Simulation of Anti-Jamming Technology in Frequency-Hopping Communication System

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1 , pp Simulation of Anti-Jamming Technology in Frequency-Hopping Communication System Bing Zhao, Lei Xin, Xiaojie Xu and Qun Ding Electronic Engineering, Heilongjiang University, Xuefu No. 7, Harbin, Heilongjiang, China Abstract. Frequency-hopping communication is widely used in the field of military communication, which has the capability to resist interference. In frequency-hopping system, narrowband interference signals exist in partial dehopping frequency-hopping signals, equivalent to time-varying interference signals existed in the frequency-hopping signals. Through integrating the diversity combining with singular value decomposition method, this paper detects the frequency-hopping points and then combines with undisturbed frequency-hopping signals. MATLAB simulation shows that narrowband interference is well restrained and the system BER is obviously improved. Keywords: Frequency-hopping, Diversity, Singular Value Decomposition, Narrowband Interference Introduction Frequency-hopping (FH) communication can reach a fairly wide bandwidth and possess the performance of anti-interference, but when the intensity of interference signals far exceeds the anti-interference ability of FH communication system itself, the receiver will fail to receive data precisely. Therefore, scientists have proposed the interference suppression method to further enhance the anti-interference ability of frequency-hopping system [-3].Fast frequency-hopping (FFH) communication has a strong ability of anti-interference, but when the system requirement is very high, it will be hard for single frequency-hopping communication system to reach the performance requirements. From the view of anti-interference, the larger number of frequency hopping, the interference smaller; but unduly large number of frequency hopping will make the system structure relatively complex. In this way, if the delay of multi-path signals is very large, the collision of frequency will occur within specific time intervals, thus leading to error code [4, 5].Singular value decomposition (SVD) method has been widely used in spectrum estimation. The analysis of the singular value of received signals in autocorrelation matrix shows that the energy of interference signals is distributed in the singular value with large numerical value [6]. ISSN: ASTL Copyright 206 SERSC

2 This paper adopts the diversity combining property of fast frequency-hopping communication system and adds in the SVD method. 2 Fast Frequency-Hopping System Fast frequency-hopping (FFH) refers to a hopping scenario in which multiple frequencies exist in the intervals of each modulation code. FFH technique boasts relatively good performance of anti-interference for its diversity combining feature. Figure is the system structure diagram of FFH. signal input modulation frequency hopping synthesizer channel frequency hopping synthesizer demodulation output frequencyhopping pattern partial frequency band interference frequencyhopping pattern Fig.. System structure diagram In figure, the frequency-hopping pattern serves as an important connection in FH communication. The carrier frequency variations are called FH pattern. Only both communication sides know the frequency-hopping pattern, which are absolutely confidential to enemies. The pseudorandom means randomness is not true, in fact there are certain rules to be followed, but it is hard for enemies to guess out the rules without knowing the pattern [7]. After going through modulation, information signals are subjected to carrier modulation. Signals become frequency-hopping signals. The receiver firstly extracts the FH synchronous signals from the delivered modulation signals to make the frequency-hopping of local pseudorandom sequence control consistent with that of the received FH signals synchronized. Then it is demodulated by using local carriers and receiving signals to obtain signals bearing information. The mathematical expression of signal at the receiver is as follows: SJ r( t) s( t) n( t) J( t) S ( t) () s(t) is useful signal, n(t) is Gaussian white noise, Jt () is interference signal, () t is the interference of other addresses in the channel. It can be expressed as: J s( t) d( t)cos(2 f t) (2) k J j j j ji i S ( t) d ( t)cos(2 f t) (3) Among which, d () t stands for the data of other users, and k stands for the number of users in the channel. j 250 Copyright 206 SERSC

3 Figure 2 shows a frequency-hopping pattern. The plane is called time-frequency domain. This domain can also be regarded as a chessboard, in which the time bands at horizontal axis and the frequency bands at vertical bands form the crisscrossed pattern. frequence (MHz) Fig. 2. FFH pattern time (seconds) 3 Singular Value Decomposition As an effective way to deal with noise, SVD technique can be used to process signals polluted by additive noises. The definition of SVD is as follows: A stands for a matrix of m n with the rank being r min( m, n) ; there is orthogonal matrix U of m m as well as orthogonal matrix V of n n such that T A UV (4) Among which, stands for the diagonal matrix of m n: 0, diag(,,, ) 2 r 0 0 = (5) The column vectors of U and V are respectively the left and right singular value vectors of A. And the, 2,, r is called the non-zero singular value. When SVD is applied into signal processing, we need to construct a matrix A of m n at first. The sampling interval is t, and m segments, used for the m lines of matrix A, are intercepted at equal length of n points from rn ( ), then the matrix of A is constructed as follows: Copyright 206 SERSC 25

4 r() r(2) r( n) r( n ) r( n 2) r(2 n) A = As W (6) r(( m ) n ) r(( m ) n 2) r( mn) Where A s stands for the matrix of signal composition,w stands for the matrix composed of noises, with R m A n s and W R mn. For the matrix A s composed of signals, the row vectors have correlation degree, and the rank of matrix is. Here, the non-zero singular value of matrix A contains the concentrated energy situation of signals and noises in sampled data. If there is no noise in signal or the SNR is high, r min( m, n) ; if there is noise in signal or the SNR ratio is low, r min( m, n). The top k relatively large singular values in nonzero singular values mainly reflect signals, while relatively small ones reflect noises. The singular values reflecting noise are set to zero, and only the top k relatively large singular values are kept. When rn ( ) only contains periodic signals with a cycle of T without noise and nt kt ( k 0,,2,3 L ), then only one non-zero singular value is obtained through SVD. Under other cases, more than one nonzero singular values are obtained. When an integral value near kt / t is assigned to M, then will be much larger than other singular values. According the diagram shown in Figure, SVD module should be added after the frequency-hopping synthesizer. 4 Simulation Result When delivering 0,000 bit of information, R b =200bit/s, and the PN code is the m sequence. Under the conditions that the hopping rate of SFH is bit/hop and the hopping rate of FFH is bit/4hop, the interference model falls into narrowband interference. Figure 3 is the curve graph of signal-to-noise ratio against bit error rate under the condition of no narrowband interference. Figure 4 is the same curve graph under the condition of narrowband interference, and the interference-to-signal ratio is 5dB. As shown in Figure 3, when the hopping rate is relatively low, the ability of interference suppression is limited; with the speeding up of hopping rate, the bit error rate declines. 252 Copyright 206 SERSC

5 0 0 bit/hop bit/2hop bit/4hop 0 - BER SNR/dB Fig. 3. Curve graph of SNR and BER under the condition of no narrowband interference 0 0 SFH FFH SVD 0 - BER SNR/dB Fig. 4. Curve graph of SNR and BER under the condition of narrowband interference Figure 4 presents the curve graph of single-frequency interference suppression performance under the method of FFH modulation. Single-frequency interference exerts random influences upon frequency hopping points. As can be seen from the graph, when the interference is relatively small, the FFH system can achieve interference suppression; when the interference is relatively large, its ability of interference suppression drops down. However, adopting SVD method can obviously lower the bit error rate, and at the same time uplift the interference suppression ability; but when the system environment is sound, the anti-interference abilities of SVD and FFH are similar. 5 Conclusion In the FFH communication system of this paper, diversity combining technique of FH is adopted to judge whether or not the FH signals are interfered with reference to SVD method; the interfered FH signals are eliminated; and the un-interfered FH signals are used for dehopping. Through system simulation, it can be seen that when there is Copyright 206 SERSC 253

6 much interference, SVD technique can be adopted to detect whether or not signals are interfered, with a view to obtaining ideal effect of interference suppression. Acknowledgments. This work is supported by Heilongjiang Provincial Education Department Science and Technology Research Project (NO ). References. Perez-Slolano, J. J., Feliei-Castell, S., Rodriguez-Hcrndndez, M. A.: Narrowband Interference Suppression in Frequency-Hopping Spread Spectrum Using Undecimated Wavelet Packet Transform. J. IEEE Transactions on Vehicular Technology. 57( 3): (2008) 2. W. M. Saad, Marsland, I.: Weighted Random Frequency Hopping in the Presence of Narrowband Interference. In: Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp IEEE Press, Krabi (203) 3. Ly-Minh-Duy Le, Kah Chan Teh, Li, K.H.: Survey on diversity combining techniques for interference suppression in fast frequency hopping systems. J. Communications, IET. 9( 2): (205) 4. Li, T., Ling, Q., Ren, J.: Spectrally Efficient Frequency Hopping System Design for Wireless Networks. In: Wireless Algorithms, Systems and Applications, pp IEEE Press, Chicago (2007) 5. Nguyen, T.T., Nguyen, H.H., Le-Ngoc, T.: Iterative interference cancellation in multiuser relaying with fast frequency-hopping modulation. J. Communications, IET. 8(5): (204) 6. Milstein, L. B.: Interference rejection techniques in spread spectrum communications. J. Proc. IEEE.76(6): (988) 7. Zhao, X., Kang. X.: The Study of the Spread Spectrum Communication System Based on Sequence Pairs. In: IEEE International Symposium on Communications and Information Technology, pp IEEE Press, (2005) 254 Copyright 206 SERSC