Combination of Turbo Coding and Cryptography in ... - yimg.com

2008 Internatioal Symposium on Telecommunications

Combination of Turbo Coding and Cryptography in NONGEO Satellite Communication Systems Dariush Abbasi-Moghadam, Vahid TabaTaba Vakili, A. Falahati Department of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran Email: [email protected], [email protected], [email protected]

DES is easily breakable because of the short key length. AES is the latest encryption algorithm, which is suitable for a variety of platforms ranging from smart cards to big servers. AES is fast in both software and hardware, is relatively easy to implement, and requires little memory [3]. Asymmetric key encryption algorithms are based on mathematical functions rather than on substitution and permutation. The use of two separate keys has profound consequences in the areas of key distribution and authentication [2]. Security services such as authentication and data integrity, which are required for the overall protection of satellite data. This has been highlighted on mitigating the risk of satellites being taken over by unauthorized users. In table I existing security services in LEO satellites are shown.

Abstract Turbo code has the capability of error correction near Shannon's limit. This code is concatenated of 2 recursive convolutional codes with pseudo random interleaving. In this paper secure turbo code performance in Rayleigh, Rician fading and NONGEO satellite channels are simulated and analyzed. Simulation results show that this code has appropriate performance in wireless channel in presence of light shadowing, and good performance in heavy shadowing. This scheme has the advantages of High-speed encryption and decryption with high security, smaller encoder and decoder size and greater efficiency.

Keyword: Turbo code, Fading, Rayleigh, Rice, Satellite I-

Table I

INTRODUCTION

Existing Security Services in LEO Satellites

Error control and security are both important aspects of modern digital communications and are often used together in one application. The demand for a reliable, secure and efficient digital data transmission system has been accelerated by the emergence of large-scale and high speed communication networks. Satellites present some significant security challenges: • Eavesdropping and active intrusion are much easier than in terrestrial fixed or mobile networks because of the broadcast nature of satellites. • Satellite systems are resource-constrained, particularly in the areas of limited transmission power (and, thus, channel capacity), and limited processing and switching capability for satellites with on-board processing. • Satellite channels experience high bit-error rates, which can result in packet loss and the loss of security synchronization. Security systems for satellite data, thus, have to be optimized to take account of these limitations, in particular the need for confidentiality and the requirement to use satellite resources efficiently. Geostationary satellites also suffer from a long propagation delay, and security systems must, therefore, add only minimal delays to traffic. Also forward error correction (FEC) mechanisms is use to reduce the satellite link’s effective error rate for a multicast channel.

978-1-4244-2751-2/08/$25.00 ©2008 IEEE

Spacecraft Name

Algorithm

Implementation Platform

STRV 1d

DES

Software on SPARC processor

METOP

ExOR

Hardware

KOMPSA II

IDEA

Hardware

EPS

3-DES

Hardware

China DMC

AES

Software

II- SECURE TURBO CODING Satellites operate in harsh radiation environment so the implementation should be robust to radiation induced bit flip errors. On average 64 bits (50 %) are corrupted with a single error during encryption using AES. The bit flip errors must be detected and corrected in order to avoid the transmission and use of corrupted data, so secure turbo coded scheme is suggested for these channels. Turbo Code has raised great interest in the coding community with its astonishing performance. It is a parallel-concatenated convolutional code (PCCC) whose encoder is formed by two or more constituent recursive systematic convolutional (RSC)

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encoders joined by interleaver. The input information bits feed the first encoder and, after having been scrambled by the interleaver, enter the second encoder [3]. Adding security to channel coding is an attractive topic, as it could reduce the overall processing cost of providing secure encoded data. A secret channel coding scheme is one that provides both data secrecy and data reliability in one process, to deal with problems in an insecure and unreliable channel. In this section we propose an adaptive secure turbo code for power line channels. For security purposes we have used encrypted puncturing matrix for turbo codes. When the channel state is bad, the transmitter picks more redundant bits for protection. Therefore, the overall throughput is low. As the channel condition gets better, less redundancy is needed for protection. Hence, a higher throughput is achieved. The bit error rate of the channel depends on the instantaneous receiver signal-tonoise ratio (SNR), code rate, frame length and constraint length. The proposed puncturing scheme is based on the pseudo-random numbers generator algorithm for selecting N bits from M turbo encoded bits. Also we can use encryption algorithm for interleaving process. This scheme has the advantages of High-speed encryption and decryption with high security, smaller encoder and decoder size and greater efficiency. In a conventional method, if there is even a single error in the received ciphertext (after channel decoding), there will be a huge number of errors in the decrypted plaintext, whereas, in the proposed scheme, it is not so.

LEO satellite communication channels are time varying and multipath, and thus cause significant fading. The motion of the land-based mobile, traversal of the satellite relative to the earth, and changing absorption, scattering, refraction, and diffraction effects of the environment cause both fast and slow fading. As with many physical channels, the statistics of the fading of the LEO channel are wide sense stationary (WSS) process. In this paper, we use of Patzold channel model for LEO satellite channel simulation.

r = Se

j ( 2πf p t +θ p )

+ X 1 + jY1

(1) where S is log-normal random process, X1 ,Y1 are Gaussian distributed and fp, θ p are constant. This model shows in Figure 2. For the GEO & MEO satellites Doppler shift frequency is low and these channels are modelled as slow fading channel, we use of Hwang model for MEO satellite, that is combination of Rice and Lognormal statistics, with independent shadowing effecting each direct and diffuse component, respectively, that given by: (2) re jϕ = Ac S1e jϕ + RS 2 e j (θ +ϕ ) where S1 and S2 are independent lognormal distributions, respectively and R has a Rayleigh distribution.

Fig. 2 LEO satellite channel model

An efficient simulation model [2], can be derived by approximating the processes v1 ( t ) and v 2 ( t ) by the following sum of N1 sinusoids: Ni

v~i (t ) = ∑ ci , n cos(2πfi , nt + θ i , n )

Fig.1 A secure turbo code scheme

III- SPACE COMMUNICATION CHANNEL MODELS

where

The quality of data transmission is crucially influenced by the particular characteristics of signal propagation in the link between satellite and other spacecraft or user. In order to investigate this subject, channel model have been derived, that described the transmission path. Space Channel is including links between LEO satellite and MEO or GEO satellite and ground station. The deep-space channel is modeled as AWGN channel and in aeronautical communication such as communication link between two satellites or between satellite and aircraft, because of absence of shadowing fading, channel is modelled as Rician process, but for terrestrial application, the shadowing occurs.

, i = 1,2

(3)

n =1

c i, n , f i, n and θ i,n are called Doppler coefficients,

discrete Doppler frequency, and Doppler phases, respectively. c i, n , f i, n and θ i,n are computed by “equal area” method that is described in [2] by: f1, n = f max sin[ N 1′ = [ N1

π 2 N 1′

1 ( n − )], n = 1,2,..., N 2

π 2 arcsin( k D )

(4)

]

where f1,n is the discrete Doppler frequencies and f 2,n are obtained by finding the Zeros of:

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f 2n − 1 − erf ( 2, n kc ln 2 ) = 0, n = 1,2,..., N 2 2N2 kD f max

(5)

⎧⎪ δ 2 N1′ i = 1 ci , n = ⎨ D , i = 2, n = 1,2,..., Ni ⎪⎩ 2 N 2 And, the

Doppler phases

θ i,n

(6) are combined to a

vector θ i = (θ i,1 , θ i,2 ,..., θ i, Ni ) , where the elements of this vector are identified with a permutation of the elements of the vector ⎡ 2π 2 π.N i ⎤ for i=1, 2. Other parameters 2 π.2 , φi = ⎢ ,..., ⎥ ⎣ ( N i + 1) ( N i + 1)

( N i + 1) ⎦

determine in table. Table II Parameters of the Analytical model for LEO satellite Fig. 3 Secure turbo code and convolutional coding in AWGN channels

Shadowing

δD

kD

Light

0.4473

0.6731

0

0.0208

B. Turbo codes in satellite with LOS component

Heavy

0.2927

0.6865

0.5569

-1.8138

When LOS and multipath component are existing in receiver, channel model is Rician. On example of this scenario is communication satellite in suburban area. This model is specified by Rice-factor witch is the ratio of scattered components to LOS components energy. For Raician fading environment with PDF of: 2 2 2 a aα (8) f A ( a ) = 2 e −( a +α ) / 2δ I 0 ( 2 ) a ≥ 0 δ δ BER in this case can drive as:

S

m

IV- SIMULATION OF SECURE TURBO CODES ‫ﻩ‬N DIFFERENT CONDITIONS In this section performance of turbo code in different satellite channel are simulated. In all cases Eb/N0 are in dB and Rb=100 Kbps, and Doppler frequency is equal 5.6 KHz.

(u ⎛ γ /(1 + ε ) ⎞⎟ − P = Q (u , v) − 0.5⎜⎜1 + e 1 + γ /(1 + ε ) ⎟⎠ ⎝ where

A. Simulation of secure turbo codes in AWGN

Gaussian noise has maximum entropy and most of random process can represent by this PDF, so we simulate secure turbo codes in chise channels. an estimated error-floor bound (free-distance asymptote) for the bit error probability over the additive white Gaussian noise (AWGN) channel may be considered as follows [8]:

Pb (e) ≥

N freeW free K

⎛ E ⎞ Q⎜⎜ 2d free R b ⎟⎟ N0 ⎠ ⎝

ε= u=

(7) v=

Where dfree is the free distance of the code, Nfree is the number of code words with output weight dfree, Wfree represents the weight of input sequence associated with output weight dfree, K is the input block length and R is the code rate. Turbo code performance in AWGN channels shows in figure 3. As we see code performance is very good. This code has 2.5 – 3 dB gain relative to convolutioal code. Therefore turbo code performance in Deep space, GEO satellite and intersatellite link need low Eb/N0. With increasing of iteration number and code length code performance enhanced dramatically.

ε ⎛⎜ 2 ⎜⎝

2

+v 2 ) 2

I 0 (uv )

E α2 , γ = d free R b 2 N0 2δ

1+

γ γ ⎞⎟ γ 2γ −2 (1 + ) /(1 + ) 1+ ε 1+ ε 1 + ε ⎟⎠ 1+ ε

2γ γ γ ⎞⎟ γ 1+ (1 + ) ⎟ /(1 + ) +2 ⎜ 2 ⎝ 1+ ε 1+ ε 1+ ε ⎠ 1+ ε

(9)

(10) (11)

ε ⎛⎜

(12) In figure 4-6 secure turbo code performance in different rice factor, constraint length, decoding algorithms and different frame size are simulated and compared. For low bit rate, satellite channel model is frequency non-selective. Figure 5 shows the secure turbo code performance in different RiceFactor (K_bar). It is seen that by increasing of K_bar code performance enhanced dramatically. Figure 5 shows the secure turbo code performance with different decoding algorithm, as we see LOG-MAP algorithm is better than SOVA, but Log-MAP complexity is higher than SOVA. In figure 6 secure turbo code performance with different constraint length are simulated. Code performance is enhanced by increasing of K but complexity and computational increased.

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only multi-path component due to impedance mismatch), whose PDF is given by: α 1 (13) Pα (α ) = exp(− ), α ≥0 Where ∞

α

α

α

is the parameter of Rayleigh PDF.

Pb = ∫ Pb (e α ) × Pα (α ) dα 0

α2

Has

χ

square probability density function, so Pb is

Pb =

N freeW free ∞ ⎡ ⎛ E ⎞⎤ ⎢ Q⎜⎜ 2d free R b χ ⎟⎟⎥ × Pχ ( χ ) dχ ∫ K N 0 ⎠⎥⎦ 0 ⎢ ⎣ ⎝

=

⎧ ⎪∞ ⎛ Eb ⎞ ⎪ χ ⎟⎟ × Pχ ( χ ) dχ ⎨∫ Q⎜⎜ α 2d free R N 0 ⎪0 ⎝ ⎠ ⎩⎪

Fig. 4 secure turbo code performance with different Rice factor and k=3, Fs=1000, r=1/2

N freeW free K

Closed forms of these integrals are as follow [13]: Pb _ multipath

where

(14) ⎫ ⎪ ⎪ ⎬ ⎪ ⎪⎭

⎫ ⎧ ⎪ N freeW free ⎪⎪ ξ 2α / 2 ⎪ = ) ⎬ ⎨(1 − 2 2K 1+ ξ α / 2 ⎪ ⎪ ⎪⎩ ⎪⎭

(15)

Eb (11) N0 In the absence of LOS channel model of LEO satellite is Rayleigh.

ξ = 2d free R

D. Secure Turbo codes in NONGEO satellite channels

In this section turbo code performance in NONGEO satellite channel such as LEO and MEO in light and heavy shadowing are simulated and analyzed. Heavy shadowing occurs in urban area. In this environment multi-path component with different delay are exist and LOS component has low effect in received signal. With consideration of Link budget for NONGEO satellite, an approximation of secure turbo code performance is: Eb Tb R xP 1 R xP (16) = = N0 N0 Rb N 0

Fig. 5 Secure turbo code performance with different decoding algorithm and Rice-Factor= 6dB, Fs=1000, r=1/2, K=3

R xP (dB) = TxP + TxG - TxL - FSL - ML + R xG - R xL

4π d f 2 H2 FSL = ( ) =( ) = Ad 2 = A 2 c λ sin ( β ) 4π d

So

=

Fig. 6 Secure turbo code performance with different constraint length and Rice-Factor= 4 dB, Fs=1000, r=1/2

(17) (18)

Pb (e) is:

Pb (e) ≅ =

2

N freeW free K

⎛ R R XP ⎞⎟ Q⎜⎜ 2d free ⎟ R b N0 ⎠ ⎝

⎞ ⎛ R Ad 2 [10 TxP + TxG - TxL - ML + R xG - R xL ] ⎟ Q⎜ 2d free ⎟ ⎜ Rb N 0 ⎠ ⎝ 2 ⎞ ⎛ R AH [10 TxP + TxG - TxL - ML + R xG - R xL ] ⎟ Q⎜ 2d free 2 ⎟ ⎜ R sin ( ) N β b 0 ⎠ ⎝

N freeW free K

N freeW free K

(19) where RxP is received power, TxP is transmitter output power,TxG is transmitter antenna gain, TxL is transmitter losses (cowaxial), ML is miscellaneous losses (fading, body loss, polarization mismatch,...), RxG is receiver antenna gain, RxL is receiver losses (coax, connectors...), FSL is free space

C. Turbo codes in satellite without LOS component

In such environments, channel model is Rayleigh process. Raleigh channel is the simplest fading channel from the standpoint of analytical characterization (worst case contain

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REFERENCES

loss or path loss, H is satellite altitude, β is elevation angle, and Rb is the overall bit rate. Figure 7 and 8 show the performance of adaptive secure turbo code in NONGEO satellite. As we see in heavy shadowing, for an appropriate communication link, Eb/N0 should be larger than 12 dB, but for light shadowing adaptive secure turbo code performance is very good for low Eb/N0.

[1]. [2]. [3]. [4].

[5]. [6]. [7].

[8]. [9]. [10].

Fig. 7 Secure turbo code performance in NONGEO satellite with heavy shadowing

Fig. 8 Secure turbo code performance in NONGEO satellite with light shadowing

V- CONCLUSION In satellite communication Uplink should be checked for integrity and authentication in order to protect the satellite from being taken over by unauthorized personnel. Downlink should be encrypted with secure and suitable algorithms to protect the valuable and sensitive data transmitted to ground. In this paper performance of secure turbo coding in NONGEO satellites are simulated and analyzed. Simulation results show a good performance and appropriate security. A combination of turbo coding and an AES cryptosystem are suggested because of its High-speed encryption and decryption with high security, smaller encoder and decoder size and greater efficiency.

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