, it is necessary to verify if it can still perform well at a later date and/or in a different location. This notion, known as resilience, is often overlooked in the RFFI literature [18]. While some methods aim to mitigate channel effects, at the price of additional complexity [26], evaluating performance in diverse scenarios provides valuable insight into what the model truly learns. It reveals whether the network captures device-specific information or merely exploits the propagation channel.

The key contributions of this paper consist in:

* Demonstrating the suitability of FCNN as lightweight candidates for different real-world RFFI datasets by exploring a wide range of different FCNN architectures.

* Comparing classical deep learning methods and a proposed lightweight FCNN for RFFI across various open RF fingerprint datasets. F1-score, number of parameters, number of FLOP and inference time are analysed.

* Showing that despite their reduced complexity, FCNNs exhibit resilience to channel and device variability comparable to larger CNNs. This observation raises critical questions about the actual number of parameters needed for RFFI and highlights the influence of dataset structure on perceived model performance.

The paper is organized as follows: Section II introduces RFFI as well as state-of-the-art databases. Section III explores the design space of FCNN for each selected dataset. In Section IV the state-of-the-art networks and a selected FCNN are evaluated both on their classification performance as well as their complexity, before Section VI concludes the work.

## A. Datasets

# II. RADIO FREQUENCY FINGERPRINT IDENTIFICATION

### A. RF Fingerprint

The process of transmitting digital data consists of several essential steps, including signal modulation, In-phase (I) and Quadrature (Q) conversion, carrier frequency modulation, amplification, filtering, and transmission via the antenna. At the receiving side, the signal is captured by the receiving antenna and processed into I and Q channels. Considering the $i^{th}$ emitter, with its corresponding fingerprint $\mathcal{F}_i$, the received signal $x_i$, depending on the original signal $x$ can be expressed as $x_i(t) = \mathcal{F}_i(x(t))$ [13].

### B. Datasets

To conduct a comprehensive study, we selected 5 of the most complete open-access WiFi datasets for RFFI: POWDER [19], WiSig [20], [21] (with the ManySig and a custom subset), Random Stable WiFi RF Fingerprints [22] (SWRFF) and ORACLE [23] (recorded 2ft away from the receiver). For each dataset, we defined two distinct situations, called Scenario 1 (S1) and Scenario 2 (S2). The two distinct scenarios are used to evaluate the ability of a network to generalize from S1 to S2, utilizing either a subsequent session record or a distinct location. The datasets and their scenarios specifications are detailed in Table I. They contain raw demodulated IQ signals (no pre-processing applied). The datasets have been resized so that all signals are of size (2, N), with N representing the temporal length of the signal and 2 representing the I and Q channels. The number of signals depends on the length N of the signals. For N = 256, ORACLE presents 4 000 signals per transmitter, but for N = 128, there are 8 000.

The scenario S1 is used in the next section. For each dataset, S1 data is separated into two groups, one with 90% for training and the remaining 10% for testing. The entirety of Scenario S2 data will be used in Section V-A for testing the resilience of the networks. In Section V-B, only the testing part of the S1 data will be used to evaluate the resilience to additive noise channel effect.

# III. FULLY CONNECTED NEURAL NETWORKS

As will be discussed in greater detail later in this work, state-of-the-art networks are mainly CNN. Yet, most of their complexity comes from their convolutional layer. This raises

<table><caption>TABLE I: Summary of RFFI datasets for N = 256.</caption><thead><tr><th>Database</th><th>POWDER - WiFi</th><th>WiSig (M = 6)</th><th>WiSig (M = 90)</th><th>Stable WiFi RF Fingerprints (SWRFF) - Random</th><th>ORACLE</th></tr></thead><tbody><tr><td>References</td><td>[19]</td><td>[20], [21]</td><td>[20], [21]</td><td>[22]</td><td>[23]</td></tr><tr><td>Description</td><td>Recordings on two distinct days.</td><td>Recordings on 4 days over a month. Set from ManySig.</td><td>Recordings on 4 days over a month. Custom set from ManyTx</td><td>Enrollment phase: All devices placed 1 meter away from the receiver. Deployment phase: Randomly moved within a 3-meter radius.</td><td>Signals recorded from 2ft to 62ft with two runs per location.</td></tr><tr><td>Transmitters (M)</td><td>4 - USRP X310</td><td>6 - Atheros AR</td><td>90 Atheros AR & QC</td><td>15 - 10 FiPy & 5 LoPy</td><td>16 - USRP X310</td></tr><tr><td>Signals</td><td>≈11700/day</td><td>1000/day/TX</td><td>50/day/TX</td><td>Enrollment: ≈5400/Tx<br>Deployment: ≈4300/Tx</td><td>4000/Tx/location/run</td></tr><tr><td>Scenario 1 - S1</td><td>Day 1</td><td>Day 1</td><td>Day 1</td><td>Enrollment phase (morning)</td><td>Run 1 - 2ft</td></tr><tr><td>Scenario 2 - S2</td><td>Day 2</td><td>Day 2</td><td>Day 2-3-4</td><td>Deployment phase (evening)</td><td>Run 2 - 2ft</td></tr></tbody></table>
the question of whether convolutional layers are necessary for RFFI and whether FCNN, which consists solely of FC layers, can achieve classification results comparable to CNN and therefore serve as a lightweight alternative for RFFI.

## A. Methodology

To find a lightweight FCNN architecture for RFFI, we first conducted an exploration of the design space of FCNN. The networks were randomly generated with a varying number of layers *L* ranging from 1 to 7 (excluding the output layer), and an even number of neurons in each layer, from 2 to 1 000 neurons, before ending with the FC layer with *M* neurons and a *Softmax* output. Additionally, four well-performing activation functions were selected: *LeakyReLU* (Leaky Rectified Linear Unit) [27], *SELU* (Scaled Exponential Linear Unit), *RReLU* (Randomized leaky Rectified Linear Unit) [28], and *GELU* (Gaussian Error Linear Unit) [29], as these were found to be the most efficient for our task. Furthermore, we varied the input window size *N*, setting it to $2^i, i \in [1, 8]$ signals. A threshold of 100 000 parameters was set as the limit of maximum parameters for the networks. Each network generated was trained for 50 epochs, with a learning rate of 0.01 that decreased by 25% every 10 epochs. The trained networks were then evaluated on their F1-scores and averaged over 5 seeds that are fixed values used for the pseudorandom number generators. Seeds impact the train-test repartition along with the network initialization. This paper uses the macro F1-Score as the main indicator of classification performance.

## B. Random Fully Connected Neural Networks

For each dataset, at least 500 random FCNN were generated. We represent each network with a single point for the WiSig (*M* = 6) dataset in Figure 1.

<figure><img src="image_2.png" /><figcaption>Fig. 1: FCNN design space exploration for WiSig (M = 6).</figcaption></figure>

When representing the F1-score with respect to the number of parameters, each dataset presents a clear front of well-balanced networks that achieve the best trade-off between both metrics, as can be seen on Figure 1 for WiSig (*M* = 6) dataset.

## C. Best Networks for Each Criterion

To have a better analysis of the capacities of the FCNN, we select, for each dataset, the best performing network with a F1-score on S1 greater than 95% (Experimentation E1), greater than 99% (E2) and the maximum F1-score on S1 (E3). For each experiment, we select the smallest network in terms of parameters that meets the condition. The selected networks are presented in Table II and pinpointed in Figure 1. The structure of the selected networks is detailed with the length of the input signal, the number of neurons in each layer, as well as the corresponding activation functions. For example, the best-performing FCNN satisfying E3, on the WiSig (*M* = 90) dataset, is a 3-layer network with an input size of *N* = 128, made of two hidden layers with 220 and 88 neurons, and an output layer of *M* = 90. The activation function after the two first layers is *LeakyReLU*. The last layer is always followed by the *Softmax* function.

<table><caption>TABLE II: Selected best-performing lightweight FCNN.</caption><thead><tr><th>Experimentation Condition</th><th>E1 ≥ 95%</th><th>E2 ≥ 99%</th><th>E3 Maximum</th></tr></thead><tbody><tr><td colspan="4" style="text-align: center; font-weight: bold;">POWDER (M = 4)</td></tr><tr><td>F1-Score</td><td>96.05</td><td>99.15</td><td>99.96</td></tr><tr><td>Parameters</td><td>2 074</td><td>5 324</td><td>78 826</td></tr><tr><td>Signal length N</td><td>32</td><td>64</td><td>128</td></tr><tr><td>Hidden Layers</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Neurons per Layer</td><td>20</td><td>40</td><td>302</td></tr><tr><td>Activation Function</td><td>GELU</td><td>GELU</td><td>LeakyReLU</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">WiSig (M = 6)</td></tr><tr><td>F1-Score</td><td>95.80</td><td>99.05</td><td>99.68</td></tr><tr><td>Parameters</td><td>552</td><td>2 706</td><td>95 738</td></tr><tr><td>Signal length N</td><td>16</td><td>64</td><td>128</td></tr><tr><td>Hidden Layers</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Neurons per Layer</td><td>14</td><td>20</td><td>364</td></tr><tr><td>Activation Function</td><td>RReLU</td><td>RReLU</td><td>LeakyReLU</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">WiSig (M = 90)</td></tr><tr><td>F1-Score</td><td>95.65</td><td>-</td><td>95.84</td></tr><tr><td>Parameters</td><td>49 338</td><td>-</td><td>83 998</td></tr><tr><td>Signal length N</td><td>128</td><td>-</td><td>128</td></tr><tr><td>Hidden Layers</td><td>2</td><td>-</td><td>2</td></tr><tr><td>Neurons per Layer</td><td>108/108</td><td>-</td><td>220/88</td></tr><tr><td>Activation Function</td><td>LeakyReLU/RReLU</td><td>-</td><td>LeakyReLU(×2)</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">SWRFF (M = 15)</td></tr><tr><td>F1-Score</td><td>96.56</td><td>99.37</td><td>99.71</td></tr><tr><td>Parameters</td><td>2 095</td><td>12 687</td><td>77 103</td></tr><tr><td>Hidden Layers</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Signal length N</td><td>32</td><td>256</td><td>256</td></tr><tr><td>Neurons per Layer</td><td>26</td><td>24</td><td>146</td></tr><tr><td>Activation Function</td><td>GELU</td><td>GELU</td><td>GELU</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">ORACLE (M = 16)</td></tr><tr><td>F1-Score</td><td>96.34</td><td>-</td><td>98.13</td></tr><tr><td>Parameters</td><td>22 346</td><td>-</td><td>79 766</td></tr><tr><td>Signal length N</td><td>64</td><td>-</td><td>64</td></tr><tr><td>Hidden Layers</td><td>1</td><td>-</td><td>1</td></tr><tr><td>Neurons per Layer</td><td>154</td><td>-</td><td>550</td></tr><tr><td>Activation Function</td><td>GELU</td><td>-</td><td>GELU</td></tr></tbody></table>

The performance of FCNN depends directly on the number of devices to be identified. Indeed, the more transmitters there are, the lower the overall F1-score becomes, as the networks
need to be able to distinguish finer details. In fact, it can be seen in Table II that at most (E3 scenario), for $M = 4$, the F1-score reaches 99.96%, for $M = 16$, only 98.13%, and for $M = 90$, it does not exceed 95.84%.

This phenomenon is partially explained by both underfitting and overfitting. Indeed, in this work a strong constraint was applied on the parameter number with a maximum of 100 000 parameters. This results in lightweight networks, but the downside is that such networks might be limited in terms of capacities to correctly classify the signals (i.e. underfitting). This is typically what seems to happen to the ORACLE dataset. However, overfitting might also be an issue especially for WiSig ($M = 90$) that presents only 50 signals per day per transmitter. For this particular dataset, the lack of signals might result in an over-specification of the networks on the training data. This is shown by a near-perfect training F1-scores but lower testing scores: for example, the E3 network for WiSig ($M = 90$) has a training F1-score of 99.99%, but when being tested, it only reaches 95.84%. Overfitting can be mitigated using methods such as data augmentation [4], [5] or regularization. However, these methods for RF fingerprinting are out of the scope of this paper.

It can also be observed that as the number of transmitters increases, the front shifts to the right, requiring additional parameters to achieve comparable performance. In Table II, it can be seen that for $M \le 15$ devices, a network with fewer than 13 000 parameters can maintain an F1-score of at least 99%, while datasets with 16 or more devices need at least 22 000 parameters to reach only a 95% F1-score. This can be attributed to the fact that the last layer requires as many neurons as devices to be identified, directly adding parameters.

In the experiments, it may be observed that the signal length used is often not the maximum possible length of $N = 256$, but more often $N = 64$ or $N = 128$. Moreover, it is also shown that the best networks usually present only one layer besides the output layer and mostly utilize the GELU function. Reducing the input length, associated with shallower networks help decrease the number of parameters and the number of FLOP in the network offering an interesting trade-off between F1-score and the number of parameters. It should be noted that shallower networks are usually associated with a shorter inference time.

# IV. PERFORMANCE OF SELECTED NETWORKS

## A. Selected FCNN

Based on the previous observations, we designed a well-performing lightweight FCNN on all datasets. This network, depicted in Figure 2, comprises a single FC layer with 400

parameters, followed by GELU activation, before a final FC layer with $M$ neurons and a Softmax function. The network takes signals of size $N = 128$. This FCNN has between 104 404, for $M = 4$, and 138 890 parameters for $M = 90$ transmitters.

<figure><img src="image_3.png" /><figcaption>Fig. 2: Representation of the chosen FCNN.</figcaption></figure>

## B. Neural Networks for RFFI

To evaluate the performance and complexity of our proposed FCNN, we compared it with multiple state-of-the-art neural networks. The neural networks used in this study are those employed in the articles associated with the selected datasets (CNN POWDER [19], CNN WiSig [20], [21], CNN SWRFF [22], and CNN ORACLE [23]), alongside a lightweight neural network named CNN-GRU Feng [14] and a lightweight CNN named CNN Feng [15]. While CNN POWDER and CNN ORACLE draw inspiration from the classical AlexNet architecture, the other networks can be considered lightweight neural networks. CNN-GRU Feng incorporates GRU, a specialized recurrent neural network architecture meant to efficiently extract temporal patterns. This unique feature contributes to its distinction as the lightest network in terms of parameters among the state-of-the-art networks.

These networks represent the diversity of different possible structures, with varying convolutional filters, either one or two dimensions, and few or multiple convolution layers. They take different sizes as input.

The details of the network architectures, as well as the training parameters used, is accessible at https://github.com/EBothereau/NeuralNetworksForJulia.

## C. F1-Score Evaluation

Each network was evaluated on each dataset. The results are presented in Table III, showing the F1-scores obtained when

<table><caption>TABLE III: F1-Scores of selected networks across all datasets, with error comparison to FCNN.</caption><thead><tr><th>BDD</th><th>FCNN (ours)</th><th>CNN-GRU Feng</th><th>CNN POWDER</th><th>CNN ORACLE</th><th>CNN SWRFF</th><th>CNN WiSig</th><th>CNN Feng</th></tr></thead><tbody><tr><td>Signal Length N</td><td>128</td><td>128</td><td>256</td><td>128</td><td>256</td><td>256</td><td>128</td></tr><tr><td>POWDER</td><td>99.94±0.02</td><td>92.32±0.17</td><td>99.98±0.04</td><td>99.98±0.04</td><td>100.00±0.00</td><td>99.78±0.31</td><td>99.93±0.14</td></tr><tr><td>WiSig (M = 6)</td><td>99.76±0.22</td><td>99.74±0.10</td><td>99.80±0.27</td><td>99.88±0.18</td><td>99.87±0.14</td><td>99.57±0.38</td><td>99.97±0.07</td></tr><tr><td>WiSig (M = 90)</td><td>96.08±0.56</td><td>91.40±2.22</td><td>94.57±0.99</td><td>94.00±2.63</td><td>99.22±0.55</td><td>93.67±2.91</td><td>99.22±0.28</td></tr><tr><td>SWRFF</td><td>99.73±0.04</td><td>93.75±0.74</td><td>99.04±0.47</td><td>99.77±0.16</td><td>99.68±0.18</td><td>99.61±0.29</td><td>99.82±0.08</td></tr><tr><td>ORACLE</td><td>99.91±0.01</td><td>95.77±0.47</td><td>99.68±0.38</td><td>99.93±0.10</td><td>99.76±0.21</td><td>98.35±0.91</td><td>99.70±0.08</td></tr></tbody></table>
they were evaluated on the test subset. Networks with F1-score higher than our FCNN are highlighted in red, while those with lower scores are marked in green. The colors are applied as a gradient scale. Additionally, the top-performing networks for each dataset are underlined.

While CNN-GRU consistently underperforms across all datasets, most CNN-based architectures reach high F1-scores, often peaking when the model was originally designed for the corresponding dataset.

Despite its simplicity, our FCNN delivers competitive results: it equals or outperforms the majority of state-of-the-art models. The performance gap with the best-performing architecture remains minimal: just 0.06% (POWDER), 0.21% (WiSig, M=6), 0.09% (SWRFF), and 0.02% (ORACLE) for datasets with a small number of transmitters.

Unsurprisingly, performance decreases on the WiSig dataset with 90 transmitters. However, most CNN-based models also show a marked performance drop, with only CNN SWRFF and CNN Feng maintaining a strong F1-score. Notably, our FCNN still ranks above the majority of the remaining architectures.

These results demonstrate that a fully connected network, though often overlooked in RFFI, can stand as a strong baseline in terms of raw classification performance, before even considering its benefits in terms of architectural simplicity that will be discussed in the next part.

## D. Complexity Evaluation

The complexity of the network is evaluated based on the number of parameters, the number of FLOP, and the inference time. The inference time is defined as the duration required to predict a label for a given batch of signals. For better precision, we measured this value as the average of 1 000 predictions on a random batch, on a machine equipped with a NVIDIA Tegra Orin GPU. To remain as general as possible and address a wide range of scenarios, all criteria are considered with equal importance. In Table V, we present all indicators for $M = 6$, as well as the ratio of the indicators of state-of-the-art networks to those of our proposed FCNN.

<table><caption>TABLE V: Complexity evaluation for all chosen networks.</caption><thead><tr><th>Model</th><th>Parameters (k)</th><th>FLOP (k)</th><th>Inference (ms)</th></tr></thead><tbody><tr><td>FCNN (ours)</td><td>105</td><td>105</td><td>0.19</td></tr><tr><td>CNN-GRU Feng</td><td>22</td><td>(×0.2)</td><td>844</td><td>(×8)</td><td>1.50</td><td>(×8)</td></tr><tr><td>CNN POWDER</td><td>1 642</td><td>(×15.6)</td><td>8 047</td><td>(×76)</td><td>3.40</td><td>(×18)</td></tr><tr><td>CNN ORACLE</td><td>971</td><td>(×9.2)</td><td>32 168</td><td>(×305)</td><td>3.65</td><td>(×19)</td></tr><tr><td>CNN SWRFF</td><td>161</td><td>(×1.5)</td><td>3 077</td><td>(×29)</td><td>2.57</td><td>(×14)</td></tr><tr><td>CNN WiSig</td><td>40</td><td>(×0.4)</td><td>514</td><td>(×5)</td><td>1.60</td><td>(×9)</td></tr><tr><td>CNN Feng</td><td>60</td><td>(×0.6)</td><td>2 532</td><td>(×24)</td><td>0.89</td><td>(×5)</td></tr></tbody></table>

CNN-GRU Feng and CNN Feng are the two networks explicitly designed for low complexity:

* CNN-GRU Feng achieves the smallest parameter count and relatively low FLOP and inference time, but its F1-score limits its applicability to highly memory-constrained scenarios where classification performance is a secondary concern.

* CNN Feng offers an excellent trade-off between size and performance, showing strong F1-scores across datasets. It remains one of the most compact models in terms of parameters and inference time. However, due to its convolutional nature, it remains less efficient in terms of FLOP. Notably, for $M \le 15$, several FCNN architectures achieve even fewer parameters while staying within 1% of the F1-score of CNN Feng (see Table II, Section III.C).

Our proposed FCNN combines strong F1-scores with the lowest FLOP count and fastest inference time among all tested architectures. It has slightly more parameters than the most compact CNNs. This difference stems from the nature of FC layers, where each parameter contributes uniquely to the computation, unlike in convolutional or recurrent layers where parameters are reused. However, this increase remains modest and the overall trade-off is highly favorable, with substantial gains in computational cost and inference speed. Compared to the best performing networks in terms of F1-score, namely CNN SWRFF and CNN Feng, the FCNN presents at least 24× fewer FLOP and is 5× faster.

## V. RESILIENCE OF THE NETWORKS

### A. Resilience Evaluation on Scenario 2

To evaluate the resilience, we used the network trained previously and evaluated it on new data for each dataset. This data was collected separately from the data used to train the network, to ensure that the network could still work at a later time and/or in a different location (S2 in Table I). For POWDER [19] and ORACLE [23], the second recording session was used, while for WiSig [20], [21], we used recordings from the 3 other sessions. For the SWRFF [22] dataset, we used the second session; however, unlike the other datasets, the devices were randomly moved. The F1-scores obtained are displayed in Table IV.

Resilience strongly depends on the dataset. For example, SWRFF displays extremely low F1-scores in this second scenario, ranging from 5.66% to 18.61%. This lower score is likely due to the fact that both the location and the propagation

<table><caption>TABLE IV: Resilience evaluation using F1-Scores of selected networks across all datasets on the second scenario, with error comparison to FCNN.</caption><thead><tr><th>BDD</th><th>FCNN (ours)</th><th>CNN-GRU Feng</th><th>CNN POWDER</th><th>CNN ORACLE</th><th>CNN SWRFF</th><th>CNN WiSig</th><th>CNN Feng</th></tr></thead><tbody><tr><td>Signal Length N</td><td>128</td><td>128</td><td>256</td><td>128</td><td>256</td><td>256</td><td>128</td></tr><tr><td>POWDER</td><td>86.96±1.11</td><td>78.80±1.44</td><td>92.70±12.48</td><td>71.27±7.06</td><td>73.37±2.11</td><td>79.41±17.09</td><td>78.14±4.20</td></tr><tr><td>WiSig (M = 6)</td><td>57.26±2.02</td><td>64.41±5.10</td><td>61.91±5.04</td><td>64.57±16.44</td><td>59.17±2.71</td><td>56.98±9.59</td><td>64.41±5.10</td></tr><tr><td>WiSig (M = 90)</td><td>21.09±0.33</td><td>15.18±0.65</td><td>9.23±0.74</td><td>7.77±1.25</td><td>16.80±1.39</td><td>7.85±1.66</td><td>21.07±2.26</td></tr><tr><td>SWRFF</td><td>18.61±0.51</td><td>5.66±0.47</td><td>9.82±4.86</td><td>5.83±1.61</td><td>11.87±2.40</td><td>15.06±1.51</td><td>7.67±2.02</td></tr><tr><td>ORACLE</td><td>9.74±0.11</td><td>7.83±0.55</td><td>30.16±1.43</td><td>20.18±0.85</td><td>21.66±4.18</td><td>26.39±0.98</td><td>10.33±0.57</td></tr></tbody></table>
channel changed between the data used for training and testing. In contrast, POWDER has a higher F1-score, ranging from 71.27% to 92.70%, because the contexts of the two scenarios are more similar and the location of the devices remains unchanged. Moreover, the number of transmitters also impacts the F1-score, and as the number of devices increases, the F1-score decreases. For example, the F1-score drops by more than 35% between WiSig $M = 6$ and $M = 90$.

The FCNN performs similarly overall to the other networks. However, it remains unclear whether the network architecture impacts resilience. For example, the FCNN is one of the best-performing networks for the POWDER, SWRFF, and WiSig ($M = 90$) datasets, while it is one of the worst-performing networks for the WiSig ($M = 6$) and ORACLE. Nevertheless, one notable advantage of our FCNN in this study, compared to other state-of-the-art networks, is its consistently low variability across different seeds. It demonstrates the lowest variability for all datasets except SWRFF, where it ranks second. The variability of our model spans from 0.11% to 2.02% F1-score, whereas other networks exhibit a range between 0.47% and 17.09% F1-score.

## B. Resilience Evaluation to Additional Gaussian Noise

This section generalizes the noise resilience capacity of the proposed FCNN compared to the best-performing state-of-the-art network. The SWRFF CNN was selected as reference since, as shown in Table III, it achieves the highest overall performance. For this study, we used the testing data of the S1 scenario. For each dataset, additional noise was added to the original signals. The level of the added noise is expressed as the Signal-to-Noise Ratio (SNR). The reported SNR values do not account for any noise already present in the original data, as some datasets lack the information needed to compute the true SNR. Instead, we define the noise level of the added component as follows:

$$ \sigma = \sqrt{P_{sig}} \cdot 10^{-\frac{SNR}{20}} \quad (1) $$

where $P_{sig}$ is the power of the original signal. Network performance was evaluated for SNR values ranging from -10 to 30 dB with 5 dB increments. Each evaluation was repeated across five random seeds and the results were averaged. Figure 3 shows that both networks perform similarly at low SNR levels, with a general decline in F1-score as noise increases. The FCNN appears to maintain higher F1-scores than SWRFF CNN at moderately noisy conditions, making it a strong candidate for practical applications.

## C. Discussions

The fact that the removal of convolutional layers does not significantly affect resilience further challenges the assumed necessity of complex architectures in RFFI. This result not only confirms the efficiency of simpler architectures, but also highlights the need to better understand what aspects of the data are actually being leveraged by these models. The comparable performance and resilience of FCNNs and CNNs suggest that the features driving identification

<figure><img src="image_4.png" /><figcaption>Fig. 3: F1-score depending on the SNR evaluated on CNN SWRFF and our proposed FCNN, for all datasets.</figcaption></figure>

may be simpler or more dataset-specific than previously assumed. This observation calls into question not only the architectural complexity, but also the standard practice of feeding raw IQ samples as inputs. It reinforces the need for more thorough and diverse dataset design in RFFI, to better assess generalization, and ultimately guide the development
of network architectures based on signal diversity rather than
implicit dataset biases.

VI. CONCLUSIONS AND PERSPECTIVES

In this work, we have revisited the role of network complex-
ity in RF fingerprint identification, showing that simple fully
connected architectures can match the performance of widely
used convolutional networks, while being significantly lighter
in terms of computational cost and inference time. Across
multiple datasets, our proposed FCNN maintains competitive
performance and comparable resilience to channel and device
variability, despite their drastically reduced parameter count.

The use of FCNNs proves that there is a simplified repre-
sentation that performs as well as CNNs for limited number
of devices, even if these simplifications raised limitations
when it comes to discriminating among a higher number
of devices, requiring finer features. These findings suggest
that the current emphasis on deep and complex architectures
may be overstated. Rather than continuously increasing model
depth or sophistication, our results highlight the importance
of reexamining both the construction of datasets and the
preprocessing steps applied prior to model training (especially
the use of raw IQ samples). A clearer understanding of which
signal characteristics actually support identification, and how
models exploit them, is essential.

Ultimately, we argue that progress in RFFI requires not
just better models, but also better datasets: datasets that ex-
pose real-world diversity, challenge assumptions about model
generalization, and support fair, reproducible comparisons
across networks. Such datasets could incorporate detailed 3D
models of the environment and materials, acquisitions that
independently vary degrees of freedom (e.g. position, channel,
interference, power, ...) and measurement campaigns that stress
devices across operating conditions (e.g. thermal variations,
aging). They should also be large-scale and diverse enough to
probe model resilience and robustness in realistic settings.

REFERENCES

[1] S. Al-Hazbi, A. Hussain, S. Sciancalepore, G. Oligeri, and P. Papadimitratos, "Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities," in *International Wireless Communications and Mobile Computing (IWCMC)*, 2024, pp. 0824–0829.

[2] A. Ahmed, B. Quoitin, A. Gros, and V. Moeyaert, “A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification,” *Sensors*, vol. 24, no. 13, 2024. [Online]. Available: https://www.mdpi.com/1424-8220/24/13/4411

[3] A. Jagannath, J. Jagannath, and P. S. P. V. Kumar, “A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges,” 2022. [Online]. Available: https://arxiv.org/abs/2201.00680

[4] K. Lin, C. Zhao, J. Chen, and L. Xiao, "RF Distillation Diffusion Model: An Efficient RFF Data Augmentation Method," in *IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2025, pp. 1–5.

[5] B. Xiong, Y. Zhong, W. Pu, and F. Zhang, “Radio Frequency Finger-
prints Identification by Convolutional Neural Network with Data Aug-
mentation,” in *6th International Conference on Electronic Engineering and Informatics (EEI)*, 2024, pp. 1394–1397.

[6] X. Yang, D. Li, F. Zhou, and N. Al-Dhahir, “A Channel Robust RF Fingerprint Identification Scheme for LTE Devices Based on DMRS Signals,” in *2023 IEEE Global Communications Conference*, 2023, pp. 3439–3444.

[7] G. Shen, J. Zhang, A. Marshall, and J. R. Cavallaro, "Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa," *IEEE Transactions on Information Forensics and Security*, vol. 17, pp. 774-787, 2022.

[8] A. Elmaghbub and B. Hamdaoui, "LoRa Device Fingerprinting in the Wild: Disclosing RF Data-Driven Fingerprint Sensitivity to Deployment Variability," *IEEE Access*, vol. 9, pp. 142 893-142 909, 2021.

[9] A. Hussain, N. Abughanam, and P. Papadimitratos, “Towards a Lightweight Edge Al-based Radio Frequency Fingerprinting,” in *International Wireless Communications and Mobile Computing (IWCMC)*, 2025, pp. 1540–1545.

[10] R. Abayneh, M. Mutala, J. Ren, and X. Yang, “Efficient Wi-Fi Device RF Fingerprint Detection and Classification Using Wigner-Ville Distribution,” in *Proc. International Conference on Communication, Image and Signal Processing (CCISP)*, 2023, pp. 504–508.

[11] X. Qi, A. Hu, and T. Chen, “Lightweight Radio Frequency Fingerprint Identification Scheme for V2X Based on Temporal Correlation,” vol. 19, pp. 1056–1070, 2024.

[12] Q. Rui, N. Elzner, Q. Zhou, Z. Wen, Y. He, K. Yang, and T. Chi, "Lightweight Machine Learning and Embedded Security Engine for Physical-Layer Identification of Wireless IoT Nodes," in *IEEE International Conference on Communications (ICC)*, 2024, pp. 2865–2870.

[13] A. Chillet, B. Boyer, R. Gerzaguet, K. Desnos, and M. Gautier, “Tangled Program Graph for Radio-Frequency Fingerprint Identification,” in *Proc. IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)*, 2023, pp. 1–7.

[14] J. Feng, X. Tang, B. Zhang, and Y. Ren, “A Lightweight Deep Learning RF Fingerprint Recognition Method,” in *Proc. International Conference on Communications, Information System and Computer Engineering (CISCE)*, 2022, pp. 452–457.

[15] ——, “Lightweight CNN-Based RF Fingerprint Recognition Method,” in *8th International Conference on Computer and Communication Systems (ICCCS)*, 2023, pp. 1031–1035.

[16] G. Qing, H. Wang, and T. Zhang, “Radio frequency fingerprinting iden-
tification for Zigbee via lightweight CNN,” *Physical Communication*,
vol. 44, p. 101250, 2021.

[17] T. Jian, Y. Gong, Z. Zhan, R. Shi, N. Y. Soltani, Z. Wang, J. G. Dy, K. R. Chowdhury, Y. Wang, and S. Ioannidis, “Radio Frequency Fingerprinting on the Edge,” *IEEE Transactions on Mobile Computing*, vol. 21, pp. 4078–4093, 2022.

[18] E. Bothereau, R. Gerzaguet, M. Gautier, A. Chillet, and O. Berder, "Investigating Sparse Neural Networks for Radio Frequency Fingerprint Identification ," in *IEEE 100th Vehicular Technology Conference (VTC2024-Fall)*, 2024.

[19] G. Reus-Muns, D. Jaisinghani, K. Sankhe, and K. R. Chowdhury, “Trust in 5G Open RANs through Machine Learning: RF Fingerprinting on the POWDER PAWR Platform,” in *Proc. IEEE Global Communications Conference (GLOBECOM)*, 2020, pp. 1–6.

[20] S. Hanna, S. Karunaratne, and D. Cabric, “WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting,” *IEEE Access*, vol. 10, p. 22808–22818, 2022.

[21] D. Raychaudhuri, I. Seskar, M. Ott, S. Ganu, K. Ramachandran,
H. Kremo, R. Siracusa, H. Liu, and M. Singh, “Overview of the
ORBIT Radio Grid Testbed for Evaluation of Next-Generation Wireless
Network Protocols,” in *IEEE Wireless Communications and Networking
Conference*, vol. 3, 2005, p. 1664–1669.

[22] A. Elmaghbub and B. Hamdaoui, "A Needle in a Haystack: Distin-
guishable Deep Neural Network Features for Domain-Agnostic Device
Fingerprinting," in *IEEE Conference on Communications and Network
Security (CNS)*, 2023, pp. 1–9.

[23] K. Sankhe, M. Belgiovine, F. Zhou, L. Angioloni, F. Restuccia, S. D’Oro, T. Melodia, S. Ioannidis, and K. Chowdhury, “No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments,” *IEEE Transactions on Cognitive Communications and Networking*, vol. 6, no. 1, pp. 165–178, 2020.

[24] B. Li and E. Cetin, “Waveform Domain Deep Learning Approach for RF Fingerprinting,” in *IEEE International Symposium on Circuits and Systems (ISCAS)*, 2021, pp. 1–5.

[25] D. Roy, T. Mukherjee, M. Chatterjee, E. Blasch, and E. Pasiliao, “RFAL: Adversarial Learning for RF Transmitter Identification and Classification,” *IEEE Transactions on Cognitive Communications and Networking*, vol. 6, no. 2, pp. 783–801, 2020.

[26] ——, “RFAL: Adversarial learning for RF transmitter identification and classification,” *IEEE Transactions on Cognitive Communications and Networking*, vol. 6, no. 2, pp. 783–801, 2019.
[27] B. Xu, N. Wang, T. Chen, and M. Li, "Empirical evaluation of rectified activations in convolutional network," *arXiv preprint arXiv:1505.00853*, 2015.

[28] D. Kim, J. Kim, and J. Kim, "Elastic exponential linear units for convolutional neural networks," *Neurocomputing*, vol. 406, pp. 253-266, 2020.

[29] M. Lee, "Mathematical analysis and performance evaluation of the gelu activation function in deep learning," *Journal of Mathematics*, vol. 2023, no. 1, p. 4229924, 2023.

## BIOGRAPHY

**Emma Bothereau** received the master's degree in electronics, electrical energy, automatics engineering from The University of Rennes in 2023, where she is currently pursuing the Ph.D. degree in telecommunications engineering and cybersecurity. Her research interest is wireless device identification thanks to the radio frequency fingerprint by using deep learning solutions.

**Robin Gerzaguet** received the degree in electrical engineering from the Grenoble Institute of Technology, France, in 2011, and the Ph.D. degree from the Gipsa-Laboratory, University of Grenoble and ST-Microelectronics, in 2015. He then joined CEA-LETI, where he was involved in physical layer and filter bank multicarrier waveforms in the context of millimeter-wave transmissions. He is currently an Associate Professor with the IRISA Laboratory, University of Rennes, France. His research interests are in signal processing techniques and hardware architectures dedicated to digital communications and RF transceivers. They include multicarrier waveforms, digital RF impairments compensation and TEMPEST attacks.

**Matthieu Gautier** holds the Engineering and M.Sc. degrees in electronics and signal processing engineering from the ENSEA graduate school and a Ph.D. degree from Grenoble-INP in 2006. From 2006 to 2011, he was a research engineer at Orange labs, INRIA and CEA-LETI laboratory. He joined University of Rennes in 2011 where he is currently a Full Professor at IUT Lannion and the IRISA Laboratory since 2024. His research activities are in the two complementary, fields of embedded systems and digital communications for high-performance software-defined radio.

**Alice Chillet** received the master's degree in Electronics, Electrical Energy, Automatics engineering from the University of Rennes in 2021 and the Ph.D. degree in telecommunications engineering and cybersecurity from the IRISA Laboratory, University of Rennes in 2024. In 2022, she worked with Tampere University on the Radio Frequency fingerprint model. Her research interest is Wireless device identification thanks to the Radio frequency fingerprint by using deep learning solutions.

**Olivier Berder** received the M.Sc. and Ph.D. degrees in electrical engineering from the University of Bretagne Occidentale, Brest, in 1999 and 2002, respectively. From 2002 to 2004 he was with the Laboratory for Electronics and Telecommunication Systems (LEST-UMR CNRS 6165), Brest. After

a post-doc with the Speech and Sound Technologies and Processes Laboratory in Orange labs, Lannion, France, in March 2005 he joined University of Rennes where he is currently a Full Professor at IUT Lannion, and the laboratory IRISA in Rennes. He is the leader of GRANIT team and his research interests focus on cooperative techniques and power management for mobile communications and wireless sensor networks. He has co-authored more than 120 peer reviewed papers in international journals or conferences and has participated in numerous collaborative projects founded by either European Union or French National Research Agency.