# Data Diversity for a Channel-Resilient Training Database for Radio Frequency Fingerprint Identification

Alice Chillet, Robin Gerzaguet, Karol Desnos, Paul Bazerque, Erwan Nogues, Matthieu Gautier

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Alice Chillet, Robin Gerzaguet, Karol Desnos, Paul Bazerque, Erwan Nogues, et al. Data Diversity for a Channel-Resilient Training Database for Radio Frequency Fingerprint Identification. The Third Workshop on Machine Learning and Deep Learning for Wireless Security at the IEEE International Conference on Communications, Jun 2025, Montreal, Canada. hal-05206721

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Submitted on 11 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Data Diversity for a Channel-Resilient Training
Database for Radio Frequency Fingerprint
Identification

Alice CHILLET†, Robin GERZAGUET†, Karol DESNOS*, Paul BAZERQUE†, Erwan NOGUES§,
and Matthieu GAUTIER†

† Univ Rennes, CNRS, IRISA, firstname.lastname@irisa.fr

* Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164, Rennes, France, firstname.lastname@insa-rennes.fr

§ DGA-MI

*Abstract*—This paper proposes to improve the resilience of the Radio Frequency Fingerprint (RFF) identification to the propagation channel effects by massively diversifying the transmitted data. RFF refers to a unique signature resulting from signal distortions caused by the imperfections of the hardware transmission chains. In recent years, RFF identification has mainly relied on supervised Deep Learning (DL) techniques, which require an unbiased training database to avoid overfitting the propagation channel environment. This can be achieved during signal acquisition by acquiring many different channel realisations. However, designing such a database is very time-consuming, mainly because of the process of increasing the channel diversity. In this paper, we propose to change the paradigm of data diversity: instead of adding diversity to the channel, we choose to add diversity to the data. After a primary study to motivate the contribution, this paper presents, a proof of concept, which is carried out using model-based RF transmission databases, showing an improvement in data acquisition time of up to 16 times. A real database is then created using Software Defined Radios and the experimental results are compared with the simulated ones.

***Index Terms***—Deep Learning, Radio Frequency Fingerprint, RF impairments models, Software Defined Radio.

# I. INTRODUCTION

Radio Frequency Fingerprint (RFF) identification leverages the unique characteristics of the analog components of communicating devices to identify them. The RFF is defined by electromagnetic distortions within the transmission chain, and can be used as a secure identifier. While RFF identification principle, based on parametric estimations, was born in the 2000s, the number of RFF identification contributions by classification methods has recently exploded with the rise of Deep Learning (DL) techniques [1], [2]. In particular, supervised DL has been extensively used in RFF classification, as it automatically learns how to classify radio transmitters by recognizing complex patterns from labelled signals. The labelled signals are mainly composed of *Preamble* data, meaning that the transmitted data is always the same for all transmissions and all considered transmitters.

The classification accuracy of DL methods dramatically depends on the database used to train the network. Many

experimental studies have shown promising results in static propagation channel conditions [1], [3], [4]. However, in dynamic channel conditions, RFF classification becomes more challenging due to the channel disturbances [1], [4]. Three approaches are proposed in the state-of-the-art (SoA) to tackle this issue. (i) First, channel equalization [1] can mitigate the channel effects, but it requires a demodulation chain and a thorough knowledge of the used modulation scheme. Moreover, this approach can delete a part of RFF. (ii) Second, controlled impairments can be introduced at the transmitter side to enhance identification robustness [4]. (iii) Third, a large database with different propagation conditions for each transmitter can be used to train the network with a strong channel diversity. For instance, Morin et al. [5] created a dataset with a moving receiver and [4] recorded signals at different distances. This technique increases the channel diversity and improves the classification accuracy in different channel conditions at the price of an important dataset creation complexity. To reduce the recording time a virtual data augmentation has been proposed in [6], [7] by adding a propagation channel model at the receiver side as pre-processing. However, the best and most popular solution is channel diversity based on mobile devices. According to several works, this solution seems to be robust to improve the classification accuracy up to 40% [5], [6], [8]. However, it requires a long process to capture enough signals to create a sufficiently diverse training database for each device and so a long access to the targeted devices that can be sensitives.

In this paper, we propose a shift in the data diversity paradigm: rather than introducing diversity through varying channel conditions, we add diversity to the data. Since RFF identification relies on DL networks, the training signals must exhibit extensive variability to ensure accurate RFF recognition and prevent bias. Our approach aims to reduce database biases and achieve the necessary diversity without having to change the transmission conditions multiple times.

In a first time, we propose to use RiFyFi [9], [10] a virtual database generator to present a proof of concept and use it as
a sizing tool for our work.

The core contributions of this paper are as follows:

* Studying the limits of the channel based diversity using model-based databases.

* Using a model-based database to present our solution, and the interest compare to SoA techniques.

* Comparing model-based and experimental-based results, that exhibits similar conclusions and demonstrates the interest of the approach.

The structure of this paper is as follows: Section II presents the RFF identification and channel resilience with DL classification. Section III presents the common channel diversity method for propagation channel resiliency. Then section IV introduces a proof of concept of diversifying data instead of channel thanks to model-based datasets. Section V showcases our experiments and results with data diversity based on experimental datasets. Finally, the paper concludes in Section VI.

## II. RFF CLASSIFICATION

### A. RF transmission chain and RFF

Before the transmission, the source information data is converted into symbol sequences which are then transformed into IQ samples and transmitted through the RF chain. A Digital-to-Analog Converter (DAC) transforms the signal into the analog domain to yield $x(t)$. The Local Oscillator (LO) modulates the signal at the carrier frequency $f_{RF}$, and the Power Amplifier (PA) amplifies it. All those components distort the signal and create the signature called the RFF of the transmitter $Tx$ denoted $\mathcal{F}_{RFF_{Tx}}$.

The signal is then transmitted over a propagation channel, either wired or wireless. The propagation channel impacts the signal due to additive noise, signal interference, or multi-path and fading channels. The channel is modeled by $\mathcal{F}_{channel}$. Although the receiver has an RFF, investigating its impact is beyond the scope of this paper. The received signal $x_{idf}(t)$ can thus be expressed as

$$x_{idf}(t) = \mathcal{F}_{channel} \circ \mathcal{F}_{RFF_{Tx}}(x(t)). \quad (1)$$

where $\circ$ represents the function composition operator, with $h(x) = g \circ f(x)$ denoting $h(x) = g(f(x))$. The disturbances introduced by $\mathcal{F}_{channel}$ make the transmitter RFF recognition difficult. A channel-resilient identification approach is therefore needed to ensure the transmitter recognition in different propagation conditions.

### B. RFF databases and resilience study method

In this paper, we propose to study the resilience of the RFF identification solutions. The study is conducted first with model-based databases and then with experimental-based databases. The model-based databases allow the exploration of different scenarios, which is useful to define the database parameters before creating an effective experimental database based on real world transmission records, for example the amount of data required to train the network. These model-based databases are created thanks to RiFyFi [9], [10], a recent

<figure><img src="image_2.png" /><figcaption>Fig. 1. Overview of resilience study method.</figcaption></figure>

virtual database generator for RFF identification. Experimental databases are created in the lab by capturing signals from our own Software Defined Radios (SDRs). Both databases are described in the experimental sections.

An RFF database is defined by several parameters: the number of transmitters $N_{Tx}$, the number of sequences/signals per transmitter $N_{sig}$, the propagation channel scenario, and the data-type mode (Preamble or Payload). This mode defines the type of symbol sequences transmitted: a fixed pattern for Preamble and random sequence for Payload. The modulation used in this paper is Orthogonal Frequency Division Multiplexing (OFDM), with same parameters as in [10]. Each sequence/signal consists of 256 IQ samples, which is typical in the SoA and serves as the input to the DL network.

To evaluate the resilience of the network under different conditions, multiple datasets, called scenarios, are required to test the network under different environmental conditions compared to the training one. Figure 1 illustrates the 8 stages involved in the process of studying the network resilience:

* 1. Creating a database (model-based or experimental) following a propagation channel scenario $S_1$;

* 2. 3. 4. Using this database to train the DL network;

* 5. Creating a new database with another scenario $S_k$;

* 6. 7. 8. Using this new database to evaluate the resilience of the DL network in the scenario $S_k$.

The performance results in $S_1$ and $S_k$ are presented in this paper for both types of database (model-based and experimental) for both modes (Preamble and Payload). $S_1$ is a scenario with a perfect propagation channel (wired for example) while $S_k$ with $k \neq 1$ corresponds to different scenarios presented in the experimental sections.

### C. RFF identification with deep learning

To identify devices using DL, the network is trained on labelled data. As in classical methods, the database is divided into training (90%) and test (10%) sets. The network predicts labels for each signal, which are then compared to the actual labels. Classification performance is evaluated with the F1-score [10]. Cross-entropy is used as a loss function.
<figure><img src="image_3.png" /><figcaption>Fig. 2. Deep Learning Architecture.</figcaption></figure>

The network used in this paper has been previously proposed and studied for RFF identification in [3], [11], representing one of the earliest contributions to RFF using DL solutions and a public database. It is a Convolutional Neural Network (CNN) with 4 Convolutional Layers (CL). Each layer is composed of two blocks of 128 filters of size 7 × 1 and 5 × 1 and a max-pooling stage. The activation function is ReLu and the optimizer is Adam. After the 4 CL, the CNN has 3 fully connected layers with, respectively, 256 nodes, 128, and the number of transmitters to identify $N_{Tx}$, in this case, 5 classes.

The network takes complex baseband signals as inputs without pre-processing. These signals are split in two raw I and Q data, with an input size $N$ of 256 samples, as defined in [3], [12]. This architecture is presented in Figure 2 and has 1,232,774 parameters.

### III. CHANNEL RESILIENCE WITH COMMON CHANNEL DIVERSITY

This section addresses the need for propagation channel diversity based on SoA results and a simulation study. The aim is to highlight the limitations of the SoA channel resilience methods and to motivate the paradigm shift toward achieving data diversity. The results presented here are obtained using RiFyFi model-based databases.

#### A. State of the art

Several works have shown the need to improve the resilience to the propagation channel [3]–[6]. For example, Morin et al. [5] present their experimental set-up called Cortex-Lab, and showed that incorporating varied multipath channel parameters in the training dataset improved network resilience in different transmission scenarios by up to 44% compared to static training, referred to as "plain" in their experiments. However, increasing the diversity requires a lengthy process of recording signals under various transmission conditions to build up the training dataset, and lacks flexibility for security application. Therefore, in this paper, we use the RiFyFi virtual database generator to investigate the network robustness, and enable comprehensive monitoring of all aspects of the experimental setup.

<table><caption>TABLE I
F1-SCORE OBTAINED IN DIFFERENT TRAINING AND SIMILARITY SCENARIOS TO EVALUATE THE RESILIENCE, PREAMBLE MODE.</caption><thead><tr><th rowspan="2">p%</th><th colspan="3">Training on S<sub>1</sub><br>without channel diversity</th><th colspan="2">Training on S<sub>2</sub><br>with channel diversity</th></tr><tr><th>N<sub>sig.</sub></th><th>Test S<sub>1</sub></th><th>Test S<sub>2</sub></th><th>N<sub>sig.</sub></th><th>Test S<sub>2</sub></th></tr></thead><tbody><tr><td>10%</td><td>10000</td><td>98%</td><td>27%</td><td>1800</td><td>84.5±0.8%</td></tr><tr><td>7%</td><td>10000</td><td>92%</td><td>24%</td><td>1800</td><td>74.8±0.6%</td></tr><tr><td>5%</td><td>10000</td><td>91%</td><td>23%</td><td>1800</td><td>67.6±0.2%</td></tr><tr><td>3%</td><td>10000</td><td>89%</td><td>23%</td><td>2700</td><td>69.2±0.3%</td></tr></tbody></table>

#### B. Model-based database leveraging RiFyFi

RiFyFi is a framework with a virtual database generator, that allows database design for RFF identification based on RF transmission models with impairments. In this paper, the impairments considered in the model are the LO effects which are Carrier Frequency Offset (CFO), IQ imbalance and Phase Noise (PN), and the PA non-linearity effects. Different RFF similarity levels are modeled and are represented with a percentage noted p%. This percentage reflects the difference between the RFF of the transmitters: the higher the percentage, the greater the difference between the transmitters. The different models and values use for experiment are given in [10] and accessible on github [9]. The databases used in this section are composed of 5 different transmitters and the size of database depends to $N_{sig}$: the number of signals that composed the dataset. While RiFyFi supports two different transmission modes: Preamble and Payload, only the Preamble mode will be considered in this section. Two scenarios are considered: scenario $S_1$, which models a wired transmission, is used to train the network and evaluate its static performance, and then scenario $S_2$ models a wireless flat-fading transmission over a path with random power, and several channel powers are generated.

#### C. Utility of channel diversity

Thanks to RiFyFi, different datasets have been created with different RFF similarity levels of 10%, 7%, 5% and 3%. The transmitted data corresponds to the Preamble mode. The training is first realised with scenario $S_1$ which models an ideal transmission with 30dB SNR. The network learning rate is set to $10^{-5}$, the dropout $dr$ to 0.25 and the batch size to 64. The network performance is then test on $S_1$ and $S_2$. The left part of Table I called "Training on $S_1$" gives the F1-scores obtained in Test for the different p% similarity scenarios for $S_1$ and $S_2$ scenarios. The scenario $S_2$, models 100 different wireless transmission with random propagation channels and allow to evaluate the resilience of network over multiple propagation channel realisations. In column 2, $N_{sig}$ gives the number of signals used for training.

The results show that the performance decreases as the transmitters similarity increases. Testing on scenario $S_2$ shows that despite correct classification in the ideal scenario $S_1$, the channel resilience is very poor.

The right part of Table I shows the F1-scores obtained in the test on $S_2$ when training was performed with a large diversity
of wireless propagation channel realizations. This simulation corresponds to the SoA solution. The number of channels in training has been empirically explored, in the table the results are obtained with 100 channels in training data-sets (different to the testing channels) and $N_{sig}$ signals per channel. The F1-score is obtained by mean 5 seeds of data generation and training. The results reveal that over 100 channels per transmitter are required, meaning capturing signals from each transmitter at 100 different locations, which is a too long and complex process for many RFF identification applications.

For instance, changing the propagation channel context for a transmitter takes just 5 seconds. This means a new record with a different propagation channel is created every 5 seconds. Collecting sufficient data for a single transmitter requires over 500 seconds (approximately 8 minutes). However, if the process isn't automated and manual context switching takes a full minute, the time required per transmitter would exceed 1 hour and 30 minutes or even more.

# IV. DIVERSIFYING DATA TO ENSURE ROBUSTNESS AND RESILIENCE

## A. Methodology

We propose to change the paradigm of data diversity to reduce the complexity and time required to build a training database while ensuring network resilience. The channel diversity traditionally used generates entropy, which helps the network to be resilient to environmental influences. The top of Figure 3 presents conventional data diversity, used in the previous section, with a fixed data *Preamble* and channel diversity. In our approach, diversity is directly introduced into the transmitted data, increasing the entropy of the signals used for identification. The bottom of Figure 3 shows our solution with an ideal propagation channel, such as a wired one, which simplifies the process of recording signals from different transmitters with sufficient diversity.

In practice, long sequences of random symbols are generated and transmitted. This transmission mode, called *Payload*, adds data diversity. This large database is then used to train the network to recognise the RFF in a noisy and diverse propagation environment.

<figure><img src="image_4.png" /><figcaption>Fig. 3. Increase entropy of training dataset by adding diversity in transmitted data.</figcaption></figure>

To present a proof of concept, experiments are firstly conducted using model-based databases. These databases are created according to the previously described parameters, including different similarity scenarios and a *Payload* mode. Two transmission scenarios are used: $S_1$ for the wired transmission model, and $S_2$ for the wireless propagation model with multiple paths and noise levels.

## B. Simulation-based proof of concept with data diversification

Three large databases with 10%, 7% and 5% similarity have been created, each consisting of random OFDM symbols to obtain 180,000 signals of 256 IQ samples. The network is trained with $S_1$ scenario using a learning rate of $10^{-5}$ and a dropout $dr = 0.25$. After training, the network is evaluated in both scenarios $S_1$ and $S_2$. Table II shows the network F1-scores following both scenarios. We chose to create 180,000 signals to match the diversity achieved with 1,800 signals and 100 channels per signal, as in traditional channel diversity solution. The results show that a very large dataset is indeed necessary to perform identification. However, only one record per transmitter is required, which is a great simplification compared to the usual channel diversity, where 100 recordings per transmitter are typically required. Using this approach, recording 180,000 signals per transmitter takes approximately 30 seconds, making it at least 16 times faster (assuming an optimistic recording time of every 5 seconds) than the previously described method. Table III shows the confusion matrix obtained in test in scenario $S_2$ for a 10% similarity. The network occasionally confuses transmitters, in particular each transmitter $i$ are often confused with the $i-1$ and $i+1$ due to the close proximity in the impairment definition [9].

<table><caption>TABLE II
F1-SCORE OBTAINED IN DIFFERENT SIMILARITY SCENARIOS TO EVALUATE THE RESILIENCE WITH DATA DIVERSITY THANKS TO PAYLOAD MODE.</caption><thead><tr><th rowspan="2">p%</th><th colspan="3">Training on S<sub>1</sub><br>with data diversity</th></tr><tr><th>N<sub>sig.</sub></th><th>Test S<sub>1</sub></th><th>Test S<sub>2</sub></th></tr></thead><tbody><tr><td>10%</td><td>180000</td><td>77%</td><td>59%</td></tr><tr><td>7%</td><td>180000</td><td>60%</td><td>47%</td></tr><tr><td>5%</td><td>180000</td><td>45%</td><td>39%</td></tr></tbody></table>

<table><caption>TABLE III
CONFUSION MATRIX OBTAINED FOR 10% SIMILARITY SCENARIO IN TEST S<sub>2</sub>, PAYLOAD MODE.</caption><thead><tr><th>True</th><th>Tx1</th><th>Tx2</th><th>Tx3</th><th>Tx4</th><th>Tx5</th></tr></thead><tbody><tr><th>TxTrue<sub>1</sub></th><td>69.7</td><td>25.4</td><td>3.0</td><td>0.5</td><td>1.4</td></tr><tr><th>TxTrue<sub>2</sub></th><td>17.9</td><td>51.0</td><td>25.5</td><td>3.8</td><td>1.8</td></tr><tr><th>TxTrue<sub>3</sub></th><td>2.4</td><td>17.6</td><td>53.4</td><td>22.1</td><td>4.4</td></tr><tr><th>TxTrue<sub>4</sub></th><td>1.3</td><td>1.7</td><td>18.1</td><td>49.8</td><td>29.1</td></tr><tr><th>TxTrue<sub>5</sub></th><td>1.1</td><td>0.4</td><td>2.5</td><td>22.0</td><td>74.0</td></tr></tbody></table>
# V. EXPERIMENTS

## A. *Experimental database*

To validate the results obtained using the model-based datasets, we create an experimental set-up with six SDRs, five are used as transmitters, and one, always the same, is used as a receiver. Table IV provides details on each transmitter: their name, references, and the label assigned in the datasets. The transmitters are all different except the two E310 devices. However, for one of them (Tx3), the GPS has been deactivated which likely results in a less stable CFO compared to the other. The transmission power of each SDR is adjusted and a normalisation per group of sequence is applied to ensure uniform received power levels, preventing the network from relying on power discrepancies, which can vary under different channel conditions. In order to assess the network's ability to detect transmitters in real conditions, we create different data sets. For each transmission scenario, the two different modes are considered: *Preamble* and *Payload*.

<table><caption>TABLE IV
TRANSMITTERS USED TO CREATE THE EXPERIMENTAL DATASET.</caption><thead><tr><th>Label</th><th>Name of transmitter</th><th>Constructor</th><th>RF daughter board</th></tr></thead><tbody><tr><td>Tx1</td><td>Blade RF</td><td>Nuand</td><td>AD9361</td></tr><tr><td>Tx2</td><td>ADALM Pluto</td><td>Analog device</td><td>AD9363</td></tr><tr><td>Tx3</td><td>E310 (no gps)</td><td>ETTUS</td><td>AD9361</td></tr><tr><td>Tx4</td><td>E310</td><td>ETTUS</td><td>AD9361</td></tr><tr><td>Tx5</td><td>X310</td><td>ETTUS</td><td>UBX-160</td></tr></tbody></table>

In the *Preamble* scenario, all transmitters send the same sequence of 30 OFDM symbols, continuously repeating this sequence for 8 seconds. In the *Payload* mode, each transmitter sends random OFDM symbols for a duration of 30 seconds.

Five different transmission scenarios are then defined, presented in Table V:

* $S_1$: All transmitters send data through the same wire and the same attenuator, and each transmitter is assumed to be cold (i.e turned on only to perform the transmission). This scenario is used as the network training dataset, while all remaining scenarios are only used for testing resilience.

* $S_{1bis}$: Identical to $S_1$, but conducted on a different day to verify the reproducibility of the ideal scenario.

* $S_2$: Same setup as $S_1$, but the transmitters are turned on in the morning and the recordings are done in the afternoon. The components of the transmitters are considered to be hot, which can affect the RFF in particular the CFO.

* $S_3$: Transmission is performed Over-The-Air (OTA) within an Anechoic Chamber (AC), with all the transmitters placed at the same location.

* $S_4$: Same as $S_3$, but each transmitter has a different location.

* $S_5$: An OTA scenario conducted in an office environment, with transmitters placed in different locations.

## B. *Experimental results*

The network is trained using data from $S_1$, multiple training are performed for different dataset sizes. For *Preamble*

<table><caption>TABLE V
EXPERIMENTAL SCENARIOS DESCRIPTION.</caption><thead><tr><th>Train/test</th><th>Scenario</th><th>Channel</th><th>hot/cold</th><th>Tx location</th></tr></thead><tbody><tr><td>Train</td><td>1</td><td>Wired</td><td>cold</td><td>-</td></tr><tr><td>Test</td><td>1bis</td><td>Wired</td><td>cold</td><td>-</td></tr><tr><td>Test</td><td>2</td><td>Wired</td><td>hot</td><td>-</td></tr><tr><td>Test</td><td>3</td><td>OTA AC</td><td>cold</td><td>same place</td></tr><tr><td>Test</td><td>4</td><td>OTA AC</td><td>cold</td><td>different locations</td></tr><tr><td>Test</td><td>5</td><td>OTA office</td><td>cold</td><td>different locations</td></tr></tbody></table>

mode, dataset sizes range from 9,000 to 45,000 signals per transmitter, while for *Payload* mode, sizes range from 9,000 to 180,000 signals per transmitter. The training ends when the network reaches a F1-score of 98% on training data. Then, each network is evaluated on the testing dataset of each scenario. The testing datasets consist of 5,000 signals per transmitter, taken from the end of the recorded signals. The learning rate is set as $\gamma = 10^{-4}$, the dropout set as $dr = 0.5$, and the batch size as 64.

Table VI shows the F1-score obtained in the *Preamble* mode for each scenario and for different sizes $N_{sig}$. of the training dataset. The F1-score value is averaged over three seeds of the network. Column 1 shows the results achieved using the test part of $S_1$, while column 1bis provides results from a second recording performed several days later to ensure the reproducibility of the ideal case. Results in columns 1 and 1bis show that increasing $N_{sig}$, in the training dataset consistently improves the F1-score in test for both cases.

The comparison of the F1-score obtained with scenarios $S_1/S_{1bis}$ versus $S_2$ shows a drop in classification performance. This suggests that the temperature of the SDR, whether hot or cold, has a relative impact on the performance. However, increasing $N_{sig}$, in the training set mitigate this drop.

The network evaluation on $S_5$ shows a poor performance, and the decrease in performance is noticeable as the scenario difficulty increases. However, this result was expected because the network was trained with an ideal scenario. This experiment corresponds to the results obtained in Table I column 4. Here, increasing the database size has no effect on the results because the data lacks diversity. The results obtained for $S_4$ are surprising, but only two transmitters are always correctly identified, giving 40% of the F1-score. This Table is useful to compare the two modes.

To improve the channel resiliency with *Preamble* scenario

<table><caption>TABLE VI
F1-SCORE OBTAINED IN TEST FOR EACH SCENARIO IN PREAMBLE MODE, DEPENDING ON THE NUMBER OF SIGNALS USED TO TRAIN THE NETWORK.</caption><thead><tr><th rowspan="2">N<sub>sig.</sub></th><th colspan="6">F1-score obtained for testing scenarios</th></tr><tr><th>1</th><th>1bis</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>9,000</td><td>76.7%</td><td>89.3%</td><td>74.5%</td><td>43.7%</td><td>40.0%</td><td>27.0%</td></tr><tr><td>18,000</td><td>95.8%</td><td>96.4%</td><td>86.7%</td><td>41.0%</td><td>40.0%</td><td>20.3%</td></tr><tr><td>27,000</td><td>97.9%</td><td>97.2%</td><td>90.0%</td><td>34.5%</td><td>40.0%</td><td>19.8%</td></tr><tr><td>36,000</td><td>98.4%</td><td>97.5%</td><td>92.2%</td><td>40.8%</td><td>40.0%</td><td>20.0%</td></tr><tr><td>45,000</td><td>97.8%</td><td>97.3%</td><td>89.3%</td><td>41.5%</td><td>40.0%</td><td>24.9%</td></tr></tbody></table>
<caption>TABLE VII</caption>

F1-SCORE OBTAINED IN TEST FOR EACH SCENARIO IN PAYLOAD MODE, DEPENDING ON THE NUMBER OF SIGNALS USED TO TRAIN THE NETWORK.

<table><thead><tr><th rowspan="2">N<sub>sig.</sub></th><th colspan="6">F1-score obtained for testing scenarios</th></tr><tr><th>1</th><th>1 bis</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>9,000</td><td>42.4%</td><td>47.8%</td><td>56.9%</td><td>37.8%</td><td>36.9%</td><td>22.3%</td></tr><tr><td>18,000</td><td>75.4%</td><td>73%</td><td>78.5%</td><td>53.2%</td><td>45.6%</td><td>42.2%</td></tr><tr><td>45,000</td><td>91.1%</td><td>89%</td><td>88.7%</td><td>57.6%</td><td>54.9%</td><td>48.4%</td></tr><tr><td>90,000</td><td>94.8%</td><td>92.9%</td><td>93.3%</td><td>59.6%</td><td>53.5%</td><td>50.8%</td></tr><tr><td>180,000</td><td>96.4%</td><td>94.5%</td><td>95.2%</td><td>60.46%</td><td>53.5%</td><td>50.4%</td></tr></tbody></table>

channel diversity should be added. However, as the process of creating a database is time-consuming, we prefer to analyze the data diversity results obtained using the *Payload* mode. Table VII presents the results obtained for each scenario and for different sizes of the training dataset. Column 1 shows the results obtained with the test part of $S_1$. Compared to *Preamble* mode, the *Payload* mode requires a higher number of signals to reach the same accuracy in $S_1$, coherently with conclusions from model-based data. The results obtained in columns 1, 1bis and 2 are very similar to those obtained in the *Preamble* mode. Scenarios 3 to 5 obtain better results in *Payload* mode than in *Preamble* mode, and increasing the database size improves the performance. This means that the data diversity added by the *Payload* mode improves the network robustness without channel diversity in the training set. However, when the propagation environment introduces harsh distortions, the quality of the performance is not sufficient and pre-processing could further improve the signal quality and hence the classification accuracy.

Table VIII shows the confusion matrix obtained during the test for scenario $S_5$ in *Payload* mode. It shows that the classification results are not as regular as the ones obtained by simulation, likely due to difference between transmitters. For instance, Pluto SDR Tx2, known for its low quality, is easier to distinguish than X310 Tx5, recognized for its high precision. This suggests that shifting the diversity approach, as proposed in this paper, could be useful for identifying different types of transmitters with distinct RFF characteristics, such as the Pluto and the E310 apparently have.

<caption>TABLE VIII</caption>

CONFUSION MATRIX OBTAINED FOR *Payload* EXPERIMENTAL DATABASE IN TEST $S_5$ SCENARIO

<table><thead><tr><th>Guess True</th><th>Tx1</th><th>Tx2</th><th>Tx3</th><th>Tx4</th><th>Tx5</th></tr></thead><tbody><tr><th>TxTrue1</th><td>0.0</td><td>46.3</td><td>0.1</td><td>11.1</td><td>42.5</td></tr><tr><th>TxTrue2</th><td>1.9</td><td>90.8</td><td>0.2</td><td>6.8</td><td>0.2</td></tr><tr><th>TxTrue3</th><td>5.0</td><td>0.0</td><td>79.8</td><td>15.1</td><td>0.1</td></tr><tr><th>TxTrue4</th><td>0.7</td><td>0.0</td><td>1.4</td><td>61.8</td><td>36.1</td></tr><tr><th>TxTrue5</th><td>36.5</td><td>0.0</td><td>6.7</td><td>35.7</td><td>21.1</td></tr></tbody></table>

## VI. CONCLUSION

This paper proposes an alternative and less complex approach for creating RFF DL identification training databases. Our solution shifts the focus from channel diversity to data

diversity to enhance network resilience to varied propagation environments. The results and analysis show significant performance improvements compared to similar *Preamble* data recording methods. In addition, our solution is at least 16 times faster than SoA channel diversity methods. While this solution alone does not fully address all identification needs, it reliably distinguishes between different types of transmitters, making it particularly useful in applications where precise recognition is less critical and identifying the transmitter type is sufficient. In a future work we can investigate more efficient methods of generating *Payload* data. This will ensure that the data adequately reflects the characteristics of the transmitter and that the network can be effectively trained.

## ACKNOWLEDGEMENT

This work is funded by DGA and Brittany region under the Creach Lab founding and by the French National Research Agency (ANR) under the grant number ANR-22-CE25-0007-01 (RedInBlack project).

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