Coding and ANN - assisted Pseudo - Noise Sequence Generator for DS / FH Spread Spectrum Modulation for Wireless Channels
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Coding and ANN - assisted Pseudo - Noise Sequence Generator for DS / FH Spread Spectrum Modulation for Wireless Channels

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One of the challenging issues in Spread-Spectrum ...

One of the challenging issues in Spread-Spectrum
Modulation (SSM) is the design of the Pseudo - Random or
Pseudo - Noise (PN) sequence generator as an option to the
already available methods. This work is related to the use of
Artificial Neural Network (ANN) for generation of the PN
sequence during transmission and reception of a SSM based
system. The benefit of the ANN - assisted PN generator shall
be that it will simplify the design process of the PN - generator
and yet provide high reliability against disruptions due to
intentional disruptions and degradation of signal quality
resulting out of variations in channel condition. The
performance of the SSM system can be further enhanced by
the use of coding. Hamming and cyclic redundancy check
(CRC) codes have been used here with the data stream to
explore if performance of the SSM system is improved further.

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Coding and ANN - assisted Pseudo - Noise Sequence Generator for DS / FH Spread Spectrum Modulation for Wireless Channels Coding and ANN - assisted Pseudo - Noise Sequence Generator for DS / FH Spread Spectrum Modulation for Wireless Channels Document Transcript

  • ACEEE Int. J. on Communication, Vol. 02, No. 02, July 2011 Coding and ANN - assisted Pseudo - Noise Sequence Generator for DS / FH Spread Spectrum Modulation for Wireless Channels Juthika Gogoi and Kandarpa Kumar Sarma Department of Electronics and Communication Engineering, Tezpur University, Tezpur, India Email: juthikagogoi@gmail.com Department of Electronics Communication and Technology, Gauhati University, Guwahati, India Email: kandarpaks@gmail.comAbstract—One of the challenging issues in Spread-SpectrumModulation (SSM) is the design of the Pseudo - Random orPseudo - Noise (PN) sequence generator as an option to thealready available methods. This work is related to the use ofArtificial Neural Network (ANN) for generation of the PNsequence during transmission and reception of a SSM basedsystem. The benefit of the ANN - assisted PN generator shallbe that it will simplify the design process of the PN - generatorand yet provide high reliability against disruptions due tointentional disruptions and degradation of signal qualityresulting out of variations in channel condition. The Figure 1 : Direct sequence spread spectrum systemperformance of the SSM system can be further enhanced bythe use of coding. Hamming and cyclic redundancy check(CRC) codes have been used here with the data stream toexplore if performance of the SSM system is improved further.Index Terms—SSM, PN sequence, ANN, Rayleigh, Rician I. INTRODUCTION Spread Spectrum Modulation (SSM) is a transmissiontechnique in which a Pseudo-Noise (PN) code independentof the information data is used as a modulation waveform tospread the signal energy over a bandwidth much greater than Figure 2 : FHSS transmitter and receiverthe signal information bandwidth. This technique decreasesthe potential interference to other receivers while achieving II. SYSTEM MODELprivacy [1]. For the SSM system performance, the processinggain of the spreading code is one of the major concerns. The The system model is depicted in Fig. 3. It is constitutedprocessing gain depends on the code length of the spreading by a SSM transmitter with a PN sequence generator formedcode. The larger the processing gain, the better is the system by an ANN. The receiver is also assisted by an ANN. Theperformance [2]. This is dependent on the PN sequence to be ANN is trained to generate the unique PN sequences whichused for the SSM. There are several approaches to how a PN are used for transmission and reception of specific datasequence can be generated, but this work considers a PN blocks. The core of the system is the ANN. The most importantsequence generator assisted by Artificial Neural Network difference with respect to classical information processing(ANN) for transmission and reception. A direct sequence techniques is that ANNs are not mathematically programmed,(DS) spread spectrum signal is generated by the direct mixing but are trained with examples [7]. The system model relatedof the incoming data with a spreading waveform before the to this work is depicted in Fig. 3 and has the components asfinal carrier modulation. But in frequency hopping (FH) the described below.spectrum of a data-modulated carrier is widened by changing A. ANN Formationthe carrier frequency in a pseudo-random manner over asequence of frequencies called the frequency hopping pattern A feed-forward ANN is created with parameters as in Table[3][4][5][6]. A generic SSM setup with both DS and FH I. It is known as Multi-Layer Perceptron (MLP) (Fig. 4). Theapproaches is depicted in the Figs. 1 and 2. equation for output in a MLP with one hidden layer is given as [8]: 26© 2011 ACEEEDOI: 01.IJCOM.02.02.180
  • ACEEE Int. J. on Communication, Vol. 02, No. 02, July 2011 Figure 3 : System Model FIGURE 4 : MULTI-LAYER PERCEPTRON TABLE I : NETWORK PARAMETERS (1)where βi is the bias value and wi weight value between the ithhidden neuron. The process of adjusting the weights andbiases of the MLP is known as training. Training the MLP isdone in two broad passes - one a forward pass and the othera backward calculation with error determination andconnecting weight updating in between. The output fromthe hidden layer is obtained depending upon the choice ofthe activation function. The values of the hidden nodes are : L h h net mj  w ji p mi   jh (2) where η is the learning rate. One cycle through the complete i 1 training set forms one epochs. The above is repeated tillThe values of the of output node can be obtained as : MSE meets the performance criteria.This cycle constitutes o o mk  f ko net mj h   (3) the learning phase of the MLP [8].Forward Computation: The errors can be computed as : B. Training and Testing e jn  d jn  o jn (4) ANNs can generalize and correctly classify inputs The mean square error (MSE) is calculated as : unknown to them previously. A training set is used to help the ANN learn which is reflected by a continuous update of M L network weights and biases, and after training, each network   e2 jn go through a testing procedure to gather data for evaluation MSE  j 1 n 1 (5) of its usefulness. One set of data is created for training the 2M ANN. The data set consists of DPSK modulated signals alongError terms for the output layer are : with ANN-assisted PN sequence. The DPSK modulated signal has a signal-to-noise (SNR) variation between -10 to 30 dB which makes 41 sets with signal variations. Further there areError terms for the hidden layer : variations like Gaussian and fading (Rayleigh and Rician) channels. This way a total of 123 signalling conditions are obtained. Of each signalling condition atleast 10 sets areWeight Update : Between the output and hidden layers taken as samples to make the training robust. Similarly a testing set is created to validate the training. This set of data consists of data bits whose length is equal to the PN sequence and is presented to each ANN. Network specific 27© 2011 ACEEEDOI: 01.IJCOM.02.02.180
  • ACEEE Int. J. on Communication, Vol. 02, No. 02, July 2011 TABLE II : PARAMETERS OF THE PN SEQUENCE FOR THE DSSSM SYSTEM TABLE III : PARAMETERS OF T HE PN SEQUENCE FOR THE FHSSM SYSTEMprocedures are then used to compare the output of each ANNagainst the desired outputs. The training time is a parameter E. System designthat is independent of the test data and is the number of With reference to Fig. 3 the PN sequence used in theseconds spent training a particular network. analysis of the DSSSM system has the parameters as shown in Table II. In both DS and FH systems a 32 bit binary streamC. Evaluation is taken as the message signal. The data in this signal can Running an ANN simulation produces a matrix of actual take two amplitudes, where one amplitude corresponds tonetwork outputs. These values can be compared to a target logical 1 and the other amplitude to logical 0. This data streammatrix of desired ANN outputs to evaluate the performance is multiplied with a cosine function of a higher frequency. Inof each network. The input layer consists of a few number of fact, this modulation can be considered as the primarynodes equal to the length of the PN sequence. The output modulation. Mathematically,layer has a size equal to the number of bits to be included in (9)the ANN generated PN sequence. The performance function such that the pre-modulated signal m(t) is given asis the least mean of squared error (LMSE) which minimizesthe average of the squared network errors. Once all the inputs (10)have been presented, the training algorithm modifies the where f = f1 and f = f2 are the two frequencies for s(t) = 1 andweights according to its procedure. The total number of s(t) = -1 respectively taken such that these frequencies lieepochs for each ANN is around 5000. The training of the within the spread spectrum bandwidth.ANN is carried out using four different training methods. For simplicity, it has been assumed that all amplitudes areThese are equal to one. After the primary modulation, the signal is  Backpropagation with gradient descent (BPGD). modulated again, to spread its frequency spectrum. This  Backpropagation with gradient descent and modulation is the multiplication of the signal m(t) with a digital momentum (BPGDM). signal of larger frequency range. The transmitted spread  Backpropagation with gradient descent and adaptive spectrum signal is given by learning rate (BPGDA). (11)  Backpropagation with gradient descent, momentum and adaptive learning rate (BPGDX). where c(t) is the PN sequence.Out of these four training methods, BPGD turns out to be At the transmitter, binary symbols are transmitted with energymost efficient in terms of training time, accuracy and number E per symbol. The noise n(t) added to this transmitted signalof epochs required to reach the desired goal. has an effect only on the amplitude. It depends on the signal- to-noise ratio (SNR). Hence the signal transmitted throughD. Hamming and CRC coding the channel can be represented by the following equation Hamming code is a linear error-correcting code which can (12)detect up to two simultaneous bit errors, and correct single-bit errors. Thus reliable communication is possible when the To recreate data at the receiver, the signal is despreaded firstHamming distance between the transmitted and received bit and then normally demodulated. The despreading can onlypatterns is less than or equal to one. By contrast, the simple be successful if the code sequence generated in the receiverparity code cannot correct errors, and can only detect an odd is exactly synchronised with that of the received signal. Thenumber of errors [9]. A cyclic redundancy check (CRC) is an parameters of the PN sequence used for the analysis of theerror-detecting code designed to detect accidental changes FHSSM system are given in Table III. With each of the signalto raw data, based on the theory of cyclic error-correcting sets considered, a unique PN sequence is generated by thecodes. CRCs are so called because the check (data ANN. There is a one-to-one correspondence between theverification) code is a redundancy (it adds zero information signal to be transmitted and the PN sequence generated.to the message) and the algorithm is based on cyclic codes This correspondence is used at the receiver for recovery of[9]. the data. The ANN for a given input of received signal provides the unique PN code. The precision of the PN code can be varied with better ANN configuration and training. 28© 2011 ACEEEDOI: 01.IJCOM.02.02.180
  • ACEEE Int. J. on Communication, Vol. 02, No. 02, July 2011 TABLE IV : B ER FOR DIFFERENT VALUES O F T HE JAMMING FACTOR OF TABLE VII : B ER FOR DIFFERENT VALUES O F T HE JAMMING FACTOR OF DSSSM SYSTEM DSSSM SYSTEM SYMBOLFOR HAMMING AND CRC CODE INFORMATION SEQUENCE TABLE V : BER FOR D IFFERENT HOPS O F T HE PN SEQUENCE OF FHSSM SYSTEM TABLE VIII : BER FOR D IFFERENT HOPS O F T HE PN SEQUENCE OF FHSSM SYSTEM SYMBOLFOR HAMMING AND CRC CODE INFORMATION SEQUENCETABLE VI : BER FOR DIFFERENT VALUES O F T HE JAMMING FACTOR OF FHSSM SYSTEM AT 2 HOPS/SYMBOL III. RESULTS AND DISCUSSION Based on the data generated by simulation of DSSSM and FHSSM systems, relationship using DPSK modulation technique between BER as a function of the jamming factor and number of hops is obtained using the PN sequence Tables IV – VI. Table IV shows that with the increase in the jamming factor from 0.1 to 1.0, the BER decreases from 0.0366 to 0.0216The entire system is trained and tested in a setup closely for the PN sequence generated using shift registers while therelated to an environment where the SSM operates. The BER decreases from 0.0307 to 0.0186 for the PN sequenceperformance of the spread spectrum system in presence of generated using ANNs. Table V shows that in case of thepartial band noise jammer is analysed for different fractions FHSSM system using the PN sequence generated using shiftof the spread spectrum bandwidth. A jammer is considered registers, as the number of hops is increased from 2 hops/that transmits noise over a fraction of the total spread symbol to 10 hops/symbol, the BER is 0.0295 and 0.0250spectrum bandwidth. The jammer signal is a Gaussian noise respectively and 0.0286 and 0.0249 respectively for the PNwith a flat power spectral density over the jammed bandwidth. sequence generated using ANNs. Table VI shows that inThe jammer spreads noise of the total power J over some case of the FHSSM system, keeping the hop rate at 2 hops/frequency range of bandwidth Wj, which is a subset of the symbol, as the jamming factor is increased from 0.1 to 1.0,total spread spectrum bandwidth Wss. It is assumed that the BER of 0.0295 and 0.0233 respectively is generated using theshifts in the jammed band coincide with carrier hop transitions. PN sequence generated using shift registers while the BER isIt is experimentally found that when the fraction of the 0.0286 and 0.0223 in case of the PN sequence generated usingjamming bandwidth is decreased, the probability of the system ANNs. Based on the data generated by simulation of thebeing jammed is decreased but the jammed signals suffer a DSSSM and FHSSM systems, relationship using DPSKhigher error rate, resulting in a degradation in the performance modulation technique between BER as a function of theof the system and vice-versa. The error rate is also calculated jamming factor and number of hops is obtained in Tablesby varying the number of hops per data symbol. VII–IX for both Hamming and CRC coded information 29© 2011 ACEEEDOI: 01.IJCOM.02.02.180
  • ACEEE Int. J. on Communication, Vol. 02, No. 02, July 2011sequence. Table VII shows that with the increase in the TABLE IX : BER FOR DIFFERENT VALUES O F T HE JAMMING FACTOR OF FHSSM SYSTEM AT 2 HOPS/SYMBOLFOR HAMMING AND CRC CODE INFORMATIONjamming factor from 0.1 to 1.0, the BER goes down from 0.0265 SEQUENCEto 0.0169 for the PN sequence generated using shift registers.The corresponding BER is 0.0240 at jamming factor 0.1 and0.0156 at jamming factor of 1for the PN sequence generatedusing ANNs for the Hamming coded sequence. The BER is0.0299 at jamming factor 0.1 and 0.0183 at jamming factor of 1for the PN sequence generated using shift registers. For theCRC coded sequence the BER is 1x10-4 at jamming factor 0.1and 1x10-4 at jamming factor of 1 for the PN sequence generatedusing ANNs. Table VIII shows that in case of the FHSSMsystem using the PN sequence generated using shift registers,as the number of hops is increased from 2 hops/symbol to 10hops/symbol, the BER is 0.0251 and 0.0246 respectively and0.0242 and 0.0240 respectively for the PN sequence generatedusing ANNs for both the Hamming coded sequence and theCRC coded sequence. Table IX shows that in case of theFHSSM system, keeping the hop rate at 2 hops/symbol, asthe jamming factor is increased from 0.1 to 1.0, BER of 0.0265and 0.0169 respectively is generated using the PN sequencegenerated using shift registers while the BER is 0.0240 and0.0156 in case of the PN sequence generated using ANNs for REFERENCESthe Hamming coded sequence while the BER is 0.0299 and0.0183 for the PN sequence generated using shift registers [1] G. Ponuratinam, B. Patel and S. S. R. K. M. Elleithy,and 1x10-4 in case of the PN sequence generated using ANNs “Improvement in the Spread Spectrum System in DSSS, FHSS, and CDMA,” Computer Science and Engineering Department,for the CRC coded sequence. From the above tables it is University of Bridgeport, Bridgeport, CT 06605.seen that when the information sequence is coded using [2] M. L. Wu, K. A. Wen and C. W. Huang, “EfficientHamming and CRC coding the BER has reduced though the Pseudonoise Code Design for Spread Spectrum Wirelessuse of coding bits leads to some waste in bandwidth. The Communication Systems”. IEEE Transactions on Circuits andBER with CRC coding has fallen to 4x10-4 and 10-4 in Rayleigh systems II: Analog and Digital Signal Processing, Vol. 48, No. 6,fading channels without ANN and Rayleigh fading channel June 2001.with ANN respectively over the SNR range -10dB to 30 dB. It [3] S. Haykin, “Digital Communications,” John Wiley and Sonsrepresents 13% and 14% improvement compared to uncoded Inc, United Kingdom, 2010.forms. Thus the use of ANN-assisted PN sequence [4] S. Zoican,”Frequency Hopping Spread Spectrum Technique for Wireless Communication Systems,” IEEE Transactions ongeneration and coding is justified for application in DS/FH Communications, 1998.SSM in wireless channels. [5] T. S. D. Tsui and T. G. Clarkson, “Spread Spectrum Communication Techniques,” Electronics and Communication IV. CONCLUSION Engineering Journal, pp 3-6, February 1994. [6] G. J. R. Povey and P. M. Grant, “Simplified matched filter The work shows the effectiveness of the use of ANN for receiver designs for spread spectrum communications applications,”PN sequence generator and coding for both DS and FH SSM Electronics and Communication Engineering journal, April 1993.systems. The extracted results show that the ANN assisted [7] P. de Bruyne, 0. Kjelsen and 0. Sacroug, “Spread SpectrumPN sequence generator helps in providing superior BER Digital Signal Synchronization using Neural Networks,” IEEEvalues in both Gaussian and Multipath faded channels. The Transactions on Communications, 1992.work also demonstrates that performance improves further [8] S. Haykin: Neural Networks: A Comprehensive Foundationwith the use of coding. The combination proves to be (2nd Edition), Pearson Education, New Delhi, 1998.effective in wireless channels. The ANN assisted PN [9] W. E. Ryan and S. Lin, Channel Codes: Classical and Modern,sequence is robust enough to prevent false triggering which Cambridge University Press, New York, 2009.also helps it to provide better performance in wirelesschannels. Thus, the system proposed can prove to beeffective and enhance performance of DS/FH SSMtransmission. 30© 2011 ACEEEDOI: 01.IJCOM.02.02.180