# Lightweight RF Fingerprint Identification: the Revenge of the Fully Connected Neural Networks?

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

► To cite this version:

Emma Bothereau, Robin Gerzaguet, Matthieu Gautier, Alice Chillet, Olivier Berder. Lightweight RF Fingerprint Identification: the Revenge of the Fully Connected Neural Networks?. Second Workshop on Physical Layer Security for Wireless Communications at IEEE PIMRC, Sep 2025, Istanbul, Turkey. hal-05395705

HAL Id: hal-05395705

https://hal.science/hal-05395705v1

Submitted on 3 Dec 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
# Lightweight RF Fingerprint Identification:
the Revenge of the Fully Connected Neural
Networks?

Emma BOTHEREAU*, Robin GERZAGUET*, Matthieu GAUTIER*, Alice CHILLET*, Olivier BERDER*

* Univ Rennes, CNRS, IRISA, firstname.name@irisa.fr

***Abstract***—Recent advances in radio frequency fingerprint identification (RFFI) show promise in enhancing the physical-layer security that is crucial for Internet of Things (IoT) devices. However, current methods rely on deep learning, which is resource-intensive for embedded devices. Convolution layers are widely used for RFFI and are the reason these methods are computationally complex. This paper calls into question the utility of these layers for RFFI and demonstrates that networks without convolutional layers, such as fully connected neural networks (FCNNs), are highly effective. When evaluated on 4 state-of-the-art RFFI datasets, it is found that the selected FCNNs require at least five times fewer floating-point operations (FLOPs) and four times less inference time than classical deep learning architectures. Depending on the dataset, the loss in F1-score ranges from 0.03% to 0.89% compared to the best-performing networks.

***Index terms*** - Radio Frequency Fingerprint Identification, Deep Learning, Lightweight ML, Fully Connected Neural Networks

## I. INTRODUCTION

Radio-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) has revitalized RFFI in the last few years, offering more robust and flexible recognition capabilities than traditional approaches. Mostly based on Convolutional Neural Networks (CNNs), these methods are computationally demanding. To address this issue, lightweight ML methods have been developed to reduce computational costs, memory costs, and/or training or inferencing times. Some lightweight RFFI methods utilize ML algorithms incorporating prior knowledge of signal characteristics and engineered functions [1], [2]. Other lightweight methods involve employing Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU) blocks [3] in the neural network architecture. RNNs, LSTM and GRU all exploit the temporal information embedded in the raw signals. However, while these neural networks can reduce the number of parameters, they may increase the required time for inference and the associated computational cost (estimated with FLOPs, Floating-point Operations). To create lighter networks, optimized layers such as space-separable convolution layers can be used, which reduce the number of multiplications and parameters required

in the convolutional layers [4]. Other methods can be employed, starting with usual networks and then reducing their complexity such as transfer learning techniques [5] or pruning methods [6], [7].

Originally designed for image classification, CNNs are typically composed of two parts: the convolutional layers and the Fully Connected (FC) layers. The first section of the network is meant to extract features from the data, while the second section aims to use these features to classify inputs. However, the convolutional layers are computationally expensive in comparison to FC layers and are designed for image processing, therefore their efficiency on raw IQ signals remains uncertain.

Fully connected neural networks (FCNNs), which employ only FC layers, are rarely used in a RFFI context. They use given processed characteristics as inputs or directly the raw signal. While some of these architectures show great potential compared to CNNs [8], they are paired with large input sizes, often resulting in the development of heavier networks in terms of parameters.

The key contributions of this paper consist in:

* Comparing classical and lightweight deep learning methods for RFFI across various open RF fingerprint datasets. F1-score, number of parameters, number of FLOPs and inference time are analyzed.

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

* Presenting a lightweight FCNN architecture suitable for RFFI, with satisfactory performance on all tested datasets.

The paper is organized as follows: Section II introduces the state-of-the-art databases and networks for RFFI, and their respective performance is evaluated in Section III. Section IV explores the design space of FCNN for each dataset, then in Section V FCNN (ours) is proposed as an overall good performing network for RFFI, before Section VI concludes the work.
## 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. We assume that there is a single receiver, and thus the impact of the fingerprint of the receiver is not considered. Considering the $i^{th}$ emitter, with its corresponding fingerprint $F_i$, the received signal $x_i$, depending on the original signal $x$ can be expressed as $x_i(t) = F_i(x(t))$.

### B. RF Fingerprint datasets

To conduct a comprehensive study, we selected 4 of the most complete open access datasets for RFFI. 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 S1 dataset is split in two with 90% for training and the remaining 10% for testing. These datasets contains raw demodulated IQ signals and have been resized so that all signals have the same length. Consequently, all signals are of size (2, N), with N representing the temporal length of the signal and 2 representing both channels (I and Q). Each dataset contains M transmitters (M different for each dataset).

The selected datasets are:

* POWDER - WiFi [9] consists of recordings collected on two distinct days. The dataset includes 4 transmitters (USRP X310) and approximately 11,700 signals per day. The scenarios considered are Day 1 for S1 and Day 2 for S2.

* WiSig - ManySig [10], [11] contains Wi-Fi recordings collected over 4 days spread out over the course of a month. It features 6 transmitters (Atheros AR5212/AR5213) with about 1,000 signals recorded per transmitter per day. The scenarios correspond to Day 1 for S1 and Days 2 to 4 for S2.

* Stable WiFi RF Fingerprints (SWRFF) - Random [12] comprises two phases: an enrollment phase where all devices are placed one meter away from the receiver, and a deployment phase where devices are randomly moved within a 3-meter radius. This dataset includes 15 transmitters (10 FiPy and 5 LoPy). During enrollment, around 5,400 signals per transmitter are collected, while approximately 4,300 signals per transmitter are collected during deployment. S1 corresponds to the enrollment phase (morning), and S2 corresponds to the deployment phase (evening).

* ORACLE [13] features Wi-Fi signals recorded at distances ranging from 2 feet to 62 feet, with two runs per location. It includes 16 transmitters (USRP X310), with

roughly 4,000 signals per transmitter per location per run. S1 is Run 1 at 2 feet, and S2 is Run 2 at 2 feet.

### C. Neural Networks for RFFI

The neural networks used in this study are those employed in the articles associated with the selected datasets (CNN POWDER [9], CNN WiSig [10], [11], CNN SWRFF [12], and CNN Oracle [13]), alongside a lightweight neural network named CNN-GRU [3]. While CNN POWDER and CNN Oracle draw inspiration from the classical AlexNet architecture, the remaining networks fall under the category of lightweight neural networks. CNN-GRU is incorporating Gated Recurrent Units (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 SotA networks. We also introduce FCNN V1, a FCNN extracted from the final layers of CNN SWRFF. This model comprises three dense layers with 100, 64, and M neurons, with M the number of emitters to be identified. It employs LeakyReLU as the activation function and applies the softmax function before the output.

## III. PERFORMANCE OF SELECTED NETWORKS

### A. Methodology

The computations are performed on a machine equipped with a NVIDIA Tegra Orin GPU. The code is based on Julia, using the Flux library.

All networks undergo 200 epochs of training on the S1 scenario only, with early stopping. The learning rates are chosen to optimize classification scores, using the Adam optimizer, and a scheduler reduces the learning rate by 10% every 10 epochs. the trained networks are then evaluated on their F1-scores for both S1 and S2 datasets and averaged over 5 seeds. Seeds impact the train-test repartition as well as the network initialization. The batch size is set to 128, and the signal length for all networks is set to N = 256.

### B. F1-Score Evaluation

The macro F1-Score (%) [1] is used as it is more stable across imbalanced classes. The obtained F1-score for each network trained on the S1 train subset for each dataset are and evaluated on the S1 test subset (presented in Table I) and on the S2 subset (presented in Table II). The best results in terms of F1-score, per scenario, per dataset are highlighted in grey. The worst results are underlined.

<table><caption>TABLE I: F1-Scores (%) for the networks trained on S1, evaluated on S1.</caption><thead><tr><th></th><th>POWDER<br>BDD</th><th>WiSig<br>BDD</th><th>SWRFF<br>BDD</th><th>ORACLE<br>BDD</th></tr></thead><tbody><tr><td>CNN-GRU</td><td>91.73±0.16</td><td>99.34±0.38</td><td>94.69±0.67</td><td>91.52±0.85</td></tr><tr><td>CNN POWDER</td><td>99.98±0.04</td><td>99.80±0.27</td><td>99.04±0.47</td><td>99.68±0.38</td></tr><tr><td>CNN ORACLE</td><td>99.99±0.01</td><td>99.73±0.30</td><td>99.63±0.17</td><td>99.92±0.14</td></tr><tr><td>CNN SWRFF</td><td>100.00±0.00</td><td>99.87±0.14</td><td>99.68±0.18</td><td>99.76±0.21</td></tr><tr><td>CNN WiSig</td><td>99.78±0.31</td><td>99.57±0.38</td><td>99.61±0.29</td><td>98.35±0.91</td></tr><tr><td>FCNN V1</td><td>99.85±0.05</td><td>99.46±0.28</td><td>99.41±0.17</td><td>95.28±0.20</td></tr></tbody></table>
<table><caption>TABLE II: F1-Scores (%) for the networks trained on S1, evaluated on S2.</caption><thead><tr><th></th><th>POWDER<br>BDD</th><th>WiSig<br>BDD</th><th>SWRFF<br>BDD</th><th>ORACLE<br>BDD</th></tr></thead><tbody><tr><td>CNN-GRU</td><td>79.75±0.90</td><td>62.12±3.11</td><td>6.29±0.66</td><td>7.98±0.51</td></tr><tr><td>CNN POWDER</td><td>92.7±12.48</td><td>61.91±5.04</td><td>9.82±4.86</td><td>30.16±1.43</td></tr><tr><td>CNN ORACLE</td><td>91.14±4.61</td><td>58.55±6.91</td><td>6.74±2.64</td><td>29.7±2.70</td></tr><tr><td>CNN SWRFF</td><td>73.37±2.11</td><td>59.17±2.71</td><td>11.87±2.4</td><td>21.66±4.18</td></tr><tr><td>CNN WiSig</td><td>79.41±17.09</td><td>56.98±9.59</td><td>15.06±1.51</td><td>26.39±0.98</td></tr><tr><td>FCNN V1</td><td>80.61±4.46</td><td>65.52±4.07</td><td>23.34±0.53</td><td>8.51±0.22</td></tr></tbody></table>

Firstly, we observe that all networks have understandably a lower F1-Score on S2 compared to S1, as they were trained on data from the exact same context. Their behaviour on S2 also highlights a great differences between the datasets. For instance, on POWDER BDD, networks achieve a maximum mean F1-score of up to 92.7% on S2, whereas on ORACLE BDD, their performances decline significantly to at most 30.16% on S2, despite both evaluating the device in the same location but at different times. SWRFF also present a low F1-score on S2, but unlike on ORACLE BDD, this can be attributed to the change in the device location.

It is also found that while CNN-GRU performs relatively poorly across all datasets, all CNNs demonstrate strong F1-scores across all datasets on S1, with CNN SWRFF emerging as the best-performing network in terms of F1-score in the S1 scenario.

FCNN V1 has good performances on most datasets, achieving F1-scores on S1 close to the CNNs F1-score. As a smaller network, FCNN V1 can generalize better by not being overly specific to the original data. This likely explains why it is the best-performing network on WiSig and SWRFF in the S2 scenario. However, FCNN V1 is susceptible to underfitting, particularly with the ORACLE dataset. On this dataset, the tested F1-score is lower than the training F1-score, with a mean of 99.55% in training versus 95.28% in testing on S1. It probably reflects the capacity limitations of this network when dealing with more complex data.

### C. Complexity Evaluation

This paper uses the macro F1-score as the main indicator. However, to evaluate the complexity of the networks, the number of parameters, inference time, and number of FLOPs per signal are also considered. The inference time is the duration needed to predict a label for a given batch of signal. For better precision, we measured this value as the average of 1 000 predictions of a random batch. Table III presents all the complexity indicators for $M = 6$; the indicators are fairly similar for different values of $M$. The ratio between the state-of-the-art network indicators and the FCNN V1 indicators is also shown.

Although they have a similar number of parameters, CNN POWDER requires 8 times fewer FLOPs than CNN ORACLE. This difference arises because convolutional layers have a significant impact on the number of parameters, and as CNN

<table><caption>TABLE III: Complexity evaluation for all chosen networks.</caption><thead><tr><th>Model</th><th>Parameters (k)</th><th>FLOPs (k)</th><th>Inference (ms)</th></tr></thead><tbody><tr><td>FCNN V1</td><td>58</td><td>58</td><td>0.4</td></tr><tr><td>CNN POWDER</td><td>1 642</td><td>(×28.2)</td><td>8 047</td><td>(×138)</td><td>2.7</td><td>(×7)</td></tr><tr><td>CNN ORACLE</td><td>1 233</td><td>(×21.2)</td><td>65 288</td><td>(×1123)</td><td>3.5</td><td>(×9)</td></tr><tr><td>CNN SWRFF</td><td>162</td><td>(×2.8)</td><td>3 077</td><td>(×53)</td><td>3.3</td><td>(×8)</td></tr><tr><td>CNN WiSig</td><td>40</td><td>(×0.7)</td><td>514</td><td>(×9)</td><td>2.0</td><td>(×5)</td></tr><tr><td>CNN-GRU</td><td>22</td><td>(×0.4)</td><td>1 628</td><td>(×28)</td><td>28.9</td><td>(×72)</td></tr></tbody></table>

POWDER has fewer filters per layer (40 versus 128 for CNN ORACLE), its computational cost is significantly reduced. The higher number of parameters in CNN POWDER is mainly due to the larger first FC layer, which has 1024 neurons, whereas CNN ORACLE has only 256.

It is observed that all CNNs but WiSig CNN have more parameters compared to CNN-GRU or FCNN V1. However, despite CNN-GRU being the smallest network in terms of parameters, its structure tends to be proportionally more computationally expensive in terms of FLOPs. This is reflected in its inference time, which is approximately ten times longer than that of the other networks. The increased inference time and number of FLOPs are due to the recursive nature of GRU networks.

FCNN V1 demonstrates significantly faster inference time than the other networks, while also having fewer FLOPs. This advantage stems from the fact that, when predicting a signal, each FCNN parameter is used only once, unlike convolutional or recurrent layers where the same parameters are reused multiple times.

In light of the poor F1-score performance of CNN-GRU, this network will not be included in the rest of this paper.

## IV. FULLY CONNECTED NEURAL NETWORKS

### A. Methodology

FCNN V1, although not specifically designed for RFFI, has demonstrated good performance in terms of its low number of parameters, FLOPs, and inference time. To find the best architecture for FCNNs, we conducted an exploration of the exploration of the design space. We randomly generated FCNNs with a varying number of layers 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, we selected four well-performing activation functions: LeakyReLU (Leaky Rectified Linear Unit), SELU (Scaled Exponential Linear Unit), RReLU (Randomized Leaky Rectified Linear Unit), and GELU (Gaussian Error Linear Unit), as these were found to be the most efficient for our task. Furthermore, we varied the input window size $N$, setting it to 4, 8, 16, 32, 64, 128, or 256 signals. We set a threshold of 100 000 parameters as the limit of 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. Given the rapid convergence of FCNNs and the training costs associated with generating hundreds of networks, we opted to employ a reduced number of epochs in this experiment.
This exploration aims to identify the most efficient FCNN architecture for RFFI for each dataset.

## B. Random Fully Connected Neural Networks

We randomly generate at least 500 FCNNs per dataset and evaluate them on both the S1 test set and the S2 dataset. Their corresponding F1-scores are shown in Figure 1a and Figure 1b. The networks discussed previously, including FCNN V1 and CNN WiSig, are also presented.

<figure><img src="image_2.png" /><figcaption>(a) Trained and tested on S1.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>(b) Trained on S1, tested on S2.</figcaption></figure>

<caption>Fig. 1: Design space exploration of FCNNs for WiSig dataset.</caption>

A clear Pareto front can be observed on Figure 1a. This front represents the best-performing networks, balancing the F1-score (S1) and the number of parameters. A similar trend can be seen in Figure 1b. It is noteworthy that the front created by the networks tested on S2 follows the same trend as the front obtained for S1, but is lower due to the networks lack of generalisation.

As Figure 1b shows, there are three main fronts. One stagnates at around an 80% F1-score, one ranges from 70% to 75%, and the last ranges from 60% to 65%. The best-performing front on S2 is mostly a one-hidden-layer dense network with an 8-element window and varying neuron size. However, the corresponding score on S1 is 95-98%. Similarly,

the second-best front on S2 corresponds to an FCNN with a 4-element window, with a different number of layers and neurons. Once again, the corresponding networks evaluated on S1 achieve scores between 92% and 98%. These F1-scores on S2 prove that overfitting on S1 has a significant impact and depends on the network architecture.

## C. Best Networks for each criterion

To have a better analysis of the capacities of the FCNNs, 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 IV and pinpointed in Figure 1. Selection is based on the F1-score and parameters, as the number of parameters are proportionate to the number of FLOPs for FCNN. Inference time mainly depends on the number of chosen layers.

<table><caption>TABLE IV: Selected best-performing FCNNs.</caption><thead><tr><th>Experiment Requirement on S1</th><th>E1 ≥ 95%</th><th>E2 ≥ 99%</th><th>E3 Maximum</th></tr></thead><tbody><tr><td colspan="4"><strong>POWDER (M = 4)</strong></td></tr><tr><td>F1-Score S1</td><td>96.05</td><td>99.15</td><td>99.96</td></tr><tr><td>F1-Score S2</td><td>75.98</td><td>82.04</td><td>85.65</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>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"><strong>WiSig (M = 6)</strong></td></tr><tr><td>F1-Score S1</td><td>95.80</td><td>99.05</td><td>99.68</td></tr><tr><td>F1-Score S2</td><td>61.54</td><td>61.95</td><td>61.05</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>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"><strong>SWRFF (M = 15)</strong></td></tr><tr><td>F1-Score S1</td><td>96.56</td><td>99.37</td><td>99.71</td></tr><tr><td>F1-Score S2</td><td>6.13</td><td>21.19</td><td>29.00</td></tr><tr><td>Parameters</td><td>2 095</td><td>12 687</td><td>77 103</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"><strong>ORACLE (M = 16)</strong></td></tr><tr><td>F1-Score S1</td><td>96.34</td><td>-</td><td>98.13</td></tr><tr><td>F1-Score S2</td><td>11.06</td><td>-</td><td>11.64</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>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 structure of the best networks per criterion is detailed with the length of the input signal, the number of neurons per layers as well as the corresponding activation functions. For example, the best-performing FCNN satisfying E3, on the SWRFF dataset, is a 3-layer network with an input size of $N = 256$, two hidden layers with 112 and 62 neurons, and an
output layer of $M = 15$. The activation functions are GELU after the first hidden layer and RReLU after the second. The latest layer is always followed by the Softmax function.

In our experiments, it is shown that the best FCNNs usually present only one layer beside the output layer and utilize the GELU function. In Experimentation E2, except for ORACLE, a small network with fewer than 10,000 parameters (therefore with less parameters than CNN WiSig, with 50× less FLOPs) can maintain a F1-score of at least 99% F1-score. In Experiment E3, the selected FCNNs had an F1-score reduction of 0.04, 0.19, and 1.79, respectively, compared to the best-performing network for the POWDER, WiSig, and ORACLE datasets, and an improvement of 0.03 for SWRFF (see Table I). This F1-score variation is traded for a network with fewer than 100 000 parameters, fewer than 100 000 FLOPs per inference, and an inference time of less than 0.5ms per batch.

It may also be observed that the signal size used is not the maximum possible length of $N = 256$, but more often $N = 64$ or $N = 128$. Indeed, reducing the input length helps decrease the number of parameters in the network, offering an interesting trade-off between F1-score and the number of parameters.

Similarly to FCNN V1, most FCNNs evaluated on the ORACLE dataset exhibit underfitting, which explains why their F1-scores do not reach 99%. However, the goal of this study is to demonstrate the potential of FCNNs as lightweight networks and emphasizes that convolutional layers may not be adapted to RFFI. Moreover, FCNNs can be improved with optimization techniques to reduce underfitting, that are out of the scope of the present paper (e.g., using a validation subset, weight regularization, dropout, early stopping).

# V. INDICATORS ON OUR SELECTED FCNN

## A. Selected FCNN

We designed a FCNN to work on all presented datasets, while remaining as lightweight as possible. FCNN (ours) has between 91 354 and 95 666 parameters depending on the number of transmitters to be identified. It comprises one hidden layer with 364 neurons, followed by LeakyReLU, and a FC layer with $M$ neurons and a Softmax function. The network takes signals of size $N = 128$. The selected FCNN is represented in Figure 2

<figure><img src="image_4.png" /><figcaption>Fig. 2: Selected FCNN.</figcaption></figure>

The network was evaluated following the same methodology outlined in Section IV-A, with the exception of the learning

rate reduction of 10% every 5 epochs (10 epochs for WiSig BDD), beginning at a learning rate of 0.01. The F1-scores for the selected FCNN, for all datasets, are presented in Table III.

<table><caption>TABLE III: F1-Scores of FCNN (ours) for all datasets.</caption><thead><tr><th></th><th>POWDER BDD</th><th>WiSig BDD</th><th>SWRFF BDD</th><th>ORACLE BDD</th></tr></thead><tbody><tr><td>S1</td><td>99.97±0.01</td><td>99.64±0.05</td><td>99.54±0.20</td><td>99.03±0.13</td></tr><tr><td>S2</td><td>86.29±0.75</td><td>54.20±0.57</td><td>21.77±0.61</td><td>9.45±0.42</td></tr></tbody></table>

A comparison with the networks in Table I if represented in Figure 3. If the value is positive, then our selected FCNN performed better than the compared network.

<figure><img src="image_5.png" /><figcaption>Fig. 3: F1-Score (S1) comparison with FCNN (ours).</figcaption></figure>

This figure shows that the F1-score of the selected FCNN is comparable to that of the best state-of-the-art CNNs. The observed F1-score losses are 0.03%, 0.23%, and 0.14% for the POWDER, WiSig, and SWRFF datasets, respectively. However, on the SWRFF dataset, the FCNN slightly compromises on F1-score in favor of reduced complexity, resulting in a loss of 0.89%.

While the F1-score degradation remains below 0.23% for POWDER, WiSig, and SWRFF, the ORACLE BDD model encounters some challenges. These are mainly due to an earlier design constraint limiting the number of parameters to fewer than 100,000. Nevertheless, the F1-score loss still remains below 0.9%.

Figure 4 compares the number of parameters, FLOPS, and inference time for each of the selected FCNN networks to the state-of-the-art network using the methodology presented in Section III-C. It is important to note that these results are independent of the datasets; they are dependent solely on the number of devices to be classified. Nevertheless, the complexity of the devices in question remains consistent within the 4-to-16 range.

WiSig CNN, the less complex CNN presented, is the closest network in terms of complexity. It has 43 times fewer parameters than our selected FCNN but has 5.6 times more FLOPs
<figure><img src="image_6.png" /><figcaption>Fig. 4: Parameter count, FLOPs, Inference time for each Neural Network presented, to classify 6 transmitters ($M = 6$).</figcaption></figure>

and four times more inference time. However, it achieves a lower F1-score.

The SWRFF CNN is the overall best performing network in terms of F1-score. It also has fewer parameters and FLOPs than its closest competitor, ORACLE CNN. Figure 3 shows that SWRFF CNN is better in F1-score compared to our presented FCNN with an improvement of between 0.03% and 0.73%. However, Figure 4 shows that SWRFF comes at a cost of 1.75 times more parameters, 33 times more FLOPs, and 6.6 times more inference time.

Convolutional layers in RFFI do not appear to be necessary, as there are alternative architectures, FCNNs, suited to real-world datasets that offer similar resilience and state-of-the-art performance, particularly in scenarios with a limited number of transmitters to identify. These alternatives achieve comparable classification capabilities while enabling a lighter implementation, resulting in reduced inference time and a lower number of FLOPs.

## VI. CONCLUSIONS AND PERSPECTIVES

In conclusion, this paper advocates for rethinking the design of neural networks for RFFI in light of the notable performance of Fully Connected Neural Networks (FCNNs). A comparison of several real-world RFFI datasets shows that state-of-the-art CNNs are significantly more computationally demanding than our proposed FCNN. Our analysis also reveals

that an input signal length of 128 samples is sufficient for effective RFFI in the real world. While the FCNN experiences a decrease in F1-score ranging from just 0.03% to 0.89% compared to the top-performing network, it achieves a 33-fold reduction in computational cost and a 6.6-fold increase in inference speed. Compared to a lightweight CNN, our FCNN delivers a higher F1-score, requires 5 times less computation, and offers at least 4 times faster inference.

## ACKNOWLEDGEMENT

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

## REFERENCES

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

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

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

[4] G. Qing, H. Wang, and T. Zhang, "Radio frequency fingerprinting identification for Zigbee via lightweight CNN," *Physical Communication*, vol. 44, p. 101250, 2021.

[5] L. Chen, C. Zhao, Y. Zheng, and Y. Wang, "Radio Frequency Fingerprint Identification Based on Transfer Learning," in *IEEE/CIC International Conference on Communications in China (ICCC)*, 2021, pp. 81-85.

[6] 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.

[7] E. Bothereau, A. Chillet, R. Gerzaguet, M. Gautier, and O. Berder, "Investigating Sparse Neural Networks for Radio Frequency Fingerprint Identification," in *2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)*, 2024, pp. 1-6.

[8] 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.

[9] 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 *IEEE Global Communications Conference (GLOBECOM)*, 2020, pp. 1-6.

[10] 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.

[11] 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.

[12] A. Elmaghbub and B. Hamdaoui, "EPS: Distinguishable IQ Data Representation for Domain-Adaptation Learning of Device Fingerprints," *ArXiv*, vol. abs/2308.04467, 2023.

[13] 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.