A Novel Method To Evaluate The Effectiveness Of Communications Jamming

IEEE 2007 International

Symposium on Microwave, Antenna, Propagation, and EMC Technologies For Wireless Communications

A Novel Method to Evaluate the Effectiveness of Communications Jamming Sha Fei Song Qizhu and Wang Junfeng 1 EMC Lab of Beijing Jiao Tong University, 100044 2 The State Radio Monitoring Center, China, 100037 e-mail: feiyoungerCg), 163.com

Yang Fei

,

,

Abstract: This paper presents a novel method to evaluate communications jamming effectiveness by using Amplitude Probability Distribution (APD), Average Crossing Rate (ACR) and Power Spectral Distribution (PSD). The three parameters give information about amplitude domain, time domain and frequency domain of a jamming signal, respectively. In earlier papers the APD parameters has been considered individually to analyze the performance degradation of communications. However, since the three parameters can give more detailed description of a jamming signal, we can connect them together with the performance of digital communications. And then according to the analysis results, we can evaluate communications jamming effectively and generate a jamming signal to accomplish communications jamming. In this paper a PHS system and pulsed sine wave jamming signals are given

as an

example.

Keywords: communications jamming, APD, NAD, PSD, pulsed jamming

I. Introduction Many researches have been done to model interference signals in order to predict the effect on a receiver and their results are greatly useful for the research of communications jamming [l]-[2]. Many different methods of measurement and detectors have been developed. Amplitude Probability Distribution (APD) and Average Crossing Rate (ACR) measurements give information about the amplitude and time statistics of envelope from the IF-filter, respectively, and Power Spectral Density (PSD) gives information about frequency domain. The use of communications jamming has greatly stimulated the interest in tactical jamming within military communications and electrical warfare. The purpose of the research is to protect communication systems from disruption and the same techniques are applicable in the study of jamming systems and in the

1-4244-1044-4/07/S25.00 ©2007 IEEE.

study of protecting communications from unintentional interference. However, the actual jamming techniques/waveforms used are outdated and not well researched. It is proposed that the various communication modulation types (analogue and digital) are investigated, and optimized jamming waveforms and techniques developed. Given all the technical details of the victim (power, modulation, etc.) and the environment (ground conductivity, path profile, etc.), we would like to know whether the jamming is successful in disrupting communication between the transmitter and the receiver. By modeling the various modulations and channel effects, APD, ACR and PSD can be developed to investigate the effect that the different jamming waveforms will have. In this paper we study communications jamming by using APD, ACR and PSD of a jamming signal, which can be designed by pseudo noise generator [3]. In section II the infrastructure of measuring methods is described. Section III gives pulsed sine wave jamming models. In section IV, we discuss communications jamming using this method and give PHS system as an example. Finally, the conclusions are given in section V.

II. The Infrastructure of MEASURING METHODS Exhaustive

explanations

about conventional types

detector, Quasi-peak measuring detectors, e.g. detector, Peak detector, Average detector, and so on, are given in CISPR standard literatures [4]. We can get the PSD of a signal from the receiver or spectrum analyzer of

rms

with these types of detectors. The PSD describes how the a time series is distributed with frequency. Mathematically, it is defined as the Fourier Transform of the autocorrelation sequence of the time series. When we study a signal, we should use appropriate detector due to our measuring purpose and the rms detector is commonly used. If the purpose is to evaluate the impact of a jamming signal on various communication systems, the power of

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Symposium on Microwave, Antenna, Propagation, and EMC Technologies For Wireless Communications

IEEE 2007 International

detector can only be used for evaluating analogue AM communication systems. So a new statistical measuring method is proposed by CISPR, which can be used to evaluate the impact of a jamming signal on digital communication systems [5]-[6]. The statistical measuring method describes the envelope statistics of a disturbance from the IF filter of the measurement receiver and the APD and ACR statistical parameters are significant ones therein. Their definitions are shown as follow. The Amplitude Probability Distribution is defined as the part of time the measured envelope of a disturbance exceeding a certain level and APD(R) is the ratio of time for which r(t) > R within measurement duration T seconds from start time tQ to end time t0+T Here r(t) is the disturbance amplitude, and an amplitude threshold R has a dimension of voltage. Figure 1 shows an example of the envelope from IF filter and the APD(R) can be written as + +1, '"- = APDiR) tx +12 +t3T (1) Moreover, the relationship between apd(R) and the rms

.

.

.

.

=

=

R

R

Envelop of Interference Signal threshold level R

THE STATISTICAL CHARACTERISTICS OF PULSED

JAMMING SIGNAL

Jamming signals can be classified on the basis of their statistical properties as stochastic or deterministic signals. In this section we are going to discuss the statistical characters of a representative type of jamming signals pulsed sine wave. Pulsed sine wave (PSW) is often used to simulate the dominant disturbance waveform in modern electronics, which can be described by three parameters: pulse repetition frequency (PRF) / pulse width Tw and carrier frequency fc. The on/off modulation of the sine wave considers the face of intermittent interference, which is shown in figure 2. ,

-pulsed repetitive

time-

Y.hiT

probability density function of the envelope is basically ~APD{R) (2) where F(R) and f(R) denote the cumulative density function and probability density function of the envelope, respectively [5].

f(R)=^-F(R)

III.

-pulsed width-

Fig.2. pulsed sine wave signal model Interference

local oscillator

filter

r(f)

spectrunr analyzer >

intermediate

frequency

APD detector

A,i r"

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Fig.3.

J Fig. 1. Envelope of Interference Signal From IF Filter The ACR(R) is the number of times that r(t) intersects r in a positive direction per second within the measurement duration. It can be defined as

ACRiR) n{R)

(3)

--

where n(R) denotes the number that envelope r(t) intersects the threshold level R in a positive direction, which is also the number of impulses that exceed the threshold level R As shown in figure 1, the threshold R is positively crossed four times so that crd(R) ait .

=

EMI and APD Measurement Receiver Model

The spectrum of the pulse is moved to the carrier frequency which becomes band-limited and it can be measured according to the model in figure 3. The spectrum analysis method has been mentioned in many literatures, so in this section we will mainly discuss APD and ACR. From figure 3 we can also see that the results of spectrum and APD are affected by the bandwidth of the filter. Thereby, when we set RBW the same as the bandwidth of communication filter, we can get the similar amplitude statistics inside the communication receiver. In our research RBW is set to 300kHz, which is the same as the channel bandwidth of PHS receiver. Moreover, we keep the peak of pulsed sine waves as a fixed value and change the pulse repetition frequency (PRF), duty cycle and the bandwidth of the filter (RBW). The results of APD measurements with different PRF, duty cycle and RBW are shown in figure 4, while results

.

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IEEE 2007 International

Symposium on Microwave, Antenna, Propagation, and EMC Technologies For Wireless Communications

10

PRF=500kHz,duty cycle=50%, RBW=300kHz PRF=500kHz,duty cycle=50%, RBW=10MHz H-PRF=500kHz,duty cycle=70%, RBW=10MHz Z^. PRF=100kHz,duty cycle=50%, RBW=300kHz -PRF=100kHz,duty cycle=50%, RBW=10MHz O.

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PRF=500kHz,duty cycle=50%,RBW=300kHz PRF=500kHz,duty cycle=70%,RBW=300kHz H-PRF=500kHz,duty cycle=50%,RBW=10MHz ~^~ PRF=100kHz,duty cycle=50%,RBW=300kHz

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disturbance level (dBuV)

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Fig.4. comparison of APD measurements

45

communications jamming effectiveness. In section IV, will introduce the relationship between the statistical characters of jamming signal and quality degradation of communication system. Moreover, we can generates a pseudo noise according to specified APD and ACR determined by equation (5) and (6) [3]. we

APD(R) APDR(fp9Wp9Bf) (4) ACRiR) ACRRifp,JVp,Bf) (5) where / is PRF, W is duty cycle and Bf represents BF. Figure 4 shows that when the RBW
IV. EVALUATE COMMUNICATIONS

=

jamming signals inside the communication receiver will lose their characteristics of pulse (straight line together with waterfall region) and the jamming energy is smoothed equally over all bits of the victim and the interference caused by pulsed jamming signals can approximately be seen as Gaussian distributed. While RBW>PRF, the jamming signal cannot be seen as Gaussian distributed and the victim perceives a pulsed jamming. Figure 4 also shows that the pulsed jamming signal with 70% duty cycle has more average power than that with 50% duty cycle. From figure 5 we can also see that when RBW
40

Fig.5. comparison of ACR measurements

of ACR are given in figure 5. From these results we can see that APD and ACR are different with the PRF, duty cycle and (RBW), and we can assume the models as follows:

a

35

disturbance level (dBuV)

According to the analysis above, we can make a clear description of pulsed signals by using APD, ACR and PSD. So when we generate a jamming signal, we can get its statistical characters firstly, and then evaluate the

JAMMING BY USING THIS METHOD In the previous section we have got the statistical models. In this section we will try to combine the two models together with PSD, and establish the relationship between them and the degradation of PHS communication. At first we can get the spectrum of the jamming signal from PSD, which include the information about frequency range and signal intensity. Then we can make APD and ACR measurements at the center of frequency range. In our research the carrier frequency of pulsed sine wave is 1906.85MHz, which is center frequency of 40th channel in a PHS system, and the APD and ACR measurement results of pulsed jamming signals with different PRF are shown in figure 6 and figure 8 respectively. The duty cycle is set to be 50% and the RBW is 300kHz which is the same with the channel bandwidth of PHS system. Figure 7 gives the results of bit error rate (BER) measurement under the pulsed jamming signals. From figure 7 we can see that pulsed jamming signals with lower PRF are more effective than those with higher PRF. However, when the PRF exceed 500kHz, which is higher than 300kHz the jamming results are very similar. Furthermore, the same trend can be seen in figure 6. So we can evaluate the jamming

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IEEE 2007 International

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Symposium on Microwave, Antenna, Propagation, and EMC Technologies For Wireless Communications effectiveness by using APD. For our TDMA system the ACR of jamming signal is another significant parameter. Figure 8 shows that the jamming signals with smaller ACR can cause more performance degradation than those with larger one. It is because smaller ACR denotes the jamming signals have more probability to impact on the using slots. So we proposed a evaluating model for

=

pulsed jamming:

Pb =APD(R)xP(bit error\jamning>R, ACR(R)) (6) +(l-APD(R))xP(bit error\janmngR, ACR(R)) denotes the BER under the condition that larger than R and ACR(R).

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Fig.6.

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APD measurement results

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Fig.7.

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pulsed jamming signals

to introduce

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and PHS novel method to effectiveness and

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of disturbancesaccording to its effect on digital communication services," Electromagnetic Compatibility, IEEE Transactions. NO. 4, NOVEMBER 2000. [2] Lee, S. H.; "Jamming effects on digital communication receivers (timing errors and frequency errors)," M.S. Thesis Naval Postgraduate School, Monterey, CA. December 1985. [3] Yamane, K.; Shinozuka, T.; Ohnuma, K. "Pseudo-noise generator with arbitrary APD, PDD and PSD," Electromagnetic Compatibility, 2000. IEEE International Symposium on Volume 1, 21-25 Aug. 2000 Page(s):471 476 vol.1 [4] CISPR 16-1-1 Ed.2: "Specification for radio disburbance and immunity measuring apparatus and methods Part l-l: Radio disturbance and immunity measuringapparatus

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signal level (dBuV) BER measurement of PHS

examples

References [1] Stenumgaard, P.F.; "Using the root-mean-square detector for

l| 111 ll li.bd

50 60

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evaluate communications jamming accomplish communications jamming. According to our research the statistical characteristics of jamming signals can be used together with the PSD. Our further research is to get the accurate prediction of degradation of digital communications by using statistical method and accomplish more effective communications jamming.

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PRF=lMHz PRF=500kHz PRF=300kHz PRF=200kHz PRF=100kHz PRF=10kHz PRF=lkHz

level is

V. CONCLUSIONS

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disturbance level (dBuv)

jamming signal

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system

Measurement apparatus".

[5] K. Wiklundh; "A Method To Determine The Impact From

Disturbing Electrical Equipment On Digital Communication System By Using APD," Proc. on EMC Europe 2002. Sorrento September 2002, pp. 93-97. [6] M. Uchino, Y. Hayashi, T. Shinozuka, R. Sato; "Development of low-cost highresolution APD measuring equipment," Proc. on 1997 Int. Symp. on EMC, Beijing, May, 1997, pp 253-256. M Schmidt, H Jakel, F Jondral, "Influence of the amplitude [7] distribution to the interference of UWB signals on radio receivers," Global Telecommunications Conference, GLOBECOM'05.

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Fig. 8.

25

30

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disturbance (dBuV)

40

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50

55

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ACR measurement results

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2005.