2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)RF Transmitter Fingerprinting Exploiting
Spatio-temporal Properties in Raw Signal Data
Debashri Roy∗ , Tathagata Mukherjee† , Mainak Chatterjee∗ , Eduardo Pasiliao3∗ 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—The recent advances of wireless technologies in RF
environments coupled with large scale usage of such technologies has warranted more autonomous deployments of wirelesssystems. Machine learning techniques, that include recurrent
structures, have shown promise in creating such autonomous
deployments using the idea of Radio Frequency Machine Learning (RFML). In large scale autonomous deployments of wirelesscommunication networks, the signals received from one component play a crucial role in the decision making process of othercomponents. In order to efﬁciently implement such systems eachcomponent of the network should be uniquely identiﬁable. In
this paper we propose a transmitter ﬁngerprinting techniquefor radio device identiﬁcation using recurrent structures, by
exploiting the temporal property of the received radio signal. We
design and implement three recurrent neural networks (RNNs)
using different types of cell models: (i) long short term memory(LSTM); (ii) gated recurrent unit (GRU) and (iii) convolutional
long short term memory (ConvLSTM), for this task. We program
8 universal software radio peripheral (USRP) software deﬁned
radios (SDRs) as transmitters and collect over-the-air raw inphase (I) and quadrature (Q) (I/Q) time series data from themusing a DVB-T RTL-SDR receiver, in a laboratory setting. We
exploit both the temporal variations as well as the inherent
spatial dependencies in the collected I/Q time series data, to learn
unique feature representations and use these as “ﬁngerprints”
for identifying the transmitters. Experimental results reveal that
the RNNs with LSTM, GRU, and ConvLSTM cells are able
to correctly distinguish between the 8 transmitters with 92%,
95.3%, 97.2% accuracy respectively.

of such systems are not cryptographically sound. In orderto get around the problem of faulty implementations, one
can use the transmitter’s intrinsic characteristics to create a“ﬁngerprint” that can be used by a transmitter identiﬁcation
system. Every transmitter, no matter how similar, has intrinsic
characteristics because of the imperfections in its underlying
components such as ampliﬁers, ﬁlters, frequency mixers aswell as the physical properties of the transmitting antenna;
these characteristics are unique to a speciﬁc transmitter. The
inaccuracies present in the manufacturing process and theidiosyncrasies of the hardware circuitry also contribute to thespatial and temporal characteristics of the signal transmitted
through a particular device.
This inherent heterogeneity can be exploited to create
unique identiﬁers for the transmitters. One such property is theimbalance in the Inphase (I) and Quadrature (Q) phase components of the signal (I/Q data). However, because of the sheer
number of the transmitters involved, manually “ﬁngerprinting”
each and every transmitter is not a feasible task [4]. Thus, inorder to build such a system, there needs to be an “automatic”
method of extracting the transmitter characteristics and using
the resulting “ﬁngerprint” for the differentiation process. One
way of achieving this is by learning the representation of thetransmitter in an appropriate “feature space” that has enoughdiscriminating capability so as to be able to differentiate
between “apparently identical” transmitters.
Among the various approaches that can be used to discern
this feature space, deep learning (DL [5]) based methods
provide an efﬁcient and automatic way of learning andcharacterizing the feature space. They are able to learn andanalyze the inherent properties of large deployments and use
it to predict and characterize the associated parameters forthe task of automatic feature learning for classiﬁcation (orregression). Deep neural networks have been shown to beeffective for automatically learning discriminating featuresfrom data for various tasks [6]. With proper choice of theneural network architecture and associated parameters, they
can compute arbitrarily good function approximations [7].
Since the task of classiﬁcation is equivalent to learning thedecision boundary, neural networks were a natural candidate
for a learning machine. Coupled with their prior success inlearning from signal data, they were the natural choice for
implementing our system.

Keywords: RF ﬁngerprinting, recurrent neural network,
supervised learning, software deﬁned radios.
I. I NTRODUCTION
We are living in a world where the distances are shrinking every day, thanks to an explosion in the use of connected devices. The ubiquitous usage of wirelessly connectedInternet-of-Things (IoT) [1] along with the deployment of
wireless autonomous systems has ushered in a new era ofindustrial scale deployment of radio frequency (RF) devices.
This prevalence of large scale peer-to-peer communicationand the nature of the underlying ubiquitous network brings
forth the challenge of accurately identifying a RF transmitter.
Every device that is part of a large network needs to be able
to identify its peers with high conﬁdence in order to set up
secure communication channels. One of the ways in which
this is done is through the interchange of “keys” [2] for hostidentiﬁcation. However, such schemes are prone to breaches bymalicious agents [3] because often the actual implementations

978-1-7281-4550-1/19/$31.00 ©2019 IEEE
DOI 10.1109/ICMLA.2019.00023

89section V. Conclusions are drawn in the last section.

Neural networks have previously been used for transmitter
identiﬁcation [8]–[10] and are particularly attractive since they
can generate accurate models without knowledge of the apriori
data distribution. Neural networks have been shown to be able
to predict modulation techniques [8] and identify transmitters[10] by only considering the spatial correlations within theactual [9] or synthetic RF data [11]. It is to be noted that allprior works have only exploited the spatial correlation of thesignal data, though a continuous signal can be represented asa time series, having both temporal and spatial properties [12].
Inspired by the success of deep learning systems for thetask of characterizing RF environments [13] and the successful
use of recurrent neural networks (RNN) for the task ofanalyzing time series data [14], we propose to use deep
recurrent structures for learning transmitter “ﬁngerprints” forthe task of transmitter identiﬁcation. Recurrent Neural Networks (RNNs) [6] have been shown to be useful for capturing
and exploiting the temporal correlations of time series data.
There are a few variants of recurrent neural networks: (i)Long Short-Term Memory (LSTM) [15], (ii) Gated Recurrent
Unit (GRU) [16], and (iii) Convolutional Long Short-Term
Memory (ConvLSTM) [17]. All these variants are designed tolearn the long term temporal dependencies and are capableof avoiding the “vanishing” or “exploding” gradient problems [18].
In this paper, we use these variants of recurrent neuralnetworks with time series of I/Q data to identify different
transmitters. The main contributions of this paper are:

II. R ELATED W ORKS
Recurrent neural networks [6] have been used extensively
for modeling temporal data such as speech [14]. There islimited amount of work that recognizes the potential of using
recurrent structures in the RF domain and in general the useof deep learning in the RF domain has been limited in the pastwith only a few applications in recent times [8], [13].
In [8], the authors have demonstrated the use of neuralnetworks for modulation detection. Apart from the results, aninteresting aspect of the work is the way I/Q values were used
as input to the neural network. More precisely, given N I/Q
values, the authors used a vector of size 2N as an input tothe neural network, effectively using the I and Q components
as a tuple representing a point in the complex plane. This
representation proves to be useful for using the I/Q data in
different learning models.
In [21], O’Shea et. al. presented a recurrent neural networkthat extracted high level protocol information from the lowlevel physical layer representation for the task of classiﬁcation.
A radio anomaly detection technique was presented in [22],where the authors used a LSTM based RNN as a time series
predictor using the error component to detect anomaly fromreal signals. Another application of RNN was proposed in [23],where the authors used a deep recurrent neural network tolearn the time-varying probability distribution of received
powers on a channel and used the same to predict the suitability of sharing that channel with other users. A method formodulation classiﬁcation was proposed in [24] for a distributed
wireless spectrum sensing network. The authors proposeda recurrent neural network using long short term memory(LSTM) cell, yielding 90% accuracy on a synthetic dataset[25]. Bai et. al. proposed an end-to-end RF ﬁngerprinting[26] method using two RNNs in order to learn the spatialor temporal pattern. Both simulated and real-world data wasused to improve the positioning accuracy and robustness ofmoving RF devices.
For the task of transmitter classiﬁcation, there are a few
traditional methods that use manual feature engineering andleverage different radio attributes like transients, or spurious
modulations to create discriminating feature sets. A transient
signal is transmitted when a transmitter is powered up or
powered down. During this short period (typically a few
micro seconds) capacitive loads charge or discharge. Different
classiﬁcation approaches using transient based recognition was
proposed in [27]–[29]. A different approach for transmitter
ﬁngerprinting and classiﬁcation was proposed in [30], where
the authors classiﬁed FM radio transmitters based on uniquestray features extracted from spurious modulation characteristics. However, none of these methods address the problem
of providing an end-to-end solution using raw signal data
for transmitter identiﬁcation using automatically extracted“ﬁngerprints”.

1) We exploit the temporal properties of I/Q data by using
a supervised learning approach for transmitter identiﬁcation using recurrent neural networks. We use two
approaches: ﬁrst, we exploit only the temporal property
and then we exploit the spatio-temporal property. We use
RNNs with LSTM and GRU cells for the ﬁrst approach
while we use a convLSTM model for the latter. Althoughtransmitter ﬁngerprinting has been studied before, tothe best of our knowledge this is the ﬁrst work whichleverages the spatio-temporal property of the over-theair signal data for this task.
2) To examine the performance of the proposed networks,
we test them on an indoor testbed. We transmit raw
signal data from 8 universal software radio peripheral (USRP) B210s [19] and collect over-the-air signals
using a RTL-SDR [20]. We use the I/Q values from each
USRP for ﬁngerprinting the corresponding transmitter.
3) The novelty of this work lies in accurately modeling andimplementing different types of RNNs to build a robusttransmitter ﬁngerprinting system using over-the-air signal data, by exploiting spatio-temporal correlations.
The rest of the paper is organized as follows: in the next
section, we present a survey of existing machine learning
based transmitter identiﬁcation techniques. In section III, we
propose different RNN models. In section IV, we present the
testbed setup and experiments that we conduct to evaluate
the proposed models. We present the experimental results in90

III. P ROPOSED RNN M ODELS FOR C LASSIFICATION
In order to estimate the noise in a RF channel, the systemneeds to “listen” to the underlying signal for sometime and“remember” the same. Previously, neural networks lacked
this capability when used in the context of temporal data.
Another issue with using neural networks with temporal data
was the problem of vanishing gradients, when trying to useback propagation. Both these problems were solved by the
introduction of Recurrent Neural Networks (RNN) [5].
a) Formulation of temporal property of RF data: Given
T training samples (for T timestamps) where each training
sample is of size of M and consists of a vector of tuples ofthe form (I, Q) ∈ C representing a number in the complex
plane, we represent a single sample as xt = [[(I, Q)i ]t ; i =1, 2, · · · , M ] ∈ C M for each timestamp t = 1, 2, · · · , T , andwe use it as an input to the neural network. We use a sample
size (M ) of 1024 as a default. We want to ﬁnd the probabilityof the input vector for next time step (xt+1 ) to belong to class
Ck , where k ∈ 1, 2, . . . , K, K being the number of classes.
The probability P (Ck |xt+1 ) can be written as
P (xt |Ck )P (Ck )P (xt xt+1 )(7)

sigmoid activationzt = σ(Wxz xt + Whz ht−1 + bz )(8)

rt = σ(Wxr xt + Whr ht−1 + br )(9)ct = tanh(Wxc xt + Whc (rt ◦ ht−1 ))(10)ht = (1 − zt ) ◦ ct + zt ◦ ht−1

(11)

GRU Cell
rt

W

xt
W

1-



zt

ht-1

ht = ot ◦ tanh(ct )tanh activation

tanh cttanh

tanh activation



sigmoid activationht

sum over all elements
W

Hadamard productweight multiplication

Fig. 2. GRU Cell Architecture Used in the RNN Model

C. Convolutional LSTM Network Model
The recurrent neural networks with LSTM or GRU cells, do
not consider the spatial information encoded in the the inputto-state or state-to-state transitions. To mitigate this problem,
we use a convolution within the recurrent structure of the
RNN. We ﬁrst discuss the spatio-temporal property of RF data
and then model a convolutional LSTM network to exploit thesame.
1) Formulation of Spatio-temporal property for RF data:
Suppose that a radio signal is represented as a time varying
series over a spatial region using R rows and C columns.
Here R represents the time varying nature of the signal andas such in our case it represents the total number of timestamps at which the signal was sampled (T in our case). C on

Here ◦ is the Hadamard product. Finally, we ﬁlter the output
values through the output gates (ot ) based on the cell states(ct ) as per equations (6) and (7).
(6)

tanh

(xt,ht-1)(5)

ot = σ(Wxo xt + Who ht + bo )

tanh

ct

The main drawback of using LSTM cells is the need for
additional memory. GRUs [16] have one less gate for the same
purpose, thus having a reduced memory and CPU footprint.
The GRU cells control the ﬂow of information just like the
LSTM cells, but without the need for a memory unit. It simply
exposes the full hidden content without any control. It has a“reset gate” (zt ), an “update gate” (rt ), and a cell state memory(ct ) as shown in Fig. 2. The reset gates determine whether
to combine the new input with a cell state memory (ct ) ornot. The update gate decides how much of ct to retain. The
equations (8), (9), (10), and (11), related to the different gatesand states are given below.

In equation (5), the old cell state (ct−1 ) is updated to the newcell state (ct ) using forget gates (ft ) and input gates (it ).
ct = ft ◦ ct−1 + it ◦ ct

ht

Fig. 1. LSTM Cell Architecture Used in the RNN Model

Note that Wxf and bf represent the associated weight andbias respectively, between input (x) and forget gate (f ) and σdenotes the sigmoid activation function. Once ft determines
which memories to forget, the input gates (it ) decides whichcell states (ct ) to update as per equations (3) and (4).
(4)

t

B. Gated Recurrent Unit (GRU) Model(2)ct = tanh(Wxc xt + Whc ht−1 + bct−1 )

ot

ct-1

Hadamard productA. Long Short Term Memory (LSTM) Cell Model
Though LSTM cells can be modeled and designed in various
ways depending on the need, we use the cells as shown in
Fig. 1. In one LSTM cell, there are (i) three types of gates:
input (i), forget (f ), and output (o); and (ii) a state update ofinternal cell memory. The most interesting part of the LSTM
cell is the “forget” gate, which at time t is denoted by ft . The
forget gates decide whether to keep a cell state memory (ct ) ornot. The forget gates are designed as per the equation (2) on
the input value xt at time t and output (ht−1 ) at time (t − 1).

(3)

ft

sum over all elements(1)

it = σ(Wxi xt + Whi ht−1 + bi )

tanh

it

where (P (xt |Ck )) is the conditional probability of xt given
the class Ck and (P (xt xx+1 )) is the probability of xt andxt+1 occurring in order.

ft = σ(Wxf xt + Whf ht−1 + bf )







ht-1

P (Ck |xt+1 ) =LSTM Cell(xt,ht-1)

91

the other hand represents the total number of features sampled
at each time stamp (in our case its 2048 since there are 1024features sampled each of dimension 2). Note that each cellcorresponding to one value of R and one value of C represents
a particular feature (I or Q) at a given point in time.
In order to capture the temporal property only, we use
a sequence of vectors corresponding to different timestamps
1, 2, · · · , t as x1 , x2 , · · · , xt . However, to capture both spatial
and temporal properties, we introduce a new vector χt,t+γ ,which is formulated as: χt,t+γ = [xt , xt+1 , · · · , xt+γ−1 ]. So
the vector χt,t+γ eventually preserves the spatial properties
with an increment of γ in time. So, we get a sequence of newvectors χ1,γ , χγ,2γ , · · · χt,t+γ , · · · , χt+(β−1)γ,t+βγ , where β
is R/γ, and the goal is to create a model to classify them into
one of the K classes (corresponding to the transmitters). We
model the class-conditional densities given by P (χt−γ,t |Ck ),where k ∈ 1, · · · , K. We formulate the probability of the next
γ-length sequence to be in class Ck as per equation 12. The
marginal probability is modeled as P (χt,t+γ ).
P (Ck |χt,t+γ ) =P (χt−γ,t |Ck )P (Ck )P (χt,t+γ )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

A. Signal Generation and Data Collection
In order to evaluate our methods for learning the inherent
spatio-temporal features of a transmitter, we used eight USRPs
of the same type, namely B210 from Ettus Research [19], astransmitters. The signal generation and reception are shownin Fig. 3. We used GNURadio [31] to randomly generatesignal and modulated the same with Quadrature Phase Shift
Keying (QPSK). We programmed the USRP B210s to transmit
the modulated signal over the air and sensed the same using
a DVB-T dongle (RTL-SDR) [20]. We generated the entire
dataset from “over-the-air” data as sensed by the RTL-SDR
using the rtlsdr python library.

(12)

RandomSignal2) The Model: The cell model is similar to an LSTM
cell, but the input transformations and recurrent transformations are both convolutional in nature [17]. We formulate the input values, cell state and hidden states as a 3dimensional vector, where the ﬁrst dimension is the numberof measurements which varies with the time interval γ andthe last two dimensions contain the spatial information (rows(R) and columns (C)). We represent these as: (i) the inputs: χ1,γ , χγ,2γ , · · · χt,t+γ , · · · , χt+(β−1)γ,t+βγ (previously
stated); (ii) cell outputs: C1 , · · · , Ct , and (iii) hidden states:
H1 , · · · , Ht . We represent the gates in a similar manner asin the LSTM model. The parameters t, it , ft , ot , W , b holdthe same meaning as in section III-A. The key operations aredeﬁned in equations 13, 14, 15, 16, and 17. The probability of
the next γ-sequence to be in a particular class (from equation
12) is used within the implementation and execution of themodel.
it = σ(Wxi χt,t+γ + Whi Ht−1 + bi )(13)
ft = σ(Wxf χt,t+γ + Whf Ht−1 + bf )QPSKModulation

USRP B210

Over The Air
Transmission
Datasets

Data
Collection

RTL-SDR

Fig. 3. Over the Air Signal Generation and Data Collection Technique

We collected I/Q signal data with a sample size of 1024 ateach time stamp. Each data sample had 2048 entries consistingof the I and Q values for the 1024 samples. Note that alarger sample size would mean more training examples for theneural network. Our choice of 1024 samples was sufﬁcient tocapture the spatial-temporal properties while at the same time
the training was not computationally intensive. We collected
40,000 training examples from each transmitter to avoid thedata skewness problem observed in machine learning. The
conﬁguration parameters that were used are given in Table
I. We collected two sets of data: (i) using 4 transmitters:
6.8 GB size, 160K rows and 2048 columns and (ii) using
8 transmitters: 13.45 GB size, 320K rows and 2048 columns.
Note that we intend to make the dataset publicly available
upon publication of the article.

(14)

Ct = ft ◦ Ct−1 + it . tanh(Wxc χt,t+γ + Whc Ht−1 + bc ) (15)ot = σ(Wxo χt,t+γ + Who Ht−1 + bo )(16)

B. Spatial Correlation in the Dataset

Ht = ot ◦ tanh(Ct )(17)

Correlation between data samples play a crucial rolein the process of transmitter identiﬁcation. We represent
the I and Q values of each training sample at time (t)as: [I0 Q0 I1 Q1 I2 Q2 I3 Q3 I4 Q4 · · · I1023 Q1023 ]t . We used the
QPSK modulation [32] which means that the spatial correlation should be between every fourth value, i.e., between I0
and I4 , and Q0 and Q4 . So we calculate the correlation coefﬁcient of I0 I1 I2 I3 and I4 I5 I6 I7 . Similarly, for Q0 Q1 Q2 Q3and Q4 Q5 Q6 Q7 . We take the average of all the correlation
coefﬁcients for each sample.

IV. T ESTBED E VALUATION
In order to validate the proposed models, we collected
raw signal data from 8 different universal software radio
peripheral (USRP) B210s [19]. We collected the data in anindoor lab environment with a signal-to-noise ratio of 30 dB,
and used the dataset to distinguish between 4 or 8 transmitters,
as mentioned in [10].

92trained network. In general to measure the effectiveness ofany learning algorithm, “accuracy” is used as the typicalperformance metric. However, accuracy can sometimes be
misleading and incomplete when the data is skewed. For
the task of classiﬁcation, a confusion matrix overcomes thisproblem by showing how confused the learned model is on its
predictions. It provides more insights on the performance byidentifying not only the number of errors, but more importantly
the types of errors.

Correlation

0.6
0.4
0.2
0
ID1

ID3

ID5
Transmitters

ID7

V. M ODEL I MPLEMENTATIONS AND R ESULTS
In this section we discuss the implementation of each of the
proposed recurrent neural networks. We train each network
for transmitter classiﬁcation with K classes. For the sake ofrobustness and statistical signiﬁcance, we present the resultsfor each model after averaging over several runs.

Fig. 4. Spatial Correlation in the Dataset

We use numpy.corrcoef for this purpose which uses Pearson
product-moment correlation coefﬁcients, denoted by r. The
Pearson’s method for a sample is given by:
(M
−1)


(Ii − I)(Qi − Q̄)

r =
(M
−1)
(M
−1)


2
(Ii − I)(Qi − Q̄)2
i=0

i=1

A. Implementation with LSTM Cells
As discussed earlier, the recurrent structure of the neuralnetwork can be used to exploit the temporal correlation in
the data. To that end, we ﬁrst implemented a recurrent neuralnetwork with LSTM cells and trained it on the collected
dataset using the paradigm as shown in Fig. 5. We used
two LSTM layers with 1024 and 256 units sequentially. We
also used a dropout rate of 0.5 in between these two LSTM
layers. Next we used two fully connected (Dense) layers with
512 and 256 nodes respectively. We apply a dropout rate of
0.2, and add batch normalization [36] on the output, ﬁnallypassing it through a Dense layer having 8 nodes. We use ReLU[37] as the activation function for the LSTM layers and tanh[38] for the Dense layers. Lastly, we use stochastic gradient descent [38] based optimization with categorical crossentropy training. Note that the neural network architecture
was ﬁnalized over several iterations of experimentation withthe data and we are only reporting the ﬁnal architecture here.
We achieved 97.17% and 92.00% testing accuracy for 4 and
8 transmitters respectively. The accuracy plots and confusion
matrices are shown in Figs. 6 and 7 respectively. Note thatthe number of nodes in the last layer is equal to the numberof classes in the dataset. It is also to be noted that during
the process of designing the RNN architecture, we also ﬁne
tuned the hyper-parameters based generalization ability of thecurrent network (as determined by comparing the training andvalidation errors). We also limited the number of recurrent
layers and fully connected layers for each model for fastertraining [39], since no signiﬁcant increase in the validation
accuracy was observed after increasing the number of layers.
The rows and columns of the confusion matrix correspondto the number of transmitters (classes) and the cell values show
the recall or sensitivity and false negative rate for each of thetransmitters. Note that recall or sensitivity represents the true
positive rates for each of the prediction classes.

(18)

i=0

where, M is the sample size, Ii and Qi are the sample values
−1)
1 (M
indexed with i. The sample mean is I¯ =
Ii .
M i=0
The spatial correlations of all the samples for the different
transmitters are shown in Fig. 4. We observe that for mostof the transmitters, the correlation is ∼0.42, with a standard
deviation of ∼0.2. However, transmitter 3 exhibits minimalcorrelation between these samples, which implies that the
spatial property of transmitter 3 is different from the othertransmitters. As a result Transmitter 3 should be easily distinguishable from the others. This claim will be validated later inthe experimental result section where we see 0% false positiveand false negative for transmitter 3 for all the three proposedmodels. This observation gives us the motivation to exploit
the spatial property as well as the temporal property for thecollected time-series data.
C. Neural Network Libraries
There are many libraries available in python with support fordifferent types of neural network and concurrent GPU architecture. We use Keras [33] as the frontend and Tensorﬂow [34]as the backend for our implementations. Keras is an overlay on
the neural network primitives provided by Tensorﬂow [34] orTheano [35] and provides a customizable interface for quick
deployment of complex neural networks. We also use Numpy,
Scipy, and Matplotlib Python libraries.
D. Experimental Setup and Performance Metrics
We conducted the experiments on a Ryzen 8 Core systemwith 64 GB RAM, a GTX 1080 Ti GPU unit having 11 GB
memory. During the training phase, we use data from eachtransmitter to train the neural network model. In order totest the resulting trained model, we use test data collectedfrom one of the transmitters and present the same to the

B. Implementation with GRU Cells
Next we implemented another variation of the RNN modelusing GRU cells for leveraging temporal correlation. We used93

LSTM Layer1

LSTM Layer2

Dense

Dense

Dense

Output(None,
1024
2048)

256

512

256

8

8

Input

Signal
Processing andData Collection

Data

Fig. 5. RNN Implementation with LSTM Cells for Transmitter Classiﬁcation(a) 4 Transmitters(b) 8 TransmittersFig. 9. Accuracy Plots for Transmitter Classiﬁcation using GRU Cells(a) 4 Transmitters(b) 8 TransmittersFig. 6. Accuracy Plots for Transmitter Classiﬁcation using LSTM Cells

the same architecture as the LSTM implementation, presented
in Fig. 8. The proposed GRU implementation needs fewer
parameters than the LSTM model. A quantitative comparison
is given in Section V-D. The only difference is that we
use two GRU layers with 1024 and 256 units instead ofusing LSTM cells. We achieved 97.76% and 95.30% testing
accuracy for 4 and 8 transmitters respectively. The accuracy
plots and confusion matrices are given in Figs. 9 and 10. The
GRU implementation provided a slight improvement over theaccuracy obtained using LSTM, for each run of the models,
for both the datasets.
C. Implementation with ConvLSTM2D Cells(a) 4 Transmitters(b) 8 TransmittersFig. 10. Confusion Matrices for Transmitter Classiﬁcation using GRU Cells

GRU Layer1

GRU Layer2

Dense

Dense

Dense

Output

256

512

256

8

8

Input

Signal
Processing andData Collection(None,
1024
2048)

Dense

Output

256

Dense

1024

Dense

ConvLSTM2D
Layer2(None,
2048)

Flatten

512

256

8

8

Fig. 11. RNN Implementation with ConvLSTM Cells for Transmitter Classiﬁcation(a) 4 Transmitters(b) 8 TransmittersFig. 7. Confusion Matrices for Transmitter Classiﬁcation using LSTM Cells

Data

Data

ConvLSTM2D
Layer1Signal
Processing andData Collection

Finally, in order to exploit the spatio-temporal propertyof the signal data, we implemented another variation of the
LSTM model with convolutional ﬁlters (transformations). The
implemented architecture is shown in Fig. 11. ConvLSTM2D

Input

uses two dimensional convolutions for both input transformations and recurrent transformations. We ﬁrst use two layersof convLSTM2D with 1024 and 256 ﬁlters respectively, anda dropout rate of 0.5 in between. We use kernel size of(2,2) and stride of (2,2) at each ConvLSTM2D layer. Next we
add two fully connected (Dense) layers having 512 and 256
nodes respectively after ﬂattening the convolutional output.
ReLU [37], and tanh [38] activation functions are used forthe convLSTM2D and Dense layers respectively. ADADELTA[40] with a learning rate of 10−4 and a decay rate of 0.9, isused as the optimizer with categorical cross-entropy training.
We achieved 98.9% and 97.2% testing accuracy for 4 and
8 transmitters respectively. The accuracy plots and confusion
matrices are given in Figs. 12 and 13 respectively. Being
able to exploit the spatio-temporal correlation, ConvLSTM
implementation provides improvement over the accuracies
obtained using the LSTM and GRU models, for both thedatasets.

D. Comparisons of LSTM/GRU/ConvLSTM Implementations
We used 90%, 5%, and 5% of the data to train, validate,
and test respectively. We ran each model for 50 epochs withearly-stopping on the validation set. One epoch consists of aforward pass and a backward pass through the implemented

Fig. 8. RNN Implementation with GRU Cells for Transmitter Classiﬁcation

9497.20%ConvLSTM

92.00%

95.30%

LSTM

98.90%
ConvLSTM

GRU

97.17%

97.76%LSTM

GRU(a) 4 Transmitters(b) 8 TransmittersFig. 12. Accuracy Plots for Transmitter Classiﬁcation using ConvLSTM Cells(a) 4 Transmitters(b) 8 TransmittersFig. 14. Comparison of Testing Accuracies of Different Types of Recurrent
Neural Networks
Approaches
Trafﬁc Sequence
Recognition [21]Automatic Modulation
Classiﬁcation [24]Transmitter
Classiﬁcation (Ours)Model

SNR (dB)

Acc (%)

Inputs

LSTM

20

31.2

Hybrid Real-synthetic Dataset

LSTM

20

90

Synthetic Dataset [25]ConvLSTM

30

97.2

Raw Signal

TABLE IIIC OMPARISON OF P ROPOSED A PPROACH WITH THE E XISTING RNN
I MPLEMENTATIONS

classiﬁcation by comparing the accuracy with that obtainedusing other methods, for the same task. It is to be noted that
all the other methods use expert crafted features as inputs ([27]–[30]), or work with synthetic datasets ( [21], [24]). Our
method, on the other hand achieves superior accuracy (97.2%)
using features automatically learned from the raw signal data,
thereby paving the way for real-time deployment of large scaletransmitter identiﬁcation systems.
It must be pointed out that the proposed RNN models can be a
trained using raw signal data from any type of radio transmitter
operating both in indoor as well as outdoor environments.
We would also like to point out that though our data wascollected in a lab environment, we had no control overthe environment, there were other transmissions in progress,
people were moving in and out of the lab and there was a
lot of multi-path due to the location and design of the lab.
Furthermore the power of the transmitters was low and hence
this compounded the problem further. Given this, though we
say that the data was collected in a lab environment, in reality
it was an uncontrolled daily use environment reﬂective of
our surroundings. Thus we can safely say that these methods
will work in any real world deployment of large scale radio
network. In summary,• Exploiting temporal correlation only, recurrent neuralnetworks yield 95-97% accuracy for transmitter classiﬁ-(a) 4 Transmitters(b) 8 TransmittersFig. 13. Confusion Matrices for Transmitter Classiﬁcation using ConvLSTM
Cells

architecture for the entire dataset. The overall accuracy of thedifferent implementations is shown in Table II. We ﬁnd that theimplementation of convolutional layers with recurrent structure (ConvLSTM2D) exhibit the best accuracy for transmitterclassiﬁcation, which clearly shows the advantage of using the
spatio-temporal correlation present in the collected datasets.
In Fig. 14, we present a better illustration of the achieved
classiﬁcation accuracies for the different implemented models.
E. Comparisons of Proposed and Existing Approaches
Next we present two comparative studies of our proposed
implementations with some existing techniques. We introduce
a differential analysis of different RNN based implementationsin the RF domain in Table III. Another comparative study
for different transmitter classiﬁcation techniques is shown in
Table IV.
The “Inputs” column in both the tables refer to the typeof inputs used for the methods under consideration. Table III
shows a comparison of our ConvLSTM based RNN for transmitter classiﬁcation with other RNN based implementations
for separate tasks like modulation recognition and trafﬁcsequence recognition. Table IV establishes the efﬁcacy of our
ConvLSTM based RNN model for the task of transmitter#Trans444888Models
LSTM (6 layers)
GRU (6 layers)
ConvLSTM (6 layers)
LSTM (6 layers)
GRU (6 layers)
ConvLSTM (6 layers)#Parameters14.2 M
10.7 M
14.2 M
14.2 M
10.7 M
14.2 M

Approach
Orthogonal Component
Reconstruction (OCR) [30]
Genetic Algorithm [27]Multifractal
Segmentation [28]k-NN [29]Ours

Acc (%)
97.17
97.76
98.90
92.00
95.30
97.20

TABLE II
ACCURACY FOR D IFFERENT I MPLEMENTATIONS#TransSNR (dB)

Acc (%)

3
5

20
25
Not
mentioned
30
30

62 - 71
85-98

InputsSpurious
Modulation
Transients

92.5
97.2
97.04

Transients
Transients
Raw Signal

88
8TABLE IVC OMPARISON OF THE O UR I MPLEMENTATION WITH THE E XISTINGT RANSMITTER C LASSIFICATION A PPROACHES

95

cation using LSTM or GRU cells. RNN implementation
with GRU cells needs fewer parameters than LSTM cells
as shown in Table II.
• Exploiting spatio-temporal correlation, the implementation of RNN using ConvLSTM2D cells provides better
accuracy (97-98%) for transmitter classiﬁcation, thus providing a potential tool for building automatic real worldtransmitter identiﬁcation systems.
• We present a comparative study of the proposed spatiotemporal property based ﬁngerprinting with the existing
traditional and neural network based models. This clearlyshows that the proposed model achieves the best accuracy
compared to any of the existing methods for the task.
VI. C ONCLUSION
In this paper, we proposed a robust transmitter identiﬁcation
technique by exploiting both the inherent spatial and temporal
properties of RF signal data. We designed and implemented
three different types of neural network models for this purpose.
We collected over-the-air signal data from USRP B210s andused the same to train and validate our system. The RNN
model using LSTM cells yields 92% testing accuracy leveraging only temporal property of the signal data. The one using
GRU cells does the same with lower space requirement andyields a better testing accuracy of 95.30%. Finally, the RNN
model with ConvLSTM cell achieves 97.20% testing accuracy
for the same dataset leveraging both the temporal and spatial
correlations. In the future we plan to use these methods for
identiﬁcation of actual infrastructure transmitters (for example
FM, AM and GSM) in real world settings.
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