2019 IEEE Wireless Communications and Networking Conference (WCNC)

Detection of Rogue RF Transmitters using
Generative Adversarial Nets
Debashri Roy∗ , Tathagata Mukherjee† , Mainak Chatterjee∗ , Eduardo Pasiliao‡∗ Computer Science† Computer Science‡ Munitions Directorate

University of Central Florida
Orlando, FL 32826{debashri, mainak}@cs.ucf.edu

University of Alabama
Huntsville, AL 35899{tm0130}@uah.edu

Air Force Research Laboratory
Eglin AFB, FL, 32542{eduardo.pasiliao}@us.af.mil

Abstract—Understanding and analyzing the radio frequency(RF) environment have become indispensable for various autonomous wireless deployments. To this end, machine learningtechniques have become popular as they can learn, analyze andeven predict the RF signals and associated parameters that characterize a RF environment. However, classical machine learning
methods have their limitations and there are situations wheresuch methods become ineffective. One such setting is where active
adversaries are present and try to disrupt the RF environment
through malicious activities like jamming or spoofing. In thispaper we propose an adversarial learning technique for identifying
rogue RF transmitters and classifying trusted ones by designingand implementing generative adversarial nets (GAN). The GAN
exploits the in-phase (I) and quadrature imbalance (i.e., theIQ imbalance) present in all transmitters to learn the uniquehigh dimensional features that can be used as “fingerprints”
for identifying and classifying the transmitters. We implementa generative model that learns the sample space of the IQ valuesof the known transmitters and use the learned representation to
generate fake signals that imitate the transmissions of the known
transmitters. We program 8 universal software radio peripheral(USRP) software defined radios as trusted transmitters andcollect over-the-air raw IQ data from them using a RTL-SDR in alaboratory setting. We also implement a discriminator model andshow that the discriminator is able to discriminate between thetrusted transmitters from fake ones with 99.9% accuracy. Finally,
the trusted transmitters are classified using convolutional neuralnetwork (CNN) and fully connected deep neural networks (DNN).
Results reveal that the CNN and DNN are able to correctly
discriminate between the 8 trusted transmitters with 81.6% and96.6% accuracies respectively.

Keywords: RF fingerprinting, GAN, machine learning,
deep neural network, IQ imbalance, USRP, confusion matrix.
I. I NTRODUCTION
Localization, identification, and characterization of radio
frequency (RF) signal sources (aka RF transmitters) are indispensable for applications such as locating a cell phone,identifying a jammer, detecting the presence or absence of asignal, tracking objects, etc. Localization of a RF transmitteris a well studied problem where the received power at areceiver is utilized to estimate the distance to the transmitter, given some known transmitters and path-loss models(see [1] and the references therein). With more and moreautonomous deployments of wireless networks, identificationof transmitters has also become important. For example, awireless sensor network relies on trustworthy signals; however, malicious transmitters can contaminate the signals and978-1-5386-7646-2/19/$31.00 ©2019 IEEEjeopardize the utility of the sensor network. Existence of suchthreats underscore the need for techniques that recognize andauthenticate transmitter identity irrespective of the network
protocols and the communication technologies being used.
However, correctly identifying a transmitter and being able
to characterize it in real time remains a challenging problem.
Use of secure mechanisms to authenticate RF transmittershas been a common way to identify malicious transmitters.
However, implementation of such secure mechanisms adds tothe computation and communication overhead for real timesystems such as connected autonomous vehicles. Recent developments in radio frequency machine learning (RFML) systems
have given rise to the possibility of using these methods forautomated real time authentication of RF transmitters. Thesemethods can also be used in adversarial settings for tasks suchas the identification of malicious transmitters by through theuse of learning powered transmitter forensics [2].
Unlike the image or speech processing domain, wheremachine learning techniques have been widely successful,learning in the RF domain is just beginning to see somebreakthroughs. For processing images, spatial correlations andknowledge of previously observed objects make future predictions possible. Similarly, in speech recognition recognizable
patterns emerge from known sequences that can be used tosynthesize phrases and words. In essence such techniques arebuilt on prior distributions and patterns that make generalizations possible. However, such techniques cannot be easily
extended to the RF domain because of the unpredictable andvaried nature of the RF signals. To make matters worse, thepresence of adversaries make it even more difficult to learn
and characterize RF signals. For one smart adversaries canspoof transmitters and introduce noise in the transmission
channels making it harder to learn unique characteristics ofthe transmitters, in essence manipulating the learning phaseto render the detection model ineffective. Thus, application of
RFML systems for RF synthesis and recognition has become
an interesting and open research area.
For machine learning techniques to be effective, one mustchoose an attribute or feature that is unique to a transmitter,irrespective of the signals it transmits. One such commonlyused feature is the rise time signature that is generated by slightvariations of the component values during the manufacturing
process. Though the rise time signature has mostly been used

2019 IEEE Wireless Communications and Networking Conference (WCNC)

for signal intelligence and by regulatory agencies, it performs
poorly in the presence of malicious entities. Moreover, the risetime signature is commercially unavailable , hence cannot be
used as a standard signature. ‘IQ imbalance’ is another featurethat is affected at the time of manufacturing due to the use of
noisy mixers, oscillators and unbalanced low pass filters [3].
This is the imbalance between the in-phase (I) and quadrature(Q) components of a signal which is the result of interactionof the radio frequency with the local oscillator frequency
which is required to get the intermediate frequency. Though
there are techniques to compensate for this imbalance [4],the fact remains that all transceivers exhibit this unique IQ
imbalance. The IQ imbalance depends on the choice of the
hardware components used, and is an unwanted byproduct ofthe manufacturing process that is hard to imitate. This imbalance can be used as a basis for feature engineering (automatedor otherwise) for transmitter identification and recognition.
However, this fact is also known by the adversaries. Thus,
their target would be to learn and estimate the probability
distribution of the training data used for model creation, given
a particular sample space. The adversaries can use a generativemodel to generate fake signals so as to spoof the transmissionof known transmitters. Generative Adversarial Nets (GAN) [5]
uses a generative model which enables the realistic generationof samples from a given distribution which can then be used totrain a discriminator for identifying real samples drawn fromfake ones obtained from the generator.
In this paper, we use generative adversarial nets (GAN)
to detect rogue transmitters. Unlike most machine learningtechniques, GAN has been designed to learn in adversarial
situations. We propose and implement a generative model anda discriminative model to (i) detect unknown and/or roguetransmitters and (ii) classify the known transmitters. We leverage the fact that the IQ imbalance for every radio transmitter is
unique and could be exploited by the GAN to generate uniquefeatures or fingerprints. The main contributions of this paperare:
We propose a generative model that uses a deep neuralnetwork (DNN) for generating (fake) signals that very
closely resemble real signals. The generator proceeds
by reducing the parameter space, replicates the timeinvariant features and serves as a compact front-end forfake radio signal generation.
• We propose a discriminative model using a deep neuralnetwork that takes input from the known signal transmitters as well as the fake signals from the generative model.
The purpose of this discriminative model is to distinguishthe real signals from the fake ones. The outcome of thedecision process is fed to the generative model, allowing
the adversary (generator) to update its model so as tobetter generate fake signals.
• Once we detect the trusted transmitters from the rogueones, we use supervised training models to classify thetrusted transmitters. We design a convolution neural network (CNN) for that purpose, which leverages the corre-

lation between the complex-valued IQ data constellations.
We design another deep neural network to improve theaccuracy of the CNN.
• Our models have been validated on a laboratory test
bed consisting of several universal software radio peripheral (USRP) B210s [6] and one RTL-SDR receiver [7].
The USRPs transmitted signals on a particular frequency
which were received by the RTL-SDR. The collected
dataset had 1024 complex IQ samples per timestamp,
generating 2048 features. The generative and discriminative models were trained and tested on the collected
dataset. The unique pattern of variation of the IQ imbalances for each radio is captured as features by themultiple layers of the neural network.
• The novelty of the proposed work lies in accurately
modeling and implementing the proposed generative anddiscriminative models on real hardware using raw IQ
data. To the best of our knowledge, this is the first paperthat uses GANs to identify adversarial RF signals and forfingerprinting radio transmitters.
Next we describe the current and previous work in machinelearning based transmitter identification.
II. BACKGROUND AND R ELATED W ORK
During the process of designing and manufacturing cheap
radio hardware certain imperfections have become the norm.
The IQ imbalance is one such imperfection that is unique todifferent radio hardware and are caused by imperfections inlocal oscillators and mixers. As a result of this, the in-phase (I)and quadrature (Q) components of the modulator are not orthogonal. When a signal is transmitted using a particular radiotransmitter, some IQ imbalance is imposed over the complexvalued IQ data during transmission [8] as shown in Fig. 1.
IQ imbalance leads to performance degradation for higher
order modulations because the symbol rotation becomes moresensitive with increasing number of constellations towards I
and Q branches [9].

Quadrature

Quadrature•

Inphase(a)Inphase(b)

Fig. 1. IQ Imbalance for QPSK: (a) Before (b) After 45° Phase Imbalance

In recent years, there have been some efforts at using
machine learning techniques for fingerprinting RF transmitters. In [10], O’Shea et al presented a radio modulation
classification method using naively learned features. They
have shown that blind temporal learning on densely encoded
time series using convolutional neural networks is a viableapproach. However, this method did not perform well in
the low signal to noise ratio (SNR) regime. In [11], theauthors have presented an unsupervised learning technique

2019 IEEE Wireless Communications and Networking Conference (WCNC)

III. P ROPOSED GAN FOR RF F INGERPRINTING
Recent advances in neural networks have made it possible toobtain robust models with low generalization errors by training“deep” neural architectures efficiently. The “depth” signifies
the number of iterative operations performed on the input datausing each layer’s transfer function and deeper architectures
allow the network to learn robust feature representations
from the input data. Though such techniques demand higher
computation and involve complicated layer-by-layer backpropagation, nevertheless, most deep learning systems are able toperform training on deep networks using some variation ofgradient decent with adaptive learning rates (e.g., Adam [19]),
regularization to avoid overfitting (e.g., Dropout [20]) andthe use of backpropagation. Our intention is to design neuralnetwork models that can train in the presence of adversaries
and discriminate between fake and known transmitters throughautomatic fingerprinting.
A. Proposed GAN Architecture
The GAN framework has two primary models, a generativemodel (G) that generates fake data from a given data distribution, and a discriminative model (D) that estimates the probability that a sample came from the training data rather thanModulation Schemesm(t)Generators(t)Signal Amplituder(t)Signal Phase

l(t)

g(t)

Feedback

Noise(I/Q Generator)

Adversarialn(t)using convolutional autoencoders which can eventually learn
the basics of modulation functions and then leverage thatto recognize different digital modulation schemes. They also
proposed a method to evaluate the quantitative metrics onreconstructed data to recognize the schemes. Another study formodulation classification using raw IQ samples was presented
in [12]. A method for modulation classification was proposedin [13] for a distributed wireless spectrum sensing network.
The authors proposed a recurrent neural network model using
long short term memory (LSTM) cell, yielding 90% accuracy
for synthetic dataset [14].
An in-depth study on the performance of deep learning
based radio signal classification was proposed in [15]. The
authors considered 24 modulation schemes with a rigorousbaseline method that uses higher order moments and strong
boosted gradient tree classification for detection. The authors
also applied their method on real over-the-air data collectedby Software Defined Radios (SDRs). An approach based on
the concept of adversary was proposed in [2] for synthesizing
new physical layer modulation and coding schemes. The
adversarial approach is targeted to learn the channel response
approximations in any arbitrary communication system, enabling the design of a smarter channel autoencoder. All these
proposed approaches demonstrate how difficult it is to makemachine learning techniques effective in the RF domain.
Motivated by the above mentioned works, we focus ontransmitter identification in the presence of adversaries. The
idea of training discriminative models via an adversarial
process was first proposed by Goodfellow [5]. Since then,generative adversarial nets (GAN) have been adopted for various fields and applications, particularly for image processing
where GANs have proved their efficacy [16]–[18].

G

Trusted?

D
Fake?

Trusted Transmitter
I/Q Data(Through SDR)

FeatureExtractor

GAN
Implementation

Fig. 2. Proposed GAN architecture

G. The proposed GAN architecture is shown in Fig. 2. G and
D are the generative and discriminative models respectively.
The adversary generates random modulation scheme (m(t)),
signal amplitude (r(t)), and phase (l(t)) and mixes additive
white Gaussian noise (n(t)) with that. The generated signal(g(t)) which is initially random in nature improves over timeas the generator learns from the discriminator and improveson its accuracy to imitate real data. On the other hand, thediscriminator (D) gets input from both the generator (G) and
Trusted transmitters. This helps it to learn to differentiate
between real and fake inputs. The known transmitter data iscollected and fed to the discriminator (D) as raw IQ values.
Overall, the target is to train G in such a way that will
maximize the probability of D making a mistake. G tunes its
hyper parameters with the feedback from D. We argue, GAN
is an efficient way to generate correlated data samples andthereby approach an accurate generative model– something
the rogue transmitters aim to achieve. Once the model istrained, the generated signals are synthesized to mimic roguetransmitters based on the sample space of IQ signal data fromthe known transmitters.
B. The Generative Model
As far as the generator is concerned, the overall problem canbe treated as an N -class decision problem where the input is acomplex base-band time series representation of the received
signal. That is, the dataset is the in-phase and quadraturecomponents of a radio signal obtained at discrete time periods
through analog to digital conversion with a carrier frequency
to obtain a 1 × N complex valued vector. Classically, this is
written as:s(t) = c1 m(t) + c2 r(t) + c3 l(t)(1)

where s(t) is a continuous time series signal modulated onto
a sinusoid with either varying frequency, phase, amplitude,
trajectory, or some permutation of multiple parameters. Here,m(t), r(t), and l(t) are the time series continuous signals
for modulation, amplitude, and phase respectively, selected
randomly by the generator. The coefficients c1 , c2 , and c3 aresome path loss or constant gain terms associated with m(t),r(t), and l(t) respectively. The output g(t) is obtained as:g(t) = s(t) + n(t)(2)

2019 IEEE Wireless Communications and Networking Conference (WCNC)

where n(t) is the additive Gaussian white noise. The output
g(t) is then fed to a generator which is used as an unsupervised
learning tool as a part of the generative network. The generator
learns the probability distribution pg (x) over sample space (x)of the input. In this case, x is the sample space of IQ values.
C. The Discriminative Model
The discriminative model learns by minimizing a cost function during training. The cost function, C(G; D), depends on
both the generator (G) and the discriminator (D). It is formulated as C(G; D) = Epdata (x) log D(x) + Epg (x) log(1−D(x)),
where pg (x) is the generator’s distribution over x, pdata (x)is the data distribution over x, D(x) is the probability that x
came from pdata (x) than pg (x). The training is formulated as:
max min C(G; D)
D

(3)

G

For the GAN framework there is an unique optimal discrimpdata (x). It isinator for a fixed generator, D∗ (x) =pdata (x) + pg (x)also inferred that G is optimal when pg (x) = pdata (x), i.e., thegenerator is optimal when the discriminator cannot distinguishreal samples from fake ones. Similarly, the D is optimal when
the discriminator can recognize each real sample from fakes.

Fig. 4. GNU Radio Flow Graph for Data Collection

Parameters
ValuesTransmitter Gain
45 dB
Transmitter Frequency
904 MHz (ISM)
Bandwidth
200 KHz
Sample Size
1024
Samples/Transmitter
40,000# Transmitters
4 and 8TABLE IT RANSMISSION C ONFIGURATION PARAMETERS

IV. T ESTBED E VALUATION
In order to validate the proposed GAN, we implemented
the generator and discriminator models using data from universal software radio peripheral (USRP) and conducted indoor
experiments to distinguish between 8 similar transmitters.
A. Signal Generation and Data Collection
In order to learn the features of similar transmitters, weused eight USRPs of the same kind, namely B210 from
Ettus Research [6]. The signal generation and reception are
shown in Fig. 3. The B210s were programmed to transmit
random data on 904 MHz using Quadrature Phase Shift Keying(QPSK) modulation. Then the modulated signal was transmitted through the USRP sink block. We used GNURadio [21]for signal processing and data transmission. The flow graph ispresented in Fig. 4. For the receiver, we used a RTL-SDR [7]which captured over-the-air raw IQ data and stored them on
file.
Signal Processing and Data Collection
RandomSignalQPSKModTransmitters

Receiver

GNURadio

USRP
B210

RTL-SDR

DatasetGeneration
rtlsdrPythonLibraryData(#samples,
2*sample size)

Fig. 3. Signal Generation and Data Collection Technique

We collected the IQ signal data with a sample size of 1024.
Each data sample had 2048 entities consisting of the I and Q
values for the 1024 samples. A larger sample size would mean
more training examples for the neural network. The choice of1024 samples was sufficient to capture the unique pattern of
IQ imbalances and at the same time it was not computationallyexpensive. We collected 40,000 training examples from eachtransmitter to avoid the data skewness problem observed inmachine learning. The configuration parameters are given in
Table I. We had two sets of data: (i) using 4 transmitters:
6.8 GB size, 160K rows and 2048 columns and (ii) using 8transmitters: 13.45 GB size, 320K rows and 2048 columns.
Real
Data

D

G

SampleSpace

Discriminator

Is
Real?

Generator

Noise

TuningParameters

Fig. 5. A Simplified View of GAN Implementation

B. Machine Learning Libraries
There are several libraries and tools that implement deeplearning frameworks with support of immensely concurrent
GPU architecture that reduce the burden of programming the
traditional routines for training of larger neural networks. We
use Keras [22] as the frontend and Tensorflow [23] as thebackend. Keras is an overlay on neural network primitives with
Tensorflow [23] or Theano [24] that provides a customizableinterface for quick deployment of complex neural networks.
We also use Numpy, Scipy, and Matplotlib Python libraries.
C. Experimental Setup and Performance Metrics
We conducted the experiments on a Ryzen 8 Core systemhaving 64 GB RAM and a GTX 1080 Ti GPU unit having 11
GB memory. We focused on three main aspects:

(None, 512) (None, 1024)(#samples,
2*sample size)(None, 1024)(None, 2048)

Dropout= 0.5

Softmax(None, 2)

OutputDropout= 0.5

Dense

Discriminative Net[0,1] OR [1,0]Dense tanhOR

Dense tanh

Fake
Data

Trusted
Data

Generated Data

Dense tanh(None, 100)

Dense tanhNoise

RandomGenerative Net

Input

2019 IEEE Wireless Communications and Networking Conference (WCNC)

2(None, 512)

Signal Processing andData Collection

Fig. 6. GAN Implementation for rogue Transmitter Detection

Designing a generative adversarial net (GAN) to distinguish rogue transmitters from trusted ones.
• Designing a convolutional neural network (CNN) to exploit the correlation in collected signal data of the trusted
transmitters.
• Designing a deep neural network (DNN) to classify thetrusted transmitters for fingerprinting.
To measure the effectiveness of the proposed neural networks, “accuracy” is used as the typical performance metric.
However, accuracy can sometimes be misleading and incomplete when data is skewed. A confusion matrix overcomes
this problem by showing how confused the classification
model is on its predictions. It provides more insights on theperformance by identifying not only the number of errors, butmore importantly the types of errors.
•

V. GAN I MPLEMENTATION
For implementing the GAN, we use the over-the-air datacollected from the trusted transmitters. The generator (G) generates fake data from the same sample space to impersonate asa transmitter. Trusted and fake data are fed to the discriminator(D) with an equal and unbiased probability. We design thediscriminator and generator separately, as shown in Fig. 5.
The generator starts with randomly generating data within
the sample space [-1,1]. Two dense layers of size 512 and1024 are applied with tanh activation function. Then one dense
layer of 2× sample size (2048 in this case) is invoked withthe sigmoid activation function. G continues to learn the data
distribution (pg ) and generating fake samples of size 2048
within the signal IQ values sample space. D consists of one
input layer of 2048 nodes, two hidden layers of 1024 and512 nodes respectively, and finally a softmax output layer of
2 nodes to classify an input as either Fake or Trusted. We
use tanh as activation function at the hidden layer and added
Dropout [20] of 0.5 in between those layers for regularization.
The overall GAN implementation is shown in Fig. 6.
We train both the generator and discriminator through iterative sequential learning to strengthen the generative model overtime. We use categorical cross-entropy training on Adam [19]optimizer for gradient based optimization. We notice that thediscriminator was able to detect the fake transmitters with 50%
accuracy before the adversarial training. After several epochs(< 50) of adversarial training, the optimal discriminator (D∗ )Fig. 7. Confusion Matrix for Determining Trusted and Fake Transmitters

is able to detect the Fake transmitters with about 99.9%
accuracy, as shown in the confusion matrix in Fig. 7. Notethat one epoch consists of a forward and a backward passthrough the designed model over the entire dataset. It is clear
from the confusion matrix that the number of false negatives
and false positives are very low and well within acceptable
range [25]. Once the GAN is trained, it will be able to detectrogue transmitters from over-the-air reception of raw IQ data.
A. CNN Implementation
The main motivation for implementing a convolution neuralnetwork was to capture the correlation between IQ values ofsamples. The CNN has three Conv2D layers of 1024, 512 and
256 filters, a Flatten operation, and three fully connected (FC)layers of 512, 256 and 8 nodes as shwon in Fig. 8. We use
Dropout [20] of 0.25 and 0.5 after each conv2D and dense
layer respectively. We use kernel size of (2,3) and stride of(2,2) at each Conv2D layer. We also apply a pooling layer
MaxPooling2D after each Conv2D layer with pool size of(2,2) and stride of (2,2). We use ReLU [26] activation for all
convolution and fully connected layers, other than the softmax
layer of the output nodes.
We obtain 89.07% and 81.6% accuracy for 4 and 8 transmitter classification respectively using the aforementioned CNN.
The low accuracies are due to the poor correlation among samples from the same radio. The accuracy plots and confusion
matrices for both the cases are shown in Figs. 9 and 10. Bothtraining and validation accuracy increase with the number of2019 IEEE Wireless Communications and Networking Conference (WCNC)

1024 512 256

Dropout= 0.5(None, 1024)

Dropout= 0.5(None, 512)

Softmax(None, 8)

Output

Data

Dense

Signal Processing
andData CollectionDense tanh

8

8

Input

Output

256

FC

512

FC

FlattenFC

Conv Layer 2

Conv Layer 3(None, 1024, 2)

Conv Layer 1

Data

Input

Signal Processing
andData CollectionDense tanh

Deep Neural Net

Convolution Neural Net

8(None, 2048)

Fig. 8. CNN Implementation for Transmitter Classification
Fig. 11. DNN Implementation for Transmitter Classification

epochs. With the CNN, the number of false positives and false
negatives are somewhat high for the predicted versus the truelabels. So, we proceed to build a deep network to achieve
better accuracy for a trusted transmitter classifier.

(a) 4 Transmitters(b) 8 TransmittersFig. 12. Accuracy for Transmitter Classification using DNN(a) 4 Transmitters(b) 8 TransmittersFig. 9. Accuracy Plot for Transmitter Classification using CNN(a) 4 Transmitters(b) 8 TransmittersFig. 10. Confusion Matrix for Transmitter Classification using CNN

B. DNN Implementation
Once the discriminator recognizes the trusted transmittersfrom the fake ones, we feed the trusted transmitter data to a
deep neural network for its classification. The implementationof the DNN is similar to the discriminator model of GAN
and is shown in Fig. 11. The only difference is that thesoftmax output layer has 8 nodes to recognize the 8 classes. We
use biases and regularization to avoid under- and over-fitting.
We use Adam [19] based optimization with categorical crossentropy training. The DNN yields an accuracy of 97.21% for
4 transmitters, and 96.6% for 8 transmitters. The accuracy andconfusion matrices are shown in Figs. 12 and 13 respectively.
It is evident that the number of false positives and false
negatives in the confusion matrices are significantly low for
the DNN.

C. Comparisons of GAN/CNN/DNN Implementations
Once a transmitter is found to be Trusted via the proposed GAN, we use the DNN or the CNN to identify it. We
used 90%, 5%, and 5% to train, validate, and test respectively.
The overall accuracy of different implementations is shownin Table II. We find that the CNN does not exhibit the best
accuracy for transmitter classification, which clearly depicts
the lack of correlation between the data. We also conducted
experiments by varying the number of transmitters from 2 to
8. In Fig. 14, we present how training and testing accuracychanges with increasing number of transmitters. As expected,
the accuracy decreases when there are more classes that atransmitter needs to be mapped to.
The GAN based deep neural network achieves an acceptableaccuracy for transmitter identification proving the feasibility ofthe proposed idea. In summary,1) GAN network is able to distinguish between Trusted
and Fake RF signals.
2) Convolution neural network yields 81%-86% accuracy(a) 4 Transmitters(b) 8 TransmittersFig. 13. Confusion Matrix for Transmitter Classification using DNN

2019 IEEE Wireless Communications and Networking Conference (WCNC)
Dataset (GB)
6.8
13.45
6.8
13.45

#Trans48
4
8MethodDNN (5 layers)
DNN (5 layers)
CNN (6 layers)
CNN (6 layers)

6.8

4

GAN (DNN)

13.45

8

GAN (DNN)#Parameters
6.8 M
6.8 M
38 M
38 M
3.6 M (G)
6.8 M (D)10.4 M (GAN)
3.6 M (G)
6.8 M (D)10.4 M (GAN)

Acc (%)
97.21
96.6
89.07
81.59
99.9

99.9

TABLE II
ACCURACY FOR D IFFERENT I MPLEMENTATIONS

100

Accuracy (%)

Train
Test

98

96

94

2

4
6
Number of Transmitters8Fig. 14. Training and Accuracy with Increasing numbers of Transmittersfor discriminating between the known transmitters.
3) Deep neural network yields excellent accuracy for
known transmitter classification.
4) DNN or CNN can be used for transmitter fingerprinting
after the discriminator is able to distinguish Trusted
and Fake signals.
It is to be noted that traditional machine learning techniquessuggest different neural networks for different kinds of classification problems. However, we have used a single neuralnetwork architecture for both 4 and 8 class classifications, forproviding an end-to-end solution. One can use the same neuralnetworks for classifying up to 8 transmitters without changing
any parameter or hyper-parameter of the proposed models.
VI. C ONCLUSIONSIn this paper, we address the problem of identifying RFtransmitters of similar types in the presence of adversarial signals. We argue that most machine learning techniques would
not be effective in adversarial situations and that breakthroughs
in generative adversarial nets (GAN) can be instrumental in
detection of rogue transmitters and subsequently the accurate
identification of known ones. We propose and implement a
generative model and a discriminative model for the GAN.
We collected over-the-air raw IQ data using USRP B210 andused that to train the GAN. The discriminator was able todetect rogue transmitters with an accuracy of ∼99.9%. As fortransmitter classification, we first implemented a convolution
neural network (accuracy ∼89%) for exploiting the correlation
between IQ data. Then we designed and implemented a deepneural network that showed an accuracy of ∼97% for transmitter identification. Our overall implementation frameworkprovides an end-to-end solution for transmitter fingerprinting
and identification using raw IQ data.
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