Jamming Detection based on Doppler Shift Estimation in Vehicular Communications Systems

International Journal of Computer Networks & Communications (IJCNC) Vol.12, No.5, September 2020
DOI: 10.5121/ijcnc.2020.12502 17
JAMMING DETECTION BASED ON DOPPLER
SHIFT ESTIMATION IN VEHICULAR
COMMUNICATIONS SYSTEMS
Javad Afshar Jahanshahi
Universidad Católica Los Ángeles de Chimbote,
Instituto de Investigación, Chimbote, Perú
ABSTRACT
Since Doppler shift is one of the most important parameters in wireless propagation, the evaluation of the
Doppler shift at the base station (BTS) in vehicular communications improves BTS in many aspects such as
channel varying rate, jamming detection, and handover operations. Therefore, in this study, we propose a
novel method at a base station based on the received user signal to estimate the channel Doppler shift seen
by BTS. Utilizing the inherent information existed in common receivers, a level crossing rate (LCR) based
Doppler shift estimation algorithm is developed without any excessive hardware. Moreover, a jamming
detection algorithm is improved based on the proposed Doppler shift estimation scheme. The performance
of the proposed scheme is evaluated in a Terrestrial Trunked Radio (TETRA) network, and comprehensive
experimental results have shown superior performance in a wide range of velocities, signal to noise ratios
and jammers.
KEYWORDS
Jamming Detection, Vehicular Communications, Level Crossing Rate, Mobile Communication, TETRA
(Terrestrial Trunked Radio).
1. INTRODUCTION
In the vehicular communications, radio frequencies are interfered by inaccurate frequency
planning, bandwidth allocation errors, or lack of ideal filters in transceivers. Unlike radio
frequency interference, jamming signals are transmitted to occupy (i.e. destroy) the radio
channels. They interfere with wireless communication channels by intentionally preventing the
transmitter to access the right link, excluding the receiver from obtaining accurate information
and/or disrupting the transmitted information over the wireless channel. Hence, they cause a lack
of security in the communications network and degrade the quality of service experienced by the
users [1].
To improve the performance of the wireless vehicular system, it is important to estimate the
speed of a mobile terminal in wireless communication links. Knowing the speed of the mobile
user enables the receiver to efficiently evaluate channel estimation. Similarly, in adaptive
transmission, it helps the transmitter to adjust a suitable modulation/coding scheme according to
the channel condition. Especially, speed information can also be used in anti-jamming
techniques, when the receiver tries to differentiate between signal attenuations caused by
jamming and channel effects [1-2]. Meanwhile, speed estimation by additional sensors like
gyroscopes or accelerometers, and systems like GPS (Global Positioning System), increases the
complexity and overall costs of user terminals, and furthermore, reduces the handset battery
lifetime. Therefore, several techniques have been proposed in the literature for mobile terminal

18
speed estimation based on channel Doppler shift measurement, and some of them have been
implemented in existing mobile communication systems. Covariance estimation schemes
estimate Doppler frequency shift by computing covariance value between training received
samples [3-12]. Other schemes for Doppler frequency shift estimation have used spectral analysis
and variance [13-14], estimation of channel envelope and angle [15-16], statistical information of
channel phase variations [17-18], Eigen based spectral estimation [19-20], spectrum estimation
method based on channel power spectrum density [21-22], multi-vector test by using maximum
likelihood approaches [23-24], wavelet analysis by tracking changes in the temporal scale [25-26]
and channel auto-correlation [27-28]. In [29], the authors proposed an LCR based algorithm that
estimates terminal’s Doppler shift over each Doppler shift estimation window, and consequent
windows do not overlap each other. They also used a single threshold for signal power
comparisons. However, the proposed algorithm in [29] cannot follow the mobile terminal’s
variation in low SNR conditions. In this paper, the Doppler shift estimation algorithm is
improved by utilizing a Doppler shift estimation window that slides over bursts with overlaps and
by introducing two different low and high thresholds for power level comparisons. These
thresholds are updated for each Doppler shift estimation window’s movement in order to better
track the Doppler shift variations even in low SNR conditions. This algorithm uses only inherent
cellular system information, which means there is no need for any hardware modification of the
user terminal, as well as cellular network signalling structure.
In order to reduce the implementation complexity, improve the performance and enhance the
efficiency of the jamming detection algorithms proposed in the mentioned references, several
improvements have been made. In the proposed algorithm, in addition to compromising and
modifications in the Doppler shift estimation process, the modulation detection process is done
only for the bursts which have been destroyed. Also, by using a new proposed Doppler shift
estimation algorithm, the scaling factor value is estimated with higher accuracy. In addition, the
high and low thresholds are set based on several experimental trials. Simulation results show that
the proper value of these parameters has a considerable impact on algorithm performance. The
proposed algorithm is modelled in a TETRA base station receiver.
The rest of this paper is organized as follows. In section 2, the latest related works are reviewed.
Then we present the system model in detail in section 3. The proposed jamming detection
algorithm is presented in section 4. In section 5, simulation results of the proposed algorithms,
both shift Doppler estimation algorithm and jamming detection method, are reported. Finally, in
section 6, we provide our concluding remarks.
2. RELATED WORKS
Utilizing different encoding, modulation, and decoding methods, one of the most important
problems of current cellular systems (GSM, TETRA, CDMA, LTE, 5G, and 6G) is that they are
not primarily designed to work in the jamming environments. Therefore, the best method for
jamming detection in these systems is one that will have the most efficient detection
performance, lowest cost and minimum complexity and maximum hardware and software
compatibility with available hardware [30].In [31], a certain test signal is transmitted to any
mobile users in the coverage area. Then the uplink is monitored to receive the response signal
form end-users. In case the respond is not received, the interference in the channel is measured. If
the interference level is higher than a threshold, the existence of jammer is announced. Their
proposed algorithm is very complicated, and it causes an additional cost and is not beneficial to
be used in common systems [32]. The proposed jamming detection algorithm presented in [33]
measures channel power between the base station and mobile user to compare with a maximum
noise power level. This algorithm also needs a modification in common hardware structures of
BTS [34]. In [35], the proposed scheme firstly measures the synchronization peak and

19
synchronization average of each burst and secondly the synchronization average is weighted
based on the channel status. If the weighted average is more than a predefined threshold, the
received burst signal is modulated. Otherwise, it has been ignored. And all the received bursts are
considered as a jammer signal. If the number of ignored is more than a high threshold, the
jamming status is distinguished. In [18], the computational complexity of the proposed algorithm
is very complicated mainly because of the modulation detection process [36]. Although the
scaling factor, which is used by the algorithm, has a significant effect, but in [35] only a very
simple method has been used to estimate it, due to its simplicity and complexity reduction.
However, this method has a low performance level in poor channel conditions (such as low SNR
and high Doppler shift). In the proposed method in [37] for jamming detection, the channel
power between the base station and the mobile station is measured and compared with the
maximum noise power level. If it is more than maximum noise power level, the temporary
mobile subscriber identity, which is recognizable to the both base station and mobile station, is
checked. If the temporary mobile subscriber identity is not understood, the number of jammed
channel increases. In this algorithm, the hardware structure of the mobile station should change.
To distinguish anomaly traffic, an Entropy-based jamming detection system evaluates the
different kinds of entropy of traffic descriptions obtained by randomly distributed data structures
[38]. At first, Shannon-based entropy measurement and its variances utilized to a distinct
boundary between normal and anomalous behaviour of data flows. The authors in [38] proposed
parameterized entropy and supervised learning methods to improve the accurate detection of
small or low attacks in IP networks. Authors in [39] presented that Shanon entropy-based
approach has a limited descriptive capability and a proper tuning parameter needed. The authors
in [40] proposed a novel jamming detection based entropy method that uses different measures of
entropy to detect anomalous behaviours such as Shanon entropy, Tsallis entropy, Renyi entropy
[41]. The above studies also show differences in measurement methods and approximate
characteristics of entropy-based jamming detection methods. Based on the statistical method, the
authors in [42] proposed a jamming detection algorithm that investigates the spatial and temporal
correlations of data in wireless networks. However, this method leads to a larger amount of
computations. To deal with resource constraints, fuzzy logic-based anomaly detection has been
used to estimate feature traffic in the statistical model. In [43], fuzzy logic is used to identify if
the anomaly is presented in a time interval. It delivers a threshold to observe forthcoming data
traffic. The authors in [44] proposed a jamming detection method that uses fuzzy logic to create a
single threshold over multiple metrics.
In fact, the main inspiration for our work is that the existing Doppler estimation based jamming
detection schemes in the literature fail to meet the real-time monitoring and detection
requirements for low complexity, accurate detection, and optimized speed. Therefore, an efficient
method that can accurately detect jamming signals is essential for vehicular communications
systems.
3. SYSTEM MODEL
In wireless telecommunication networks, the base station is a network element providing an air
interface between radio units of mobile subscribers and the network infrastructure (referred to as
Switching and Management Infrastructure). The base station is responsible for radio transmission
and reception to and from wireless subscriber stations over the air interface. An example of a
basic architecture of a base station receiver along with the jamming detection block is shown in
Figure 1

20
Figure 1 basic architecture of a base station receiver along with the jamming detection block
RF part amplify radio frequency (RF) signals received from an antenna, which provide selectivity
and mix the received carrier to a lower intermediate frequency (IF). Then the received signals
delivered to the baseband sections, such as the demodulation block, synchronization block,
diversity combining and the Cyclic Redundancy Check (CRC) block. The diversity reception is
optional, if it is employed, the baseband signals can be combined in the diversity-combined
block. This improves the bit error rate considerably. In the present work, this block has not been
employed.
In Time Division Multiple Access (TDMA) systems, the physical channel is a time slot. There
can be a present number of time slots, i.e. physical channel, on the same carrier frequency. A
burst is a period of an RF carrier which is modulated by a data stream. A burst thus presents the
physical content of the timeslot or a subplot. The burst, i.e. the modulated data stream in the time
slot, also contains a training sequence in the center of the time slot.
The received bursts after transmission to the baseband frequency are demodulated. Then they
delivered to the synchronization block. In the synchronization block, in order to synchronize the
received bursts in the synchronization block, a cross correlation function is calculated between
the selected training sequence reference signal, which exists in the base station receiver, and
training sequence samples of the incoming signal data. Then an average value E(RRxTx) of the
cross-correlation vector is calculated in order to define the average amplitude of the vector. Next,
a peak value P(RRxTx) is searched from the correlation vector, the peak is assumed to be in the
center of the training sequence. Then, the synchronized bursts are delivered to the CRC block.
The CRC check is a method for detecting errors in the transmission of data by using a polynomial
code and cyclic check character. In the CRC block, cyclic redundancies of the bursts are
analyzed. If the burst has been extremely damaged by the external factors (such as channel
Doppler shift, environmental noise, or the jamming signals) and is not recyclable, the CRC pass
flag is considered to be zero. Otherwise, it set to 1.
As is observed in Figure1, the synchronized bursts are delivered to the CRC block and the
jamming detection block as well. As we will see in the following sections, the jamming detection
block includes Doppler shift estimation block and modulation block. The Doppler shift
estimation is performed by the synchronized burst. The average and the peak values of each burst
are used to make decision in the modulation detection block.

21
4. THE PROPOSED JAMMING DETECTION ALGORITHM
The proposed algorithm uses three parameters of each burst along with the synchronized bursts.
These three parameters are as follows: the average synchronization, the peak synchronization,
and the CRC flag. The incoming parameters to the jamming detection block have been shown in
Figure2.
Figure 2 the jamming detection block
At first, power amplitude of the incoming synchronized bursts is measured. Then, the power
amplitude of each burst is used in both Doppler shift estimation and Received Signal Strength
Indicator (RSSI) comparison processes. In the Doppler shift estimation process, the Doppler shift
that bursts experienced through the channel is estimated. Then, based on the estimated Doppler
shift, the value of the scaling factor is identified. In the next section, we will describe how to
identify this factor.
The jamming detection block also includes modulation detection that is used for detecting
whether or not the synchronized bursts are modulated according to the modulation method used
in the specific radio system. In order to reduce the computational complexity, only the
modulation detection’s accuracy of the damaged bursts will be examined. The estimated Doppler
shift, the average, and the peak values of each burst are used to make decisions in the modulation
detection block. The status bits 0 and 1 represents a modulated burst and an un-modulated burst,
respectively, in the received burst flow.
Then, the jamming detection also checks the RSSI level of each burst, in order to ensure a
sufficient quality for the signal. If the power of the received burst is too low (below a certain
predetermined limit like fixed threshold), the last burst is considered to be a modulated burst
regardless of the decision of the modulation detection. This feature prevents unnecessary alarms
if the channel is not good enough to ensure a sufficient signal quality for the modulation

22
detection. However, the jamming detection algorithm is never capable to detect a jamming signal
with extremely low power. This is not a serious problem, since a low power level jamming signal
would not usually affect the performance of a receiver. In a certain signal to noise ration level,
the number of un-modulated bursts in the time window may oscillate at the critical level of the
threshold value. This causes the jamming detection to switch between the ON and OFF states. In
order to keep from happening unexpected alarms, a transition interval is employed. The transition
interval sets two limits, an upper and a lower one. Figure 3 shows the flowchart of the proposed
jamming detection algorithm.
Figure 3 flowchart of the proposed jamming detection algorithm
4.1. The Modulation Detection Block
The base station receiver includes modulation detection that is used for detecting whether or not
the received burst is modulated, according to the modulation method used in the specific radio
system. The decision in the modulation detection is carried out by comparing the weighted
synchronization average value with the synchronization peak value, which is calculated in the
synchronization block, earlier. The modulation detection equation is given by [35-36]:

23
T = P(RRxTx) − ζ × E(RRxTx) (1)
Where P(RRxTx) denotes the synchronization peak and E(RRxTx) denotes the synchronization
average which are provided by calculating the cross-correlation function between training
sequence samples of the incoming signal data (Tx) and the selected training sequence reference
signal (Rx), which exists in the base station receiver. Ζ represents the scaling factor which
depends on the channel Doppler shift. If the value of T is higher than or equal to zero, the burst is
considered to be modulated and the modulation status bit is set to 1. Otherwise, the burst is
unmodulated and the modulation status is set to 0.
{
T ≥ 0 Modulated
T < 0 Unmodulated
(2)
Then, the power level of the unmodulated burst is compared with a certain predetermined limit
(fixed threshold). If it is lower than the fixed threshold, regardless of the decision of the
modulation detection, the burst considered to be modulated. This means that the jamming
detection algorithm recognizes only the jammer with the power more than the fixed threshold.
The modulation detection algorithm is shown in Figure 4.
Figure 4 the modulation detection algorithm
4.2. The Proposed Channel Doppler Shift Algorithm
Figure 5 shows the structure of the received complex samples over one Doppler shift estimation
window (i.e. 0.5 or 1 second). This window slides over samples of the received signal. Each
window divided into N groups of samples:
[ ]
WL
N
M
 (3)

24
where[.] is the rounding down operator, WLis the number of samples within a Doppler shift
estimation window and M is the segmentation factor. The segmentation factor will be updated for
each received burst.
Figure 5 structure of Doppler shift estimation window.
The flowchart of the proposed Doppler shift estimation algorithm is illustrated in Figure 6. At the
first stage, the power of the received signal is calculated. This power is measured within a fixed
size Doppler shift estimation window. Then, the power meter computes group powers
( ), {1,2,3,..., }D
WL
S i i
M

*
2
( 1)* 1
1
( ) . ( )
( )
i M
D
z i M
S i s z
M n   
  (4)
where ( )DS i is the power of samples over the ith
group. In the third stage, RMS meter computes
the root mean square of group powers during Doppler shift estimation window as:
2
1
1
. ( )
WL
M
D
i
RMS S i
WL
M

  (5)
The calculated RMS is then used to determine low and high thresholds for level crossing
calculations. Then, high and low level crossing thresholds HT and LT are calculated. These
thresholds should be fractions of the RMS value calculated in previous stage:
.
.
1
H
L
T y RMS
T x RMS
o x y


   
(6)
In the fourth stage, level crossing counter counts level crossing frequency RL , which indicates
how many times group powers ( )DS i cross thresholds HT and LT in positive slope.

25
Figure 6 Signal Flow of the Proposed Algorithm.
In Rayleigh fading channel with 2-dimensional isotropic scattering, the Doppler shift is given by
[45-46]:
2
.
. 2
R
D
H L
L e
f
T T
RMS

 



 (7)
where Df denotes the Doppler shift, and e is Euler’s number. The Doppler shift of each received
burst is given by its angle of arrival n , the carrier frequency cf , the propagation speed C (which
is the speed of light), and the mobile terminal speed v . It can be calculated as:
. .cos( )c
D n
f
f v
C
 (8)
For the maximum value of the Doppler shift, the mobile terminal speed is given by
max
.D
c
C
v f
f
 (9)
For example, when signaling is done with 396cf MHz in a typical value for a TETRA system,
100 km/h terminal speed results in maximum Doppler shift of max
37Df Hz . The estimated

26
speed is reported in the fifth stage. In the last stage, segmentation factor for the next incoming
burst is updated as:
max
[ ]
*
s
D
f
M
external factor f
 (10)
where[.] is again a rounding down operator, sf is sampling frequency, maxDf is maximum
Doppler frequency and “external factor” which depends on the channel type. In rapidly changing
channels, since the amplitude of the signal varies more rapidly during a burst, the limits of
external factor cannot be set as high as what it is in static channels. A rapidly changing channel
may appear in some situations, i.e. where the speed of the user terminal is high. The algorithm
interrupts until it receives new bursts. Algorithm started again (go to stage two) by using this new
value of M when a new burst arrives.
4.3. How to Calculate the Scaling Factor
By increasing user’s speed, channel Doppler shift and thus the channel power change rapidly.
Since it increases the value of the synchronization average, E(RRxTx), this makes the decision of
the modulation detection uncertain. By decreasing the amount of the scaling factor, the
performance of the modulation detection is improved. In other words, the sensitivity of
modulation detection is reduced, which means that the tendency to classify bursts as unmodulated
in difficult channel conditions is decreased. On the other hand, in static channels (channels that
have low power changes), the scaling factor should be increased such that the modulation
detection is more sensitive and it has a higher tendency to neglect bursts.
The scaling factor ζ is set based on the channel conditions and the amount of the channel Doppler
shift expresses the status of the channel. Therefore, the scaling factor should be set so that in a
certain Doppler shift and for all possible signals to jamming ratios (SJRs) has the best
performance. To obtain the relation between the scaling factor and the amount of the channel
Doppler shift, a realistic physical layer simulator of TETRA system including transmitter,
channel, and receiver has been used. First, it is necessary that the Doppler shift’s effect is
determined, without considering the effect of the environmental noise on the received data at the
receiver (e.g. a high signal to noise ratio: SNR = 30dB). For this purpose, the mobile station
transmitted data have been passed through a Rayleigh fading channel with different Doppler
shifts. The percentage of extremely damaged bursts versus SJR in different channel Doppler shift
values has been shown in Figure 7. The horizontal axis includes SJR values from 0 to 15 dB and
the vertical axis shows the percentage of the damaged bursts which are not recyclable by the
CRC block (total number of bursts have considered to be 1000). The jammer signal has been
considered a fixed amplitude sinusoid wave with 1 kHz frequency and has been applied to all the
transmitted bursts over the channel

27
Figure 7 the percentage of the damaged bursts which are not recyclable
As can be inferred from Figure 7, by increasing the Doppler shifts in a certain SJR, the
percentage of the damaged bursts increases as well. Consider a high value of SJR (e.g. SJR=15),
where the jammer obviously does not affect the received data, considerably. It can be seen that by
increasing the channel Doppler shift, the percentage of the unrecyclable bursts increases.
Therefore, the scaling factor should be adjusted so that the jammer detection algorithm
distinguishes between such damages and the damages caused by Jammer and do not consider
these damaged bursts to be the jammed bursts. In order to obtain the scaling factor ζ range based
on the different Doppler shifts, decision criteria, 𝛿, is defined as follows.
𝛿 = ∑|𝐶𝑅𝐶. 𝑃𝑎𝑠𝑠(𝑖) − 𝑀𝑜𝑑. 𝐹𝑙𝑎𝑔(𝑖)|
𝑁
𝑖=1
(11)
Where |. | is absolute value, Mod. Flag(𝑖) is the modulation status of 𝑖th burst, CRC.Pass(𝑖) is the
CRC status of the 𝑖th
burst and 𝑁 is the total number of the transmitted bursts. If the modulation
status of the 𝑖th
burst is 0, then the burst considered to be modulated. Otherwise, the modulation
status is set to 1 and it considered to beunmodulated. If the received burst is retrieved correctly,
the status bit of the CRC.Pass(𝑖) is 0 and when it is recycled incorrectly, the CRC.Pass(𝑖) is set
to 1. Theδ changes versus a range of the scaling factor ζ values for a given channel Doppler shift
(e.g. 140𝐻𝑧) and for different values of SJR is shown in Figure 8. In order to focus on the
jammer, these results are driven with the assumption of negligible environmental noise (e.g.
SNR=15).

28
Figure 8 theδchanges versus a range of the scaling factor ζ in Doppler shift 140𝐻𝑧
By looking at the definition of δ, we can find that in ideal situation, δ should have its minimum
value at low SJRs (here, SJRmin = 0) and its maximum value at high SJRs (here, SJRmax = 15)
Therefore, the best scaling factor, ζopt, should be chosen so that the value of 𝛿 in the SJRmin has
the highest difference with the value of 𝛿 in SJRmax.:
ζopt = argmaxζ( δSJRmax
− δSJRmin
) (12)
Figure 9 variation of ( δSJR=15 − δSJR=0) versus the range of the scaling factor ζ in Doppler shift 140𝐻𝑧.

29
In Figure 9 variation of the equation (12) versus the range of the scaling factor ζ in the channel
Doppler shift of 140𝐻𝑧 is shown. The optimum value of the scaling factor for other Doppler
shifts say 30 -120 Hz, has been deduced in a similar way and shown in Figure 10.
Figure 10 variation of ( δSJR=15 − δSJR=0) versus the range of the scaling factor ζ
4.4. The Jamming Detection window Block
Having determined the modulation status of each received burst by using the concluded scaling
factor from the estimated Doppler shift, the synchronization average, and the synchronization
peak of each burst, the unmodulated burst’s power is compared with the fixed threshold in order
to ensure sufficient power for the received burst. Then, the modulation status is changed if the
power of the received burst is below the fixed threshold. Afterward, the jamming detection
algorithm counts the number of the unmodulated burst within a fixed time window which is
called ‘jamming detection window’. The jamming detection window slides over the received
burst status bits of the modulation detection block. The moving step of the jamming detection
window is only one burst and its time is fixed and its length depends on the required accuracy of
the system. The jamming detection window is shown in Figure 11.

30
Figure 11 the jamming detection window
The purpose of applying the jamming detection window is to decrease the harmful effects of
environmental noise as much as possible. In jamming detection window, the number of
unmodulated bursts is counted. In order to avoid an uncertain jamming detection decision in the
jamming detection window, especially in low SNRs, a transition interval set as up and a low
threshold (which are called 𝑈𝑝𝑇𝐻 and 𝐷𝑜𝑤𝑛𝑇𝐻, respectively). The transition interval reduces
the amount of unnecessary alarms in the case of the varying channel. Then, it is checked whether
or not the previous burst was jammed. If the last burst was jammed, the number of unmodulated
bursts is compared with𝐷𝑜𝑤𝑛𝑇𝐻. Otherwise, the number is compared with 𝑈𝑝𝑇𝐻. The jamming
detection flag bit sets to 1, if the number of unmodulated bursts exceeded the threshold in both
states. Consequently, the jamming detection flag bit is up (i.e. bit=1) until the amount of
unmodulated bursts drops below the threshold in both states. The jamming detection algorithm is
shown in Figure 12. Having indicated the jamming status of the burst, the jamming detection
window moves ahead.
5. DISCUSSION AND SIMULATION RESULTS
In order to evaluate the performance of the proposed algorithms for estimating the channel
Doppler shift and jamming detection by BTS for the uplink channel, simulations are performed
according to conditions reported in Table 1. In the simulation, we used a realistic physical layer
simulator for TETRA systems including a Rayleigh fading channel.

31
Figure 12 algorithm of the jamming detection
Table 1 Simulation Parameters
Parameters Values
Simulated Receiver TETRA Receiver
Carrier Frequency 396 MHz
Modulation Mode / 4 QPSK
Access Method TDMA with 4 timeslots
per carrier
Channel Model Rayleigh Fading Channel
Speed of Mobile 0-120 km/h
Length of the Doppler
shift Estimation Window
500 msec (34 Bursts), 1
Burst= 14.17 msec
Length of the jamming
detection Window
500 msec (34 Bursts), 1
Burst= 14.17 msec
Jammer Signal Sinusoid Signal with
1𝑘𝐻𝑧 frequency
Sampling Frequency 8 kHz
Simulation burst length 1000 Bursts
[𝑆𝐽𝑅 𝑚𝑖𝑛, 𝑆𝐽𝑅 𝑚𝑎𝑥] [0 𝑑𝐵, 15 𝑑𝐵]

32
Figure 13 shows the model of the TETRA system and the Jammer. The transmitted signals are
attacked by a jammer, then the jammed signal passes through a Rayleigh fading channel, and at
the receiver, the additive white Gaussian noise is added.
Figure 13 how the jamming signals have been applied to the transmitted signals
The percentage of the extremely damaged bursts at the TETRA receiver in ideal conditions (i.e.
𝑆𝑁𝑅 = 30 𝑑𝐵 and channel Doppler Shift = 0) is shown in Figure 14. The jammer is a sinusoid
wave. These values are the percentage of the unrecyclable bursts, which are not able to be
recycled based on the CRC pass block. On the other hand, these values are used as the expected
number of destroyed bursts, (Γ) , for different 𝑆𝐽𝑅𝑠.
Figure 14 the percentage of the extremely damaged bursts in the TETRA receiver in ideal conditions.
5.1. Performance of the Proposed Channel Doppler Shift Estimation Algorithm
The accuracy of the proposed algorithm in tracking the channel Doppler shift is shown in Figure
15. After receiving 34 bursts (34 burst = 0.5 sec), the proposed algorithm starts, and the initial
estimation of user’s Doppler shift are performed. Then, the Doppler shift of each incoming burst
is estimated. It can be seen that the proposed algorithm outperforms the reference algorithm [29-
30] in following the channel Doppler shift, in low SNR conditions.

33
Figure 15 Comparison between the result of Doppler shift estimation in proposed method and Hong
method [29].
5.2. Effects of Environmental Noise
In order to assess the effect of environmental noise on the performance of the proposed jamming
detection algorithm, the parameter Π is defined as follows:
Π =
∑ |JamVec(i) − Mod.Flag(i)|N
i=1
N
(14)
where |. | is the absolute operator, Mod.Flag(𝑖) is the modulation status of the 𝑖th
burst,
JamVec(𝑖) is the jamming detection status of the 𝑖th
burst and N is the number of all received
bursts in the TETRA receiver. If the 𝑖th
received burst is unmodulated, then theMod.Flag( 𝑖) = 1.
Otherwise, it is modulated andMod. Flag( 𝑖) = 0. TheJamVec( 𝑖) = 1, if the 𝑖th
burst is jammed
and JamVec( 𝑖) = 0 if it is not jammed.Πis the relative difference between these two flag statuses.
Figure 16 shows the parameter Π versus SJR changes in different SNRs. Here, the sinusoid
jamming signal with 1kHz is applied on the transmitted signals. Then, the jammed signal has
been passed through the Rayleigh fading channel with 0Hz Doppler shift.
Figure 16 parameterΠ variations versus SJRfor different SNRs.

34
The higher values of Πshow more correlation between being jammed and being unmodulated.
Therefore, in high Πvalues jamming detection only based on modulation status is much reliable.
In low Πvalues, more caution in the jamming detection process only based on the modulation
detection status should be exercised. This caution is considered by using the jamming detection
window, the 𝑈𝑝𝑇𝐻 and 𝐷𝑜𝑤𝑛𝑇 limits.
Since the sinusoid jammer signal has been applied on all sent bursts, the relative difference
between the jamming detection statuses JamVec(. ) and the modulation detection statuses
Mod.Flag(. ) by reducing the jammer power. This means that, the destroyed bursts by
environment noise is considered as the damaged bursts by the jammer. Expected by increasing
the SJR, the Π increases and reachs to 1 in SJRs more than 13𝑑𝐵 (because based on Figure 14, the
percentage of the destroyed bursts by the jammer is 0 when 𝑆𝐽𝑅 is more than 13𝑑𝐵). However,
the environmental noise effects cause the difference between the jamming and modulation
detection statuses. By decreasing the𝑆𝑁𝑅, for instance, consider a certain 𝑆𝐽𝑅 (e.g. 𝑆𝐽𝑅 = 20),
the environmental noise effect increases and then the Π value reduces more. The algorithm
performance is improved by applying the fixed𝑇𝐻, appropriate 𝑈𝑝𝑇𝐻 and 𝐷𝑜𝑤𝑛𝑇 limits. Then,
the differences between the JamVec(. ) and Mod.Flag(. ) statuses reduce.
Compared to Figure 16, this improvement is observed in Figure 17. The values of the fixed TH,
UpTH and DownTH are set to −85 dBm, 12 and 9 dB, respectively. The jamming detection in
low and high SJRs is easier than detection in middle SJRs (i.e. the SJRs form 5dB to 13dB)
because a proportion of the jammed bursts are destroyed. This problem is solved to a great extent
by jamming detection window, and UpTH and DownTH limits. Precise determination of the
threshold parameters has a great impact on the jamming detection performance and these
parameters should be obtained by the extensive experiments.
Figure 17 the improved parameter Π versus SJR for different SNRs.

35
5.3. False Alarm and Missed Detection
In order to evaluate the performance of the proposed algorithm, two criteria of false alarm and
missed detection are defined these criteria are calculated as follows.
False AlarmSJR = ∑
ΔSNR − Γ
k × Γ
k
SNR:{ΔSNR>𝛤} (15)
Missed DetectionSJR = ∑
Γ − ΔSNR
k′ × Γ
k′
SNR:{ΔSNR<𝛤 }
Where k and k′ are the number of times that the number of detected jammed bursts are more or
less than the real numbers, respectively. ΔSNRis the number of detected jammed bursts by the
proposed jamming detection algorithm in a certain 𝑆𝑁𝑅. Γis the number of expected jammed
bursts by a certain 𝑆𝐽𝑅 which are announced by CRC block in the ideal conditions. The amount
of the normalized errors is summed together to obtain the number of false alarms and the missed
detections at a specific SJR and in a specific range of SNRs.
The achieved number of false alarms and missed detections by applying the proposed jamming
detection algorithm as well as a reference algorithm, are shown in Figure 18. The amounts of the
fixed TH, UpTH and DownTH are set to −85 dBm, 12 and 9 dB, respectively
Figure 18 the amount of the false alarm and the missed detection
The proposed algorithm in [29] cannot carefully follow the channel Doppler shift in low𝑆𝑁𝑅
conditions. Overestimating a Doppler shift causes to obtain a lower scaling factor that is needed
and the number of bursts that are marked as unmodulated, increases, consequently. Therefore, the
number of false alarms increases as well.

36
As mentioned before, the UpTH and DownTH limits are obtained by experimental tests and have
a great influence on the false alarm and the missed detection of the proposed jamming detection
algorithm. These limits indicate the sensitivity of the proposed jamming detection algorithm
against the attack. By decreasing these parameters, the algorithm sensitivity increases. Therefore,
the number of false alarms increases, and the number of missed detections decreases. The
optimized values for these thresholds should be obtained by doing experimental tests. The
optimized value of these two limits and their impacts on detection performance are shown in
Figure 19. The optimized value of the UpTH and DownTH is 18 and 14, respectively.
Figure 19 using the optimized value of theUpTH and DownTH
In Figure 20, the performance of the proposed jamming detection algorithm has been compared
with kurhila et.al [35] algorithm. The fixed threshold and 𝑆𝐽𝑅 have been considered TH =
−85 dBm and SJR = 7.5 dB, respectively. The percentage of the damaged burst at 𝑆𝐽𝑅 = 7.5𝑑𝐵
in the TETRA simulated receiver is equal to 52%. It can be found that the proposed algorithm
outperformed the kurhila et al. algorithm, especially in low𝑆𝑁𝑅𝑠. In addition, the impact of the
optimized limits can be implied?.

37
Figure 20 comparison of the performance of the proposed jamming detection algorithm
6. CONCLUSIONS
In this paper, a level crossing based algorithm for Doppler shift estimation is improved and used
to enhance the performance of the proposed jamming detection algorithm. The proposed jamming
detection algorithm is modelled in a TETRA simulated base station receiver. In order to reduce
the implementation complexity, and to improve the performance and enhance the efficiency of
the jamming detection algorithm in comparison with reference models [35-36], several
improvements are made. In the proposed algorithm, the modulation detection process is only
applied to the distorted bursts. Also, by using the proposed Doppler shift estimation algorithm,
the scaling factor values with high accuracy is estimated. In addition, the high and low thresholds
are calculated by several experiences. It is observed in our simulation results that the proper
values of these thresholds have a considerable impact on the performance of the system. It is
shown that the performance of the improved Doppler shift estimation algorithm in moderate
SNRs (i.e., SNR=5 dB) for TETRA users is in good agreement with other published results. The
application of the proposed Doppler shift estimation algorithm is modelled and simulation results
exhibit a noticeable improvement in the presence of a wide range of velocities and jammers.
The main limitation of deploying the algorithm is the lack of a real platform for experimental
simulations. Then, the next steps to demonstrate the applicability of our proposed framework
would be to build it in hardware, so as to be able to investigate its performance and energy
efficiency under real-life conditions instead of relying on simulations and assumptions. It would
also be interesting to consider neural networks in training BTS in order to achieve considerable
performance enhancements in terms of jamming detection and missed detection of the destroyed
signals.

38
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
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