An efficient impulsive noise cancellation scheme for power-line communication systems using ANFIS and chaotic interleaver

Accepted Manuscript An efficient impulsive noise cancellation scheme for power-line communication systems using ANFIS and chaotic interleaver

Yassine Himeur, Abdelkrim Boukabou

PII: DOI: Reference:

S1051-2004(17)30065-9 http://dx.doi.org/10.1016/j.dsp.2017.04.005 YDSPR 2101

To appear in:

Digital Signal Processing

Please cite this article in press as: Y. Himeur, A. Boukabou, An efficient impulsive noise cancellation scheme for power-line communication systems using ANFIS and chaotic interleaver, Digit. Signal Process. (2017), http://dx.doi.org/10.1016/j.dsp.2017.04.005

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Highlights • • • • •

Data transmission over Powerline communication (PLC). PLC channel and impulsive noise models. Adaptive noise cancellation using ANFIS. Security enhancement using chaotic interleaving. Broadband and Narrowband PLC systems.

Digital Signal Processing Digital Signal Processing 00 (2017) 1–24

An efficient impulsive noise cancellation scheme for power-line communication systems using ANFIS and chaotic interleaver Yassine HIMEUR1 , Abdelkrim BOUKABOU 2

Abstract Impulsive noise is one of the main disturbances that damage the data transmission over power-line communication (PLC) systems. This paper presents an adaptive noise cancellation approach based on the adaptive neuro-fuzzy inference system (ANFIS) and a chaotic interleaver, namely ANC-CI-ANFIS scheme for impulsive noise estimation and suppression from the OFDM PLC channel. The ANFIS is based on a hybrid learning algorithm to identify parameters of Sugeno-type fuzzy inference system. Accordingly, fuzzy membership function parameters are trained using a combination of both least-square and back propagation gradient descent algorithms to emulate a given training data set. Furthermore, transmitted data are managed with a chaotic interleaver to secure data transmission and give more robustness against impulsive bursts. Simulation results are carried out on an OFDM PLC transmission chain compatible with the HomePlug AV standard under different impulsive noise scenarios. The results demonstrated the scheme’s ability to detect and remove the impulsive noise from the PLC channel while keeping a high security level by using the chaotic interleaver. The major advantage of this system is its ease of implementation and faster convergence rate. c 2011 Published by Elsevier Ltd.  Keywords: Powerline communication, impulsive noise, ANFIS, adaptive noise cancellation, chaotic interleaver.

1. Introduction In recent years, the increase demand for multimedia applications networking has led to the consideration of various communication systems for transmitting broadband data over wired and wireless networks. In particular, communication using power-line network known as power-line communication (PLC) is a technology aimed at transforming the electric power network into a communication highway, thereby applying communication based theories to power-line network. Hence, data can be transmitted to every device connected to the power-line network using existing wiring to provide data communication access. The concept of PLC is simple; it conveys data signals over electric cables by superimposing high frequency (data) signals over electricity signal (50/60Hz) by transforming the power-line to dual purpose highway for electricity and communication signals [1]. However, due to the nature of the power-line channel, it presents unfavorable channel properties characterized by noise and high data attenuation. In fact, the impulsive noise can affect a large amount of data during transmission by producing high level of bit error rates (BER) and therefore, degrades considerably the performance of the PLC channel. The duration of this impulsive noise varies 1 Corresponding author. Department of Electronics, University of MSB Jijel, BP 98 Ouled Aissa 18000, Jijel, Algeria TELECOM Division, Centre de D´eveloppement des Technologies Avanc´ees (CDTA), Baba Hassen 16303, Algiers, Algeria E-mail address:[email protected]. 2 Department of Electronics, University of MSB Jijel, BP 98 Ouled Aissa 18000, Jijel, Algeria E-mail address:[email protected].

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from some microseconds to a few milliseconds where its power spectral density (PSD) can reach values of 50 dB [2]. In the literature, many techniques are developed to eliminate the impulsive noise in the power-line network. Blanking [3, 4, 5], clipping [6] and blanking/clipping [7] nonlinearities are among the most utilized methods. In [4, 5], the author proposed an algorithm to detect and remove impulsive noise in a frequency-domain after OFDM demodulation and channel equalization. In [6], the authors developed a deep clipping approach which is characterized by two alternative parameters, namely, a threshold and a depth factor. In [7], the authors proposed an adaptive recursive noise compensator for impulsive noise mitigation over OFDM based PLC channel. We note that even if these techniques can reduce the effect of impulsive noise, they cannot give relatively good communication performance for PLC channel transmission. On the other hand, techniques based on error correcting codes are commonly used in PLC communications systems. In fact, linear error correcting codes such as low-density parity-check (LDPC) [8], LDPC-convolutional codes [9], turbo codes [10, 11] and Luby transform (LT) codes [12] are recently applied for transmitting data over the PLC channel. These techniques can give acceptable error protection capability with good communication performance at the expense of a lower transmission rate. From another side, nonlinear techniques based on fuzzy systems have been widely applied for noise reduction especially in image applications without having prior knowledge about the original image [13, 14]. In fact, data corrupted by impulsive noise generally have nonstationary statistical characteristics formed through a nonlinear system process. Consequently, fuzzy based nonlinear schemes are more powerful to impulsive noise filtering since they have a high capability to approximate any complex nonlinear function. In addition to the fuzzy systems discussed above, a number of filtering methods based on neural networks have been proposed [15]–[17]. A high performance can be obtained in noise filtering application by combining the fuzzy and neural networks which is widely termed as neurofuzzy system [18]–[24]. In particular, adaptive neuro-fuzzy inference system (ANFIS) is a neuro-fuzzy system that combines the learning capabilities of neural networks, with the functionality of fuzzy inference systems. As pointed out earlier, ANFIS have been adopted to many applications. It uses a hybrid learning algorithm to identify parameters of fuzzy inference systems. To train FIS membership function parameters and then following a given training data set, ANFIS carries out a combination of the least squares method (LSM) and the back propagation (BP) gradient descent method. Based on the bit error rate (BER) and some other performance criteria such as signal-to-noise ratio (SNR), mean square error (MSE), structural similarity index measure (SSIM) and subjective evaluation measure, it has been found that the neuro-fuzzy based techniques provide much better performance than the state-of-the-art filters. In addition, most of PLC applications require secure, reliable and high data rate communication. Chaotic techniques are a promising solution for enabling secure communication [25]–[28]. It has been demonstrated that even one dimensional discrete chaotic system is able to provide a high level of security [29]. It is well known that maintaining information security on PLC channels is a challenging task. This is due to the fact that with suitable receivers, an intruder can intercept information from PLC transmission in the local area. In addition, it is difficult to discover such interceptions. Therefore, security of PLC transmission is very important. A practical solution to this problem can be reached by using a chaotic interleaver. The use of ANFIS for impulsive noise mitigation through the PLC systems is motivated by the following reasons: Firstly, its effectiveness against different kinds of noise attacks; Secondly, parameters of the ANFIS are independent from the nature of the noise to be reduced; Thirdly, to the best of the authors’ knowledge, there is no work which discusses the use of ANFIS for noise reduction over the PLC channel. Hence, in order to preserve signal details transmitted over this channel and avoid low transmission rate, we propose an adaptive noise cancellation scheme based on chaotic interleaving and ANFIS, namely ANC-CI-ANFIS, to detect and remove impulsive noise from corrupted transmitted signals and ensures a secure transmission over the PLC channel. In ANC-CI-ANFIS, we combine some of the essential characteristics of ANFIS such as the learning capability with ability to solicit interpretable IF-THEN rules. Furthermore, the chaotic interleaver is used to reduce the effect of impulse bursts while securing data transmission. This chaotic interleaver has a low implementation complexity with few parameters to be transferred. Performance analysis are provided to demonstrate the effectiveness of the proposed scheme in impulsive noise removing from OFDM PLC transmitted data while ensuring a high level of secure communication. The rest of this paper is organized as follows. Description of the PLC system including PLC channel model and the impulsive noise model is presented in Section 2. The PLC system used in the simulation is described in Section 3. The proposed ANC-CI-ANFIS scheme is detailed in sections 4 and 5. In Section 6 we present several simulation results along with the performance analysis of the proposed schemes. Concluding remarks are given in Section 7. 2

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Figure 1. Simplified structure of PLC network used in bottom-up channel modeling.

2. PLC System Description 2.1. PLC channel model Basically, there are two types of PLC channel models: the bottom-up model [30] and the top-down model [31]. In this paper, we are interested with the first model which is based on the transmission line theory. This channel model requires a perfect knowledge of the targeting power-line network including its topology, the used cables and load impedances of terminals. The network elements are composed of seven cables of lengths Li (i ∈ {1, 2, 3, 4}), Si (i ∈ {1, 2, 3}), and five terminal units Zi (i ∈ {1, 2, 3}), ZG , ZL . The units Z1 , Z2 , Z3 represent the load impedances used in the network, and ZG , ZL represent the impedances of the transmitter and receiver, respectively. Fig. 1 illustrates a simplified structure of a PLC network used in bottom-up channel modeling. Clearly, the transmitter and receiver are linked together, and the relation between the two sides can be determined based on ABCD matrix rules and a two-port network arrangement. According to the description in [30], the frequency response of the PLC channel model is determined using the following equation:   ZC  H( f ) =  (1) AZC + B + CZC ZG + DZG  where A, B, C, and D are frequency dependent coefficients, calculated from the secondary parameters: characteristic impedance ZC , propagation constant γ and cable length l, as follows     cosh(γl) ZC sinh(γl) A B (2) = 1 cosh(γl) C D ZC sinh(γl) PLC systems can be divided into two main classes: the broadband PLC (BB-PLC) systems and the narrowband PLC (NB-PLC) systems. BB-PLC systems operate in the frequency band 1.8–250 MHz with typical SNR ranging from 0 dB to 20 dB [2, 32]. This class of PLC systems is employed for ‘last mile’ communications between smart meters and data concentrators, which are arranged by local utilities on medium-voltage (in the US) or low-voltage (in Europe) power lines. This technology has gained particular attention in the last few years since it has ability to provide data rates of more than 200 Mbit/s, making it easy to fulfill the users’ home entertainment needs including high definition television (HDTV) [33]. On the other hand, NB-PLC systems operate in the frequency band 3–500 KHz and the regular SNR values range from -5 dB to 10 dB [6, 32, 34]. This class furnishes home area networks that interconnect smart appliances with smart meters for energy consumption profiling and automatic control. Recent wellknown automation and control systems are being integrated into upcoming smart home applications, communications, and security [35, 36]. 3

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2.2. Impulsive noise model Impulsive noise is a serious source of degradation when transmitting data over the PLC channel. Serious efforts have been made in order to find the time and frequency characteristics of the impulsive noise in PLC channel in order to prevent severe signal degradations [34, 37]. Some models characterize only the probability density function (PDF) of the noise amplitude, whereas others consider the time correlation of impulsive events. Interference generated by this type of noise can be modeled at the receiver by Middleton class A distributions [38] or Poisson-Gauss processes [39, 40]. However, Middleton’s class A model may not be the most suitable candidate to describe impulsive noise in the PLC environment since it is originally designed for man-made interferences [41]. For a block length of N symbols representing the data sequence, the total noise uk (k = 0, 1, . . . , N − 1) in a PLC channel may be expressed as uk = wk + ik , (3) where wk is the additive white Gaussian noise (AWGN) which represents the background noise and ik is the impulsive noise given by (4) ik = bk · gk , whereas gk is a complex WGN with mean zero, and bk is the Poisson process with probability mass function  p, bk = 1, Pr (bk ) = 0, bk = 0,

(5)

where p represents the impulsive bursts probability of occurrence. Therefore, the total noise generated over the PLC channel can be expressed as (6) uk = wk + bk · gk . The PDF of the total noise P(uk ) is given by [40, 42]: P(uk ) = (1 − p) · G(uk , 0, σ2w ) + p · G(uk , 0, σ2w + σ2i )

(7)

where σ2u and σ2i are the variances of the AWGN and the impulsive noise, respectively, and G(·) is the Gaussian process given by 1 exp(−(x − μ)2 /2σ2x )), (8) G(x, μ, σ2x ) = √ 2πσ2x with mean μ and variance σ2x . 3. Structure of the transmitter and receiver The proposed ANC-CI-ANFIS scheme is illustrated in Fig. 2. As can be seen, a data sequence d = [d0 , d1 , . . . , dN−1 ] is mapped using quadrature amplitude modulation (QAM) to get the sequence x = [x0 , x1 , . . . , xN−1 ] and passed through the inverse fast Fourier transform (IFFT) modulator to get the corresponding continuous-time signal. Then the obtained sequence feeds the chaotic interleaved in order to generate the interleaved information sequence s = [s0 , s1 , . . . , sN−1 ] . Each obtained data block is pre-appended with a cyclic prefix (CP) to mitigate the inter-symbol interference (ISI) and then passed through the digital to analog converter. The time-domain transmitted signal s(t) is expressed as s(t) =

N−1 1  xn e j2πnt/T s , 0 < t < T s , N n=0

(9)

where T s is the active symbol interval and xn is the mapped version of the data sequence. The transmitted signal passes throughout the PLC channel with impulse response h(t) derived from the frequency response in (1). In the receiver side, the time-domain received signal r(t) can be written as r(t) = h(t) ⊗ s(t) + u(t), 4

(10)

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Figure 2. Block diagram of the OFDM baseband transmission model.

where u(t) is the total noise transmitted via the PLC channel and ⊗ represents the convolution process. The received signal is passed through the CP removal block to remove the guard intervals and then transformed into the frequency-domain by means of FFT. The received signal is given in the frequency-domain by Rk = Hk S k + Uk = Hk S k + Wk + Ik , k = 0, 1, ..., N − 1,

(11)

where R = [R0 , R1 , ..., RN−1 ], H = [H0 , H1 , ..., HN−1 ], S = [S 0 , S 1 , . . . , S N−1 ], U = [U0 , U1 , . . . , U N−1 ], W = [W0 , W1 , . . . , WN−1 ] and I = [I0 , I1 , . . . , IN−1 ] are the FFTs of the received signal, the channel response, the transmitted signal, the total noise, the AWGN and the impulsive noise, respectively. The received signal is given in the time-domain by rk = IFFT (Hk S k ) + uk , k = 0, 1, ..., N − 1.

(12)

4. Chaotic interleaver design Interleaving process is commonly used in communication systems to overcome correlated channel noise. Conventional interleavers have shown some limitations when dealing with impulsive noise. In this section we will apply the chaotic 2-D logistic function to randomize the bits of the data sequence [43]. In fact, the chaotic interleaver distributes burst errors generated by the impulsive noise through different positions, which can enhance the robustness of the proposed system. Furthermore, the initial conditions, control parameters and number of iterations are used as a secret key. This adds a degree of encryption to the transmitted data. Fig. 3 illustrates the effect of chaotic interleaving on impulsive burst errors, it can easily distribute burst errors generated by the impulsive noise through different positions which can reduce their effects on the transmitted signals. Accordingly, the chaotic interleaving is performed using Algorithm 1. 5. Design of the adaptive noise cancellation scheme With the aid of the ANFIS we will implement the proposed noise cancellation algorithm to adaptively detect and remove the impulsive noise generated through the PLC channel. Hence, we built a first-order Sugeno-type fuzzy 5

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A summary of the chaotic interleaving algorithm. Step 1.Generate a chaotic sequence using the following chaotic 2-D logistic function y1 (k + 1) = α1 y1 (k) − y1 (k)3 + α2 y1 (k − 1), k = 0, . . . , N − 1.

(13)

where α1 = 1.9 and α2 = 0.5 are the system parameters. Step 2. Construct a chaotic binary sequence C = [c0 , c1 , . . . , cN−1 ] as follows:  1, i f y1 (k) > T, ck = 0, otherwise,

(14)

where T is a specific threshold. A vector Vin of linear indexes corresponding to C will be assigned. Step 3. Divide the bits in the input vector Vin = [v1 , v2 , ..., vN ] into two groups B1 = [b10 , b11 , . . . , b1N −1 ] and 2

B2 = [b20 , b21 , . . . , b2N −1 ] depending on the bits in the chaotic binary sequence C. For each bit ck in C, we check 2 the corresponding bit in Vin . If the bit is 1, we put the corresponding bit in Vin into B2 . Otherwise we put this bit into B1 . Step 4. Put Vout = C, then Steps 1 to 4 are repeated M times in order to have a flat output distribution. Finally, we get the result value in Vout by concatenating B1 and B2 . Step 5. Recover the input sequence C from the output sequence Vout , by operating in the inverse order, and we must know values of the secret key K s composed of initial conditions, control parameters and number of iterations, it can expressed as K s = [y(0), z(0), α1 , α2 , M].

Figure 3. Effect of chaotic interleaving against impulsive burst errors.

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Figure 4. ANFIS structure.

inference system using a set of rules that can be easily implemented in the receiver side to detect and remove the impulsive noise without taking into account the parameters used in the noise generation. Hence, the proposed noise cancellation is independent on the type of the noise introduced through the PLC channel. The two rules used in the ANFIS are expressed as follows [23]: Rule 1: Rule 2: Rule 3: Rule 4:

If If If If

x1 is A1 x1 is A1 x1 is A2 x1 is A2

and x2 and x2 and x2 and x2

is B1 then is B2 then is B1 then is B2 then

f11 f12 f21 f22

= = = =

p11 x1 + q11 x2 + r11 , p12 x1 + q12 x2 + r12 , p21 x1 + q21 x2 + r21 , p22 x1 + q22 x2 + r22 ,

(15)

where x1 and x2 are input variables, Ai , Bi (i = 1, 2) are fuzzy sets, pi j , qi j , ri j (i, j = 1, 2) are parameters of the output membership functions. Fig. 4 shows the schematic diagram of the ANFIS model. Accordingly, Accordingly, ANFIS is characterized by a five-layer feed forward network for the training process. For the first layer, the outputs are given by O1Ai = μAi (x1 ), O1B j = μBi (x2 ), i, j = 1, 2

(16)

where μAi and μBi are membership functions (MFs) of fuzzy sets Ai and Bi , respectively. Usually, μAi and μBi are chosen as Gaussian or triangular functions. In this paper, we choose triangle MFs expressed as follows ⎧ ⎪ 0, ri ≤ ai ⎪ ⎪ ⎪ r −a ⎪ ⎪ ⎨ bii −aii , ai ≤ ri ≤ bi (17) μ(ri ) = ⎪ ⎪ ci −ri ⎪ ⎪ ci −bi , bi ≤ ri ≤ ci ⎪ ⎪ ⎩ 0, r ≥c i

i

where {ai , bi , ci } (i = 1, 2) are the premise parameters set. The second layer is considered as the rule layer. Each node in this layer calculates the firing strength of each rule via multiplication, i.e., (18) O2i j = wi j = μAi (x1 ) × μB j (x2 ), i, j = 1, 2, where O2i j represents the output of layer 2.

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Figure 5. A general representation of the adaptive noise cancellation based ANFIS.

The layer 3 is the normalization layer for which, each neuron calculates the normalized firing strength of a given rule. The output O3i, j of this layer is given as wi j O3i j = w¯ i j = 2 2 i=1

j=1

wi j

, i, j = 1, 2,

(19)

where w¯ i j represents the normalized firing strength. Every node in layer 4 is an adaptive node determined by the following output (20) O4i j = w¯ i j fi j = w¯ i j (pi j x1 + qi j x2 + ri j ), i, j = 1, 2. Finally, the last layer is designed to calculate the summation of output of all incoming signal as follows 2 2 2  2  i=1 j=1 wi j fi j w¯ i j fi j = 2 2 . O5 = i=1 j=1 wi j i=1 j=1

(21)

To guarantee a rapid training and adaptation of the ANFIS, the hybrid learning algorithm is based on both LSM and BP algorithms. As a consequence, the ANFIS converges faster since it reduces the dimension of the search space of the BP algorithm [44]. The general block diagram of the ANC-CI-ANFIS filtering is shown in Fig. 5. As can be seen, the total noise uk is added to the transmitted signal sk and then transmitted through over the PLC channel. Hence, the proposed ANC-CI-ANFIS is applied to estimate a measurable noise uˆ k and then the corresponding noise will be suppressed from the received signal rk to estimate and restore the transmitted signal sˆk . The ANC-CIANFIS scheme is implemented as it is explained in Algorithm 2. Remark. If we consider expectations on both sides and assuming that sk (m)is not correlated with uˆ k , then E(e2k ) = E(s2k ) + E((uk − uˆ k )2 ) − 2E(sk · uˆ k ).

(26)

Furthermore, if the signal sk (n) is a random signal with zero mean, then E(sk · uˆ k ) = 0, and hence, E(e2k ) = E(s2k ) + E((uk − uˆ k )2 ),

(27)

where E(s2k ) is not affected when ANFIS is adjusted to minimize E(e2k ). A flowchart for noise cancellation using ANFIS is illustrated in Fig. 6. The flowchart gives more explanations on how the ANFIS algorithm was implemented through the PLC channel. 8

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A summary of ANC-CI-ANFIS algorithm. Step 1. Generate the impulsive noise ik using Poisson-Gauss process as described in Section 2. In realistic scenario, the impulsive noise is estimated in an offline process when no communication is occurred. Step 2. The OFDM signal sk obtained after, QAM mapping, IFFT modulation and chaotic interleaving using Algorithm 1 is transmitted through the PLC channel. Step 3. Estimate the total noise uˆ k (m) using an unknown nonlinear function as follows uˆ k (m) = (ik (m), ik (m − 1)) = α2

sin(ik (m))ik (m − 1) , 1 + [ik (m − 1)]2

(22)

where α is a parameter between 0 and 10, fixed experimentally and used to control the noise level. Note that this equation is generally used to estimate the colored noise from the white noise. However, in our work we have adapted it to estimate the total noise from the impulsive noise. Step 4. The received signal rk is the sum of the original OFDM signal sk and the noise uk , i.e., rk = sk + uk . Step 5. The noise ik is passed through the ANFIS adaptive filter to produce an estimated output uˆ k . Step 6. Estimate the received signal rˆk is derived by subtracting the estimated noise uˆ k from the time domain representation of the received signal rk as follows (23) rˆk = sk + uk − uˆ k . Step 7. Generate the frequency-domain representation of the received signal by means of FFT and then apply a frequency channel equalizer (FEQ) as follows ˆ Sˆ k = Rk × (1/H)

(24)

where Rk and Hˆ are the frequency domain representations of the received signal and the estimated channel response, respectively. Step 8. Generate the time-domain representation of the received signal by means of IFFT to obtain the estimation of the transmitted signal sˆk Step 9. Calculate the following error ˆek 2

= =

ˆrk − uˆ k 2 =  sˆk + uk − uˆ k 2 , 2 sˆk + uk − fˆ(ik , ik−1 , ik−2 , · · ·) ,

where fˆ is the function implemented by ANFIS, and then return to Step 1 until restoration of the whole signal.

9

(25)

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Figure 6. Flowchart of noise removal using ANFIS.

6. Results and discussion The simulations are carried out using an OFDM PLC transmission channel and a QPSK baseband modulation. The transmitted signal consists of 128000 samples. The AWGN is generated by a Gaussian noise with zero mean and unity variance. Further, the signal-to-AWGN ratio is fixed to 20 dB. The generated impulsive noise was implemented using different amplitudes to manage and reach different SNR values. The output SNR is given by  2 s (28) SNR = 10 log10  k2 uk where sk is the transmitted OFDM signal and uk is the total noise. Several simulations with various probabilities of impulsive noise occurrence were performed for which the probabilistic arrival rate of the impulsive noise events p was set to 1%, 10%, 20% and 30%. The impulse bursts probability term denotes the proportion of corruption of signal samples by impulsive noise. If impulse probability is equal to 10%, then it means that the 10% of the signal samples were corrupted by impulsive noise. This is required as a performance validation prior to application on a realistic scenario. Parameters used in bottom-up PLC channel model are given in Table 1 and electrical cable characteristics in Table 2. Fig. 7 shows the frequency responses of three channel scenarios known as; best case: has an average attenuation of 20 dB, medium case: presents a 33.6 dB average attenuation and worst case: exhibits an average attenuation of 46 dB. Hence, the bottom-up channel provides different scenarios of channel modeling that can be classified into three categories according to the average attenuation level of the channel. We restrict the study on the worst case (third case) to show the performance of the proposed scheme under high impulsive noise conditions. 6.1. Performance of chaotic interleaving process 6.1.1. BER performances Initially, it is of utmost importance to check the performance of the proposed chaotic interleaving without the adaptive noise cancellation technique. Fig. 8 shows the BER performance for the following parameters: numbers of iterations M = 10 and M = 30, initial conditions y1 (0) = 0.1 and y1 (0) = 0.1, the secret key K s = [0.1, 0.1, 1.9, 0.5, M], the threshold T = 0.5 and impulsive noise events p fixed at 1%, 10%, and 20%, respectively. Clearly, the proposed chaotic interleaver (CI) block can greatly enhance the BER and SNR performance. For example, by using M = 10 10

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Table 1. Parameters used in Bottom-up PLC channel model [30].

Line index

Cable index

length( m)

14.9 29.1 11.9 10.39.8 138.8 2.2 8.5

L1 L2 L3 L4 S1 S2 S3

type 3 4 5 1 5 1 1

T1 T2 T3 T4 T5 T6 T7

Impedance index

value(Ω)

50 50 552 1705.3 674

ZT ZR Z1 Z2 Z3

Table 2. Characteristics of bottom-up PLC network cables used in the simulation [30], R = R0 .10−5

G0 .5.10−14 .2π f (S /m).

Cable type Section (mm2 ) Equivalent strain (eq ) ZC (X) C (pF/m) L (μH/m) R0 G0

1 1.5 1.45 270 15 1.08 12 30.9

2 2.5 1.52 234 17.5 0.96 9.34 34.7

3 4 1.56 209 20 0.87 7.55 38.4

4 6 1.73 178 25 0.78 6.25 42.5

5 10 2 143 33 0.68 4.98 49.3

0

−5

H(f)in dB

−10

−15

−20

−25

best−case medium−case worst−case

−30

−35

0

0.5

1

1.5

frequency in MHz

2

2.5

3 7

x 10

Figure 7. Frequency responses of different PLC channels generated by the bottom-up model.

11

f (Ω/m) and G =

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0

10

−1

10

−1

−2

−2

BER

BER

−3

−4

−2

10

−3

10

−4

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−5

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−5

−5

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−6

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−6

0

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SNR [dB]

15

20

10

−3

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−4

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No interleaving CI (M=10) CI (M=30)

−1

10

10

10

0

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No interleaving CI (M=10) CI (M=30)

10

10

10

(c)

0

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No interleaving CI (M=10) CI (M=30)

BER

(a)

12

−6

0

5

10

15

SNR [dB]

20

10

0

5

10

15

20

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Figure 8. BER performance of the chaotic interleaving (CI) under, a) p=1%, b) p=10% and c) p = 20% using the bottom-up PLC channel model.

iterations and under Eb/No=5 dB, an SNR improvement of 6–8 dB can be reached. Moreover, by increasing the iteration number, more enhancements can be reached. By using M = 30 iterations and under EB/No=0 dB, a SNR improvement of 4 dB can be reached for an impulsive noise probability p = 1%, and up to 13 dB SNR enhancement can be achieved for p = 10% and p = 20%. 6.1.2. Security Analysis Space key. As shown in section 3, the encryption key of the proposed encryption approach is constituted of five parts, namely x1 (0), x2 (0), a, b and Mt , where the first four parts are considered as a fraction part for double-precision float number of 52-bit length adheres to the IEEE 754 standard; and the last term Mt stores the iteration number which contains eight bits. Consequently, the key employed in the proposed encryption algorithm is of 52 × 4 + 8 = 216-bit length. Thus, the cipher key space has a high robustness to brute-force attacks [45] since it is similar to or better than state-of-the-art encryption methods and standards [46, 47] Key sensitivity analysis. To have a high security level, each encryption system must be sensitive to the encryption key. Such sensitivity is generally analyzed with reference to two aspects: Encryption: here we evaluate the difference between two ciphertext images C 1 and C 2 with reference to the same plaintext image when two encryption keys K 1 and K 2 are used, which are different only in one bit. Decryption: here we measure the difference between two decrypted images D1 and D2 with reference to the same ciphertext image when two encryption keys K 1 and K 2 are used, which are different only in one bit. Fig. 9 depicts the key sensitivity of the proposed chaotic encryption scheme regarding the encryption and the decryption processes, where K 2 , K 3 are slightly different from K 1 with only one bit. These results obviously illustrate that the chaotic encryption based 2D logistic function is highly sensitive to the encryption key for both encryption and decryption processes, meaning that the proposed chaotic encryption has good confusion properties [48]. Histogram Analysis. In order to check the image encryption quality, it is very important to investigate the encryption image histogram. A uniformly distributed histogram for ciphertext image is highly needed, since a secure image encryption approach aims to randomize a plaintext image effectively. Fig. 10 illustrates different ciphertext histograms extracted from the encrypted images. It is observable that these images cover the format from binary and 8-bit gray. From these results, it is clearly shown that the ciphertext image histograms become very flat after encryption despite the fact that some plaintext images have highly tilted histograms. Robustness to differential attack. The robustness to differential attack is another issue that should be addressed in any encryption system. The goal here is to study the effect of the difference in inputs on the corresponding outputs [49, 50]. It is a usual model of cryptanalysis, and a secure encryption method must have high robustness versus this 12

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type of attack. To measure the robustness of an image encryption method against the differential attack, we make use of the number of pixel changing rate (NPCR) and unified average changed intensity (UACI) tests. Let us consider the plaintext image P, and P2 as another plaintext image obtained from P by changing one bit of a pixel. Let C 1 and C 2 represent two ciphertext images encrypted from P and P2 , respectively. Therefore, the NPCR and UACI are defined by:  A(i, j) × 100% (29) NPRC(C 1 , C 2 ) = G i, j and UACI(C , C ) = 1

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We used the 8-bit grayscale images selected from the USC-SIPI ’Miscellaneous’ image dataset to test the robustness of the proposed chaotic encryption approach vs. other recent and well-known techniques [50]. Table 3 presents the comparison results in term of NPCR and UACI scores. The results show that the characteristics of our approach have excellent performance, with high scores (NPRC = 99.61%, UACI = 33.47). The proposed approach achieves the closest average scores of NPCR and UACI to the expected ones provided in [51]. Therefore, we can clearly prove that it has good robustness against differential attack. 6.2. Performance of the ANC-CI-ANFIS scheme In this section, simulation results of the proposed ANC-CI-ANFIS scheme are shown on an impulsive noise PLC channel. This is required as a performance validation prior to application on a realistic environment. The ANFIS is trained using four fuzzy rules with triangular MFs. The number of epochs is 20. The initial step size and step size increase rate are 0.1 and 0.01, respectively. Both LSM and BP algorithms are used for FIS training to model a given set of input/output data. 6.2.1. The case of BB-PLC systems In what follows, we discuss the performances of the proposed approach over some recent algorithms for the case of BB-PLC technology. Figs. 11(a)–(d) show the performance of the proposed ANC-CI-ANFIS scheme on an OFDM signal transmitted through a multipath PLC channel under impulsive noise environment. Hence, the original OFDM signal (green curve) is compared to the noisy OFDM signal (blue curve) and to the estimated OFDM signal (red curve). Clearly, satisfactory results are obtained even if the impulsive bursts probability is relatively important, i.e., p = 30%. Fig. 12 depicts the zooming of the obtained results when bursts probability is p = 30%. Indeed, from simulation results, each recovered signal are agree with each original signal. ANFIS technique has enough capability to use as adaptive noise filter for OFDM noisy signal. Three quantitative indexes are measured to compare the performance: the BER, the MSE and the SNR improvements error of the estimated output signal. The performances of the considered ANC-CI-ANFIS are then compared to the iterative noise suppression (INS) scheme [4] and the composite comparison value (CCV) based filter [52]. Fig. 13 illustrates the BER simulation results. Clearly, the proposed scheme successfully detects and removes the impulsive noise even with high impulse occurrence probabilities. Overall, the effectiveness of ANFIS noise cancellation was confirmed. For example, when an Eb/No=0 dB is considered, the BER enhancement achieved by the proposed approach can devolve from 2 × 10−1 without cancellation to 8 × 10−4 with ANC-CI-ANFIS. It also guarantees more less BER than the other algorithms. Furthermore, we can see that the increase of the impulsive probabilities p does not affect the performance of ANC-CI-ANFIS, in contrast to INS and CCV algorithms which are highly affected by the rise of p. Fig. 14 gives the MSE performances of ANC-CI-ANFIS in comparison to INS and CCV algorithms. From this figure, we find that using the ANC-CI-ANFIS algorithm to remove noise is much better than using INS or CCV. For 14

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Table 3. Imperceptibility results of the proposed watermarking system

File name 5.1.09 5.1.10 5.1.11 5.1.12 5.1.13 5.1.14 5.2.08 5.2.09 5.2.10 7.1.01 7.1.02 7.1.03 7.1.04 7.1.05 7.1.06 7.1.07 7.1.08 7.1.09 7.1.10 boat.512 alaine.512 gray21.512 numbers.512 ruler.512 5.3.01 5.3.02 7.2.01 testpat.1k

Mean Std

Liao’s NPCR UACI 49.8093 16.6687 99.6140 33.5374 49.8138 16.7015 49.8280 17.0621 99.5972 33.6419 99.6368 34.2965 99.6208 33.4267 99.6174 33.4553 99.6292 33.4993 49.8005 16.8228 49.8039 16.8126 49.8096 16.7308 99.6094 33.4778 99.6063 33.4581 99.6048 33.4489 99.6323 33.5216 99.6101 33.4496 49.8100 16.7680 49.8199 16.8557 99.6037 33.6291 99.6292 33.4419 99.6254 33.4770 99.6120 33.4503 99.6304 34.0635 498086 49.8086 996163 99.6163 49.8199 33.4685 99.6108 33.4786 81.8195 28.4887 24.3022 0.06116

Hua’s NPCR UACI 99.6658 33.5908 99.6475 33.5366 99.6674 33.4398 99.5941 33.4228 99.6445 33.4205 99.5975 33.4696 99.6281 33.4720 99.6197 33.4921 99.6288 33.4914 99.6273 33.5212 99.5892 33.4846 99.6201 33.4647 99.5894 33.5202 99.6185 33.5400 99.6117 33.5254 99.6223 33.5205 99.6151 33.5678 99.6044 33.5223 99.6101 33.4325 99.6006 33.5097 99.6128 33.5477 99.6082 33.3930 99.6059 33.3993 99.6265 33.5129 99.6098 33.4532 99.6119 33.4853 99.6156 33.4965 99.6124 33.4455 99.6180 33.4887 0.01957 7.97291

15

LAS-IES NPCR UACI 99.6064 33.4456 99.6154 33.4946 99.6244 33.5541 99.5703 33.4302 99.6109 33.4438 99.6364 33.4655 99.5870 33.4008 99.6260 33.4804 99.6124 33.4563 99.5992 33.5037 99.6075 33.4237 99.6079 33.4291 99.5988 33.4739 99.6170 33.4362 99.6272 33.3954 99.5931 33.4073 99.6094 33.4332 99.6162 33.4117 99.6045 33.4344 99.6154 33.4654 99.6196 33.4225 99.6022 33.4608 99.6141 33.4240 99.6120 33.4262 99.5931 33.4585 99.6128 33.4605 996156 33.4556 99.6072 33.4347 99.6093 33.4476 0.01332 0.03371

Proposed NPCR UACI 99.5124 33.5214 99.6121 33.4215 99.5943 33.4014 99.5811 33.4158 99.5963 33.4236 99.5945 33.3951 99.5878 33.3978 99.5812 33.4182 99.6100 33.4263 99.6028 33.4474 99.6078 33.4326 99.5811 33.4836 99.5946 33.4782 99.5937 33.4716 99.5912 33.4365 99.6014 33.4313 99.6013 33.4460 99.6148 33.3856 99.6097 33.3941 99.6101 33.3973 99.6185 33.4104 99.6034 33.4089 99.5941 33.4561 99.5945 33.4635 99.6032 33.4392 99.6108 33.4547 99.6036 33.4301 99.5971 33.4146

99.5965 0.01932

33.4322 0.03173

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example, when ANC-CI-ANFIS is applied under Eb/No=0 dB and a bursts probability p = 0.01, a MSE=1.63×10−4 is reached which is much less than MSE=2×10−2 and MSE=5.1×10−3 , that are obtained by CCV and INS, respectively. On the other hand, ANC-CI-ANFIS keeps obtaining a reduced MSE even when the noise level is increased, e.g. with burst probability, p = 0.2 and p = 0.3. Consequently, the obtained results clearly depict the success of the ANC-CIANFIS to suppress the impulsive noise even under high noise level. The SNR of the denoised signals using ANC-CI-ANFIS, INS and CCV algorithms were computed and the results are presented in Fig. 15. The results show that the performance of the ANC-CI-ANFIS method is high in improving the SNR. For example, more than 12 dB in SNR improvements are achieved with ANC-CI-ANFIS against INS and CCV algorithms, under Eb/No=0 dB and p = 0.01. Moreover, even if the impulsive probability p is increased, the SNR improvements introduced by ANC-CI-ANFIS are kept high for all Eb/No values. Under p = 0.2, the SNR improvements can exceed 13 dB in comparison to INS scheme and surpass 15 dB against CCV algorithm. 6.2.2. The case of NB-PLC In order to fit the scenario of NB-PLC, simulations are conducted using NB-PLC channel model described in [2]. We compare our proposed algorithm with two types of impulsive noise cancellation algorithms which use noise spar18

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Table 4. Time complexity of the proposed ANC-CI-ANFIS approach over some other algorithms

Algorithms INS CCV SBL w/ null tones SBL w/ all tones MMSE w/ NSI ANC-CI-ANFIS

Time complexity (in s) 6.667 36.437 129.012 30.185 25.321 32.576

sity to detect and suppress impulsive bursts. Hence the ANC-CI-ANFIS approach is compared with the parametric MMSE detector [53], denoted as MMSE w/NSI and sparse Bayesian learning (SBL) algorithm [32] denoted as SBP w/all tones which uses information available in all tones to estimate the impulsive noise. For this purpose, we generated an asynchronous impulsive noise from two different statistical models: a 3- component Gaussian mixture (GM) distribution with π = [0.9, 0.07, 0.03] and γ = [1, 100, 1000]. Performance analysis over the multipath PLC channel in terms of BER, MSE and SNR are plotted in Fig. 16. From Fig. 16(a) and (b), we can easily see the efficiency of the proposed ANC-CI-ANFIS scheme to mitigate the impulsive noise. For example, the BER can be reduced from 0.5 without cancellation to 0.006 under Eb/No=0 dB. Moreover, from Fig. 16(c), the superiority of the proposed ANC-CI-ANFIS scheme in terms of SNR is clearly shown in comparison to the other schemes. For example, more than 5 dB of SNR enhancement is achieved in comparison to SBL algorithm. 6.3. Time complexity Table 4 illustrates the complexity performance of different approaches when a data stream of 480000 bits is transmitted over the PLC channel. The complexity is measured by checking the execution time. This last one is obtained using Matlab 8.1 on a 3.3 GHz intel Core i3 computer with 12 GB of memory. It is shown from this table that ANC-CI-ANFIS can give less time complexity in comparison to the other algorithms such as CCV and SBL w/ null tones and comparable complexity to SBL w/ all tones. 6.4. Channel estimation In this section we try to fit a realistic environment by considering channel estimation instead of an ideal channel. Therefore, a simple yet effective channel estimation algorithm based on Least Squares (LS) in the frequency domain is used [54]. This estimator is selected since it has a low computational complexity. Fig. 17 illustrates the performance obtained when an LS estimation is introduced through the PLC based OFDM. It is clear that by using the LS estimator, a small degradation is occurred in term of BER under the different impulsive occurrence probabilities. Consequently, this can be acceptable since this algorithm adds a slight complexity to the OFDM system. 7. Conclusion Impulsive noise in PLC channel can be considered as a non-linear process which is usually too complicated for accurate canceling by traditional and statistical models. Therefore, an adaptive noise cancellation technique is proposed in this paper based on the ANFIS to adaptively suppress the impulsive noise generated through the PLC channel. Furthermore, a chaotic interleaving process is performed to secure data transmission and give more robustness against impulsive bursts. Simulation results demonstrate that the proposed ANC-CI-ANFIS scheme gives absolutely better noise suppression and data restoration performance when compared with some well-known impulsive noise reduction schemes. Moreover and from a security point of view, the way the data are interleaved with the chaotic interleaver significantly enhance the security by decreasing the information leakage about the key of interleaving in addition to reducing the effect of impulsive bursts. Therefore, the proposed scheme is an effective way to suppress the impulsive noise from the PLC channel and to improve the security accuracy of transmitted signals. Simulation results show that the proposed adaptive noise cancellation based ANFIS is advantageous in the sense that it has a linear-in-parameter 20

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characteristic. Consequently, the time complexity and the robust nonlinear processing ability are well consorted. Another positive point of the proposed scheme is its capability to track the dynamic change of OFDM signals affected by impulsive noise across trials. Acknowledgement: This project was financially supported by the DGRSDT (Direction G´en´erale de la Recherche Scientifique et du D´eveloppement Technologique) of Algeria (PNR 13/u18/4368). [1] H.C. Ferreira, L. Lampe, J. Newbury, T.G. Swart, Power Line Communications: Theory and Applications for Narrowband and Broadband Communications over Power Lines, Wiley, 2010. [2] M. Nassar, L. Jing, Y. Mortazavi, A. Dabak, Il Han Kim, Evans, B.L., Local Utility Power Line Communications in the 3–500 kHz Band: Channel Impairments, Noise, and Standards, IEEE Signal Proc. Mag. 29 (5) (2012) 116–127. [3] S.V. Zhidkov SV. Impulsive noise suppression in OFDM based communication systems, IEEE Trans. Consumer Electron. 49 (4) (2003) 944–8. 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Biography Dr. Yassine Himeur received the Master degree in electronic from The University of Science and Technology–Houari Boumediene (USTHB), Algiers, Algeria in 2011 and the PhD degree from Jijel University in 2015. Currently, he is a Senior researcher at Centre de Développement des Technologies Avancées (CDTA), in Algiers, Algeria. His current research interests are, Multimedia security, Multimedia retrieval and Powerline Communication. He is the recipient of the International Conference on Signal Processing and Multimedia Applications (SIGMAP 2014) Best Paper Award. He has authored several papers in refereed journals and international conference proceedings.

Pr. Abdelkrim Boukabou was born in Ouargla, Algeria, in 1977. He received the Dr. Eng. degree in electronic from Contantine University, Contantine, Algeria, in 2006. Currently, he is a full professor at Jijel University. His current research interests include nonlinear control and communication theory, power-line communications, smart grid communications, and robotic sensor networks. He has authored several papers in refereed journals and international conference proceedings, and has been serving as an Editor, and program committee member to numerous journals and conferences. He is also the recipient of the International Conference on Signal Processing and Multimedia Applications (SIGMAP 2014) Best Paper Award.

Biography