Analysis of transmission line loaded with BCI probe using circuit concept approach

Transcription

1 IEICE Communications Express, Vol.4, No.7, Analysis of transmission line loaded with BCI probe using circuit concept approach Kimitoshi Murano 1a), Naoki Takata 1, Majid Tayarani 2, Fengchao Xiao 3, and Yoshio Kami 3 1 School of Engineering, Tokai University, Kitakaname, Hiratsuka-shi, Kanagawa , Japan 2 School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran , Iran 3 Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofugaoka, Chofu, Tokyo , Japan a) murano@tokai.ac.jp Abstract: Bulk current injection (BCI) test is adopted as an immunity testing method for automotive electronic equipment. In this letter, an analytical method for obtaining the terminal output of the transmission line excited by using the BCI probe is proposed. The test setup is analytically solved by using a circuit concept approach because it is considered to be a transmission line externally excited by an electromagnetic field. To confirm the validity of the proposed method, a single-ended transmission line loaded with the BCI probe is considered as an example. The comparison with the analytical solution and our experimental results shows a good agreement. Keywords: BCI test, immunity, electromagnetic coupling, transmission line, circuit concept approach Classification: Electromagnetic Compatibility (EMC) References DOI: /comex Received May 25, 2015 Accepted June 18, 2015 Published July 13, 2015 [1] Road vehicles Component test methods for electrical disturbances from narrowband radiated electromagnetic energy Part 4: Harness excitation methods, International Standard, ISO , Dec [2] A. Orlandi, G. Antonini, and R. M. Rizzi, Equivalent circuit model of a bundle of cables for bulk current injection (BCI) test, IEEE Trans. Electromagn. Compat., vol. 48, no. 4, pp , Nov DOI: /TEMC [3] H. Tanaka, A. Takahashi, Y. Hattori, and M. Izumichi, A modeling methodology for simulation of BCI (bulk current injection) test, IEICE Trans. Commun., vol. J96-B, no. 4, pp , April 2013 (in Japanese). [4] Y. Kami and R. Sato, Circuit-concept approach to externally excited transmission lines, IEEE Trans. Electromagn. Compat., vol. EMC-27, no. 4, pp , Nov DOI: /TEMC [5] N. Takata, Y. Kami, F. Xiao, M. Tayarani, and K. Murano, Susceptibility characteristics of transmission line in BCI test, IEICE Tech. Rep., EMCJ , vol. 114, no. 398, pp. 1 4, Jan (in Japanese). 223

2 IEICE Communications Express, Vol.4, No.7, Introduction Bulk current injection (BCI) test is usually conducted to evaluate the immunity characteristics of automotive electronic equipment [1]. In the BCI test, an electric current is induced in a wire harness connecting to the equipment under test (EUT) by using a BCI probe in a toroidal coil generating a magnetic field linking the wire harness. In recent years, some studies on simulation model of the BCI-test system have been carried out. A circuit model of a bundle of cables in the BCI-test setup was suggested to predict the induced signals at the terminations in [2], and a simulation model of BCI-test system was reported to predict the resonant frequencies of the injected current occurring on the wire harness [3]. In contrast, because the BCI-test model can be considered as a kind of electromagnetic (EM) coupling phenomenon between the external-em field and a transmission line, the output of the transmission line can be analytically derived using a circuit concept approach [4]. If the model can be analytically solved, the optimum position of the BCI probe on the wire harness can be easily found, and over or underestimation of the immunity of the EUT can be avoided [5]. This letter describes a new analytical method of the BCI-test system based on the circuit concept approach. The method makes it possible to derive the induced current or voltages at the terminals of the transmission line under all conditions. In this letter, the proposed method is validated by an experimental result of a single transmission line which is loaded with a BCI probe. 2 Formulation for BCI test setup Consider a transmission line of length externally excited by an EM-plane wave in Fig. 1(a). By modifying the telegrapher s equation, a relation between two terminal outputs on both sides of the transmission line can be expressed as follows [4]: " # Z " # " # Vð0Þ V f ðxþ Vð Þ FðxÞ dx ¼ Fð Þ ð1þ Ið0Þ 0 I f ðxþ Ið Þ where V and I are the line voltage and current, and V f and I f are the distributed voltage and current source on the transmission line expressed as V f ðxþ ¼ j! Z h 0 B e z dy I fðxþ ¼j!C Z h 0 E e y dy where h and C are the line height and the line capacitance, respectively. V f and I f are equivalently generated by the external-em fields, E e and H e ¼ B e = 0. F is the chain matrix of the transmission line as follows: 2 3 cos x jz 0 sin x FðxÞ ¼4 j 1 5 sin x cos x Z 0 DOI: /comex Received May 25, 2015 Accepted June 18, 2015 Published July 13, 2015 where β and Z 0 are the phase constant and the characteristic impedance of the transmission line, respectively. In Eq. (1), the 2nd term on the left side indicates the voltage and current induced by E e and B e, so that those effects are equivalently expressed at the starting point x ¼ 0 in Eq. (1). Thus an equivalent circuit corresponding to Eq. (1) can be expressed as Fig. 1(b) where the effects of E e and B e 224

3 IEICE Communications Express, Vol.4, No.7, Fig. 1. Externally excited transmission line. DOI: /comex Received May 25, 2015 Accepted June 18, 2015 Published July 13, 2015 are expressed by using the equivalent source E eq0 and J eq0. E eq0 and J eq0 are as follows. " # E Z " # eq0 V f ðxþ ¼ FðxÞ dx I f ðxþ J eq0 0 Here, we assume that x 0 is an arbitrary position on the transmission line. Multiplying on both sides of Eq. (1) by the unit matrix I ¼ Fðx 0 ÞF 1 ðx 0 Þ,we finally obtain the following expression: " # Z " # " # Vðx 0 Þ V f ðxþ Vð Þ Fðx x 0 Þ dx ¼ Fð x 0 Þ : ð2þ Iðx 0 Þ 0 I f ðxþ Ið Þ The equivalent circuit corresponding to Eq. (2) is expressed as Fig. 1(c). Namely, Eq. (2) shows that the effects of E e and B e are expressed at the arbitrary position x 0 on the transmission line. In the case of BCI-test setup, the BCI probe locates at an arbitrary point on the transmission line. The probe generates a magnetic field locally linking the line, so that its effect can be expressed in an equivalent voltage source Eeq BCI at the point as shown in Fig. 2(a). In this case, Eq. (2) can be written as " # " # " # Vðx 0 Þ EBCI eq Vð Þ ¼ Fð x 0 Þ : ð3þ Iðx 0 Þ 0 Ið Þ Multiplying both sides of above equation by Fðx 0 Þ yield " " # " # Vð0Þ # EBCI eq0 Vð Þ ¼ Fð Þ Ið0Þ Ið Þ where J BCI eq0 ð4þ 225

4 IEICE Communications Express, Vol.4, No.7, Fig. 2. Two kinds of equivalent circuits of transmission line with BCI probe. " Eeq0 BCI # 2 3 cos x 0 ¼ Eeq BCI 4 j 1 5: sin x 0 Z 0 J BCI eq0 Eq. (4) shows that the equivalent voltage and current sources are expressed at the terminal x ¼ 0 on the transmission line as shown in Fig. 2(b). The terminal output can be provided theoretically by solving Eq. (4): the induced currents at both terminal loads, R 0 and R, are 2 3 " # Ið0Þ ¼ Ið Þ 6 4 ðr cos þ jz 0 sin Þ 1 cos þ j R sin Z 0 R " # Eeq0 BCI J BCI eq0 : 3 Experimental validation Experimental evaluation of a transmission line loaded with a BCI probe have been conducted to validate the proposed method. Experimental setup is shown in Fig. 3(a). The length and height h of the transmission line examined here are 1 m and 40 mm, respectively. In this case, the characteristic impedance Z 0 is about Ω. One terminal of the transmission line is terminated with a load of 50 Ω, and the other is connected to port#2 of a network analyzer. In addition, the terminal of the BCI probe is connected to port#1 of the network analyzer so that the transmission coefficient js 21 j is measured as relative values of the terminal output for a constant current injected to the transmission line. Fig. 3(b) shows the relative terminal outputs measured for various frequency and probe position x 0. In this figure, js 21 j is normalized by the maximum value for obtaining the relative output variation. The theoretical results under the same condition mentioned above are shown in Fig. 3(c). From these results, we can see that the theoretical results accord well with tendency of Fig. 3(b). Figs. 3(d) and (e) show a comparison between experimental and theoretical values for the frequency of MHz as an example. Both results show that the terminal output may greatly change by the probe position x 0. From these result, it is found that the terminal output can be expected by using the proposed method. Moreover, it is indicated that the most suitable probe position where over or underestimation of the conducted immunity of the EUT is avoided can be found. DOI: /comex Received May 25, 2015 Accepted June 18, 2015 Published July 13,

5 IEICE Communications Express, Vol.4, No.7, Ω Fig. 3. Measurement of relative terminal output of transmission line loaded with BCI probe. 4 Conclusion A theoretical method for analyzing the transmission line loaded with the BCI probe was proposed. The validity of this method was demonstrated through the experiments using the single-ended transmission line. Since this method is based on the circuit concept approach, it is applicable to various kinds of transmission lines under any terminal conditions such as a multi-conductor transmission line with any load impedance. And it needs to estimate quantitatively the equivalent voltage source due to the BCI probe because the theoretical results shown here are in relative value. DOI: /comex Received May 25, 2015 Accepted June 18, 2015 Published July 13,

6 IEICE Communications Express, Vol.4, No.7, OpenStack hypervisor, container and Baremetal servers performance comparison Yoji Yamato a) Software Innovation Center, NTT Corporation, Midori-cho, Musashino-shi, Tokyo , Japan a) yamato.yoji@lab.ntt.co.jp Abstract: Recently, IaaS services provide not only virtual machines on hypervisors but also Baremetal servers or container based virtual servers. In this paper, we measure performances and start up time of Baremetal server, container servers, virtual machines on OpenStack with virtual server number changing and evaluate quantitative performances. Keywords: performance, cloud computing, IaaS, Baremetal, container, hypervisor, OpenStack Classification: Network References [1] OpenStack web site, [2] W. Fester, A. Ferreria, R. Rajamony and J. Rubio, An updated performance comparison of virtual machines and Linux containers, IBM Research Report, July [3] B. Russell, Passive benchmarking with docker LXC, KVM & OpenStack, slides in Apr [4] UnixBench web site, 1 Introduction DOI: /comex Received June 2, 2015 Accepted June 30, 2015 Published July 28, 2015 Recently, cloud technology has been progressed and many providers have started cloud services. To build IaaS systems, many providers adopt open source software such as OpenStack [1] and CloudStack. NTT group also has started IaaS services based on OpenStack since Currently, many cloud services provide virtual servers to users using virtual machines deployed on hypervisors such as Xen or KVM. However, hypervisors have a demerit of much virtualization overhead. Therefore, some providers have started to provide non-virtualized Baremetal servers (hereafter, Baremetal) or container based virtual servers which overheads are small (hereafter, Container). 228

7 IEICE Communications Express, Vol.4, No.7, It is generally said that Baremetals and Containers show better performances than virtual machines on hypervisors. However, there are few works to compare performances and start up time of those three in same conditions and appropriate usage discussions based on quantitative data are not enough. For example, [2] compared performances of Baremetal, Docker and KVM but there is no data of start up time. The work of [3] includes Baremetal, Docker and KVM results of boot up time, reboot time and other performance data other than Unixbench data. However, it did not compare 3 types with virtual server number changing. Therefore, this paper measures performances and start-up time of Baremetal using Ironic, Containers by Docker and virtual machines on KVM with virtual server number changing on OpenStack and shows quantitative data. The previous work of [2] and [3] do not have enough data with virtual server number changing. 2 Outline of Baremetal, Container and Hypervisor In this section, we compare Baremetal, Container and Hypervisor qualitatively. Baremetal is a non-virtualized physical server and same as an existing dedicated hosting server. IBM SoftLayer provides Baremetal cloud services adding characteristics of prompt provisioning and pay-per-use billing to dedicated servers. In OpenStack, Ironic component provides baremetal provisioning. Because Baremetal is a dedicated server, flexibility and performance are high but provisioning and start-up time are long and it also cannot conduct live migrations. Containers technology is OS virtualization. OpenVZ or FreeBSD jail were used for VPS (Virtual Private Server) for many years. Computer resources are isolated with each unit called container but OS kernel is shared among all containers. Docker which uses LXC (Linux Container) appeared in 2013 and attracted many users because of its usability. Containers do not have kernel flexibility but a container creation only needs a process invocation and it takes a short time for start up. Virtualization overhead is also small. OpenVZ can conduct live migrations but Docker or LXC cannot conduct live migrations now. Hypervisors technology is hardware virtualization and virtual machines are behaved on emulated hardware, thus users can customize virtual machine OS flexibly. Major hypervisors are Xen, KVM and VMware ESX. Virtual machines have merits of flexible OS and live migrations but those have demerits of performances and start up time. Next, we compare performance and start-up time quantitatively. 3 Performance measurement conditions DOI: /comex Received June 2, 2015 Accepted June 30, 2015 Published July 28, 2015 This paper measures performances and start up time of 3 types servers with same conditions. We use OpenStack version Juno as a cloud controller, a physical server provisioned by Ironic as Baremetal, Docker as a container technology and KVM/QEMU as a hypervisor. Ironic, Docker and KVM are de facto standard software in OpenStack community. Server instances are Ubuntu Linux servers with Apache2 web servers from 10 GB image file and we request 3 types instances provisioning to a same physical server using OpenStack compute component Nova. 229

8 IEICE Communications Express, Vol.4, No.7, Fig. 1. Performance measurement environment servers 3.1 Performance measurement items Measured servers: Baremetal provisioned by Ironic, Containers based on Docker, Virtual machines on KVM Virtual server number: 1, 2, 3, 4 Only 1 for Baremetal case, 1 4 containers for Docker case and 1 4 virtual machines for KVM case. When there are plural virtual servers, all physical resources are equally separated to these plural servers. Performance measurement UnixBench [4] is conducted to acquire UnixBench performance indexes. Note that UnixBench is a major system performance benchmark. Start up time measurement A time from Nova server instance creation API call to each Linux and Apache2 server start up is measured. For Baremetal case, we measure not only total time but also each processing time of start up and we also measure the 1st time boot and the 2nd time boot. 3.2 Performance measurement environment For a performance measurement environment, we prepared 1 physical server on which 3 types servers were provisioned and 1 physical server which had OpenStack components (Nova, Ironic, PXE server for Ironic PXE boot and so on). These servers were connected with Gigabit Ethernet and Layer 2 switch. Fig. 1 shows each server specification. 4 Performances of Baremetal, Docker and KVM DOI: /comex Received June 2, 2015 Accepted June 30, 2015 Published July 28, UnixBench performance Fig. 2 shows a performance comparison of 3 types servers. Vertical axis shows UnixBench performance index value and horizon axis shows each server with virtual server number changing. Based on Fig. 2 results, it is clear that Docker containers performance degradation is about 75% performance compared to Baremetal performance. And it is also said that Docker performance is degraded when we change virtual server number but it is not inverse proportion. Almost all performances of Docker are 230

9 IEICE Communications Express, Vol.4, No.7, Fig. 2. UnixBench performance index score comparison better than KVM but file copy performances are worse than KVM, therefore the total index value is not much higher than KVM. Meanwhile, virtual machines on KVM performance degradation is more larger and only 60% performance compared to Baremetal performance and KVM performance degradation tendency with virtual server number change is as same as Docker. 4.2 Start up time Fig. 3(a) shows start up time of 3 type servers. When virtual servers are plural, average start up time is showed. Fig. 3(b) shows each processing time of Baremetal start up for the 1st time boot and the 2nd time boot. From Fig. 3(a), Baremetal start up takes much long time than KVM and Docker. This is because Baremetal start up needs image writing for PXE boot for the 1st time boot and it takes long time. For the 2nd time boot, it does not need image writing and total start up time is about only 200 sec (see, Fig. 3(b)). Comparing Docker and KVM, Docker containers start up are shorter than KVM virtual machines and are less than 15 sec. This is because a virtual machine start up needs OS boot but a container creation only needs a process invocation. Precisely, Docker instance creation only takes several hundred msec but OpenStack processing such as API check, port creation and IP address setting take about 5 sec. DOI: /comex Received June 2, 2015 Accepted June 30, 2015 Published July 28, 2015 Fig. 3. Start-up time comparison. (a) Baremetal, Docker and KVM start up time. (b) Each processing time of Baremetal start up. 231

10 IEICE Communications Express, Vol.4, No.7, Discussion Here, we discuss appropriate usages of IaaS servers based on quantitative data. Because Baremetal shows better performances than other 2 types servers, it is suitable to use large scale DB processing or real time processing which have performance problems when we use virtual machines. Containers lack flexibility of kernel but performance degradation is small and start up time is short. Thus, it is suitable for auto scaling for existing servers or shared usages of basic services such as Web or mail. Hypervisors are suitable to use for areas which need system flexibility such as business applications on specific OS. 5 Conclusion This paper measured performances and start up time of 3 types IaaS servers; Baremetal, Docker and KVM with virtual server number changing and showed quantatitive data. We also studied application areas of each type based on the rusults. In the future, we plan to enhance IaaS services line up for appropriate use of 3 types servers. DOI: /comex Received June 2, 2015 Accepted June 30, 2015 Published July 28,

11 IEICE Communications Express, Vol.4, No.7, Index-based maximum likelihood RaptorQ code decoder Yi-Pin Lu a) and Yi-Yao Lan Graduate Institute of Electronics Engineering, National Taiwan University, Taipei City, Taiwan a) Abstract: Aiming at the next generation forward error correction code (FEC) in the application layer, a RaptorQ code decoding algorithm is proposed in this paper. The proposed index-based decoder significantly reduces the decoding complexity by tabulates the indices of the non-zero entries in the sparse code generator matrix. As the number of tabulated indices is much less than the dimension of the code generator matrix, the computational complexity is up to ten times lower than direct implementation of the Raptor code decoder, the previous version of RaptorQ code. Finally, saving of up to two orders of magnitude in the required memory is also achieved by the proposed solution. Keywords: error correction code, fountain code, Raptor code, RaptorQ code Classification: Fundamental Theories for Communications References [1] M. Luby, A. Shokrollahi, M. Watson, T. Stockhammer, and L. Minder, RaptorQ forward error correction scheme for object delivery, Internet Engineering Task Force, RFC6330, http//:tools.ietf.org/html/rfc6330, accessed Jun. 11, [2] P. Cataldi, M. P. Shatarski, M. Grangetto, and E. Magli, Implementation and performance evaluation of LT and Raptor codes for multimedia applications, Proc. IEEE Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), Pasadena, California, USA, pp , Dec DOI: /IIH-MSP [3] T. Mladenov, S. Nooshabadi, and K. Kim, Implementation and evaluation of Raptor codes on embedded systems, IEEE Trans. Comput., vol. 60, no. 12, pp , Dec DOI: /TC [4] S. Kim, S. Lee, and S. Y. Chung, An efficient algorithm for ML decoding of Raptor codes over the binary erasure channel, IEEE Commun. Lett., vol. 12, no. 8, pp , Aug DOI: /LCOMM [5] A. Shokrollahi, Raptor codes, IEEE Trans. Inf. Theory, vol. 52, no. 6, pp , June DOI: /TIT DOI: /comex Received June 12, 2015 Accepted June 30, 2015 Published July 28,

12 IEICE Communications Express, Vol.4, No.7, Introduction RaptorQ code [1], the first standardized forward error correction code (FEC) in the application layer, has recently drawn much attention from researchers. While conventional FEC systems need to retransmit the complete coded sequence if the receiver fails to decode the information sequence, the RaptorQ code decoder retains the partial coded sequences that are correctly received, until enough newlygenerated coded symbols for reconstructing the information sequence are received. Once the accumulated correctly-received coded sequence length exceeds the threshold, satisfactory error rate performance can be guaranteed [1]. However, the RaptorQ decoder is too complicated owing to the requirement of inversion of a huge matrix [2]. A matrix inverse with dimension up to 2 16 is required, accounting for 92% of the total decoding complexity [3]. Kim et al. [4] reduces the complexity of such matrix inversion for the Raptor code [5], the previous version of the RaptorQ code. The Raptor and RaptorQ codes are nearly identical except that some entries in the RaptorQ code generator matrix are octet, while the Raptor code only uses binary values to construct its code generator matrix. In this paper, we propose an index-based maximum likelihood (ML) RaptorQ code decoder based on the two-stage decoder structure in [4]. However, since the RaptorQ code generator matrix contains entries with octet values, the complexity of the RaptorQ decoder is significantly higher than that of the Raptor code. To mitigate this issue, we first partition these two stages properly so that most computations remain binary. Then, instead of storing the complete code generator matrix [3], we propose to tabulate the indices of the nonzero binary entries and then perform all the corresponding binary operations based on these indices. Owing to the sparse property of the code generator matrix, the number of the tabulated indices is much less than the dimension of the code generator matrix, leading to significant complexity reduction. From our analysis, the computational complexity of the proposed index-based ML decoder is only quadratic to the dimension of the code generator matrix, while the implementation of [4] for RaptorQ code needs cubic complexity. Simulation results demonstrate that when the code generator matrix is large, only about onetenth computational complexity and one-hundredth memory are respectively required when compared with [4]. 2 Preliminary knowledge of the RaptorQ decoder Before introducing the proposed index-based ML RaptorQ decoder, this section reviews the concept of ML decoding algorithm for the Raptor code. ML decoding, as known as full rank decoding, is performed by solving a set of linear equation, since each coded symbol y is a linear combination of the M 1 source symbols x by using " # y ¼ A 1 A 2 x; ð1þ DOI: /comex Received June 12, 2015 Accepted June 30, 2015 Published July 28, 2015 where A 1 is an M M code generator matrix for x. Then, y is infinitely generated by the code generator matrix ½A 1 > A 2 > Š >, where A 2 is generated by a designated 234

13 IEICE Communications Express, Vol.4, No.7, random generator. By removing the erasures from the coded symbols, the received symbols are denoted as y. When the numbers of received symbols reaches M, the receiver acknowledge the transmitter to stop sending new coded symbols. The decoder then recover the source symbols by computing the inverse of the corresponding code generator matrix D x ¼ D 1 y; where it will be successful if and only if D is full rank. Since the positions and the number of erasures are random and depend on transmission, D 1 has to be computed on-line. For more details, interested readers are referred to [1]. 2.1 Gaussian Elimination (GE) The equation in (2) is commonly solved by GE, a recursive algorithm comprising the row/column exchanges and row operations. The step-by-step operation of GE in the ith iteration step is given by Pivot row identification: The pivot row possessed the fewest non-zero entries is identified and exchanged with the ith row vector, Pivot column identification: Denote the first non-zero entry in the pivot row vector (the ith row currently) as the pivot entry and the corresponding column as the pivot column. Put the pivot entry to the diagonal line by the column exchange. Normalize the entry values in the pivot row vector by the value of the pivot entry, Forward elimination: Use the pivot row vector (the ith row currently) to null the pth entries in the pivot column (the ith column currently) by the row operation, ði þ 1Þ p M, Backward elimination: Use the pivot row vector to null the qth entries in the pivot column by the row operation, 1 q ði 1Þ. Increase the value of i by 1. If i M, repeat steps from 1) to 4); otherwise, the GE procedure is terminated. With the GE procedure, D is diagonalized, leading to the solution of D 1. Since the complexity of each iteration in GE is OðM 2 Þ due to the pivot row identification and the forward/backward eliminations, the complexity of the GE including M iterations is OðM 3 Þ. ð2þ DOI: /comex Received June 12, 2015 Accepted June 30, 2015 Published July 28, Two-stage Raptor ML decoding algorithm Instead of using GE, the low-complexity Raptor ML decoder in [4] splits the diagonalization of D into two stages that respectively adopted a modified GE (MGE) and GE. MGE is first proposed in the 3GPP standard [1] and tailored for the Raptor code generator matrix. MGE is different from GE in that the fourth step backward elimination is revised to Column inactivation: Except the pivot column, all columns with non-zero entries in the ith position are moved to the rightmost part of the matrix. Thus, the MGE is simpler than the GE since all the row operations involving additions and subtractions in backward elimination are substituted by simple column exchanges. However, please note that instead of diagonalization, MGE 235

14 IEICE Communications Express, Vol.4, No.7, Fig. 1. A set of two subfigures: (a) Matrix D during the first stage. (b) Matrix D after the first stage. generates a matrix with non-zero entries located in the diagonal line and the rightmost columns. The two-stage Raptor decoder operates as follows: In the first stage, MGE upper triangluarizes matrix D. The columns shifted to the rightmost part are grouped and defined as U, whose column number increases as MGE operates, as shown in Fig. 1(a). The matrix D after the first stage is illustrated in Fig. 1(b), where the left-upper and left-bottom parts respectively become a diagonal matrix and a null matrix, while the rightmost part is a dense matrix since many non-zero entries are included in U by the column inactivation. Then, the dense matrix U is processed by GE in the second stage so as to fully diagonalize the matrix. By using the low-complexity MGE to process most of the columns in the first stage, more than 90% of the overall complexity is reduced [4]. 3 Index-based ML RaptorQ decoder DOI: /comex Received June 12, 2015 Accepted June 30, 2015 Published July 28, 2015 The major difference of the Raptor and RaptorQ codes lies in the fact that some entries in the RaptorQ code generator matrix are octet. Based on the Raptor decoder introduced in the previous section, our proposed RaptorQ decoder also consists of two stages. In the first stage, the row vectors with octet entries are excluded from the candidates of the pivot rows, in order to make most computations of the MGE remain binary. An index table is then constructed to store all non-zero binary entries in D. For instance, for the row vector [ ], the indices stored in the table are [1, 2, 4, ]. Since the column/row exchange and the row operation for binary entries in the first stage are translated into the update of the index table, the computations in the first stages are greatly simplified. Specifically, considering the exchange of the mth and the nth columns, all the binary non-zero entries in the row vectors can be quickly identified by the index table. Then, only the row vector with different values of the mth and the nth entries need to update the index in the table by changing m to n, and vice versa. When the values of the mth and the nth entries are identical, either zero or one, the index table remains the same. For the exchange of the row vectors, we only need to interchange their indices stored in the table. Last, the row operation can be done by first checking which row vector has nonzero value in the same position as the position of the pivot entry, i.e., the index i in the ith iteration. Then, these row vectors are updated by removing those indices that also exist in the pivot row, and adding new indices that only exist in the pivot row. For example, for the pivot row with tabulated indices [2 4 7] which 2 is the 236

15 IEICE Communications Express, Vol.4, No.7, Fig. 2. Average runtime vs. sequence length for various decoding algorithms. The numbers in the boxes are the slopes of the regression lines for various algorithms. position of the pivot entry, the row vector with tabulated indices [2 4 6] is updated to [6 7] after the row operation. Last, due to the sparsity of D, the size of the index table is much smaller than M 2, and the computations row/column exchange and row operation are thus reduced from processing M entries to only a few indices. Consequently, the computational complexity of the MGE is reduced from OðM 3 Þ to OðM 2 Þ. 4 Simulation results DOI: /comex Received June 12, 2015 Accepted June 30, 2015 Published July 28, 2015 The computational complexity comparisons are depicted in Fig. 2 by measuring the average runtime of the Matlab code with the Intel(R) i GHz. The dashed lines are the linear regressions of the numerical results of the decoding algorithms. We can see that while the two reference algorithms have complexity of OðM 3 Þ, the proposed algorithm shows a complexity OðM 2 Þ. Thus, the complexity saving of the index-based ML decoder is more pronounced for large M. Only when M<1200, the index-based ML decoder is inferior since each row vector stores different numbers of indices, leading to the overhead of the irregular memory configuration and access. Nevertheless, this overhead is negligible for large M where up to ten times computational complexity saving is achieved. Memory is precious resource in a complicated decoder. We compare the memory requirement by the three decoder algorithms and show the comparison results in Fig. 3. Unlike the reference algorithms that use one bit and eight bits to store all entry values of the code generator matrix, the proposed algorithm keeps only the indices of nonzero binary entries and thus its memory requirement is drastically reduced. More than hundred times saving is achieved for the cases with large sequence length. 237

16 IEICE Communications Express, Vol.4, No.7, Fig. 3. Memory requirement vs. sequence length for various decoding algorithms. 5 Conclusion In this paper, we develop the first decoder for the RaptorQ code with the octet code generator matrix. By properly partitioning the decoding process, most operations are performed with binary values. Together with the proposed index table that saves the positions of the binary entries, our proposed index-based ML RaptorQ decoder consumes only one-tenth computational complexity and one-hundredth memory, when compared with the decoders that directly extend the algorithms for Raptor code decoders. Acknowledgments This work was supported in part by Ministry of Science and Technology, Taiwan (R.O.C.) under Grant no. NSC MOST E DOI: /comex Received June 12, 2015 Accepted June 30, 2015 Published July 28,

17 IEICE Communications Express, Vol.4, No.7, Two-stage SPIT detection scheme with betweenness centrality and social trust Miho Kurata a), Kentaroh Toyoda a), and Iwao Sasase Dept. of Information and Computer Science, Keio University, Hiyoshi, Kohoku, Yokohama, Kanagawa , Japan a) toyoda+kurata@sasase.ics.keio.ac.jp Abstract: Detecting SPIT (Spam over Internet Telephony) is an urgent demand in voice communication services. In this paper, we propose a twostage SPIT detection scheme using BC (Betweenness Centrality) and social trust to decrease misdetection of a call from low-frequent users as SPIT. BC indicates user s centrality in the entire network and the BC against legitimate users gradually increases with time even if users seldom call. We first use BC to identify a call request from a low-frequent user then judge the call legitimacy by using social trust. By the computer simulation, we show that our scheme improves the detection accuracy. Keywords: SPIT detection, VoIP, security, social trust Classification: Internet References [1] A. D. Keromytis, A comprehensive survey of voice over IP security research, IEEE Comm. Surv. and Tutor., vol. 14, no. 2, pp , DOI: / SURV [2] J. Seedorf, N. D Heureuse, S. Niccolini, and M. Cornolti, Detecting trustworthy real-time communications using a Web-of-Trust, IEEE GLOBECOM, pp. 1 8, DOI: /GLOCOM [3] T. Kusumoto, E. Y. Chen, and M. Itoh, Using call patterns to detect unwanted communication callers, IEEE/IPSJ International Symposium on Applications and the Internet (SAINT), pp , DOI: /SAINT [4] M. A. Azad and R. Morla, Caller-REP: Detecting unwanted calls with caller social strength, Comput. Secur., vol. 39, Part B, pp , DOI: /j.cose [5] N. Chaisamran, T. Okuda, and S. Yamaguchi, Trust-based VoIP spam detection based on calling behaviors and human relationships, J. Inf. Process., vol. 21, no. 2, pp , DOI: /ipsjjip [6] U. Brandes, A faster algorithm for betweenness centrality, J. Math. Sociol., vol. 25, no. 2, pp , DOI: / X Introduction DOI: /comex Received June 7, 2015 Accepted July 2, 2015 Published July 31, 2015 Various voice communication services are getting popular with the growing smartphone market. However, it is reported that malicious users or companies 239

18 IEICE Communications Express, Vol.4, No.7, may abuse them for advertisement or fraud, which is called SPIT (Spam over Internet Telephony) [1]. Hence, detecting SPIT calls or spammers in voice communication services is an urgent demand for the service providers. In particular, a social trust-based approach is receiving much attention due to growing SNS-based voice communication services. A social trust-based approach judges the legitimacy of a call with a trust value calculated from caller-callee relationships [2, 3, 4, 5]. We especially pay attention to the scheme [5] since it can correctly classify unknown users. This scheme uses the call duration as the trust value and the longer a user calls to a callee, the higher trust value the callee gives to the caller. However we notice that the scheme [5] raises false alarms for low-frequent legitimate users as time goes on. That is, the trust value of low-frequent users gradually decreases since they seldom receive calls. In this paper, we propose a two-stage SPIT detection scheme with BC (Betweenness Centrality) and social trust. We use BC to allow a call request from a low-frequent legitimate user at the first stage. And then we judge the legitimacy of a call by using the conventional scheme [5]. BC indicates how much a user is gone through shortest paths between paths among other users. The intuition behind utilizing BC is that spammers call towards users while they seldom receive calls and thus spammers tend to be isolated at the edge of the entire network. On the other hand, the value of BC against legitimate users gradually increases with time even if users seldom call. By the computer simulation, we show the legitimacy of introducing BC and also clarify that our scheme improves the detection accuracy. 2 System model We define a voice-based spammer as the attacker model and the aims of spammers are advertisement, voice phishing, and illegal sales. Spammers call towards randomly chosen users but they seldom receive calls from others. The call frequency is higher than that of legitimate users. Since the contents of SPIT seems to irritate ordinary users, the call duration tends to be much shorter than that of legitimate users. We assume that a SPIT detection system is deployed in a voice communication service provider and its task is to judge whether a call request should be established or not when receiving a call request from a user. We assume that as many as N user users (including both legitimate users and spammers) in the service provider and the system can access to users CDR (Call Detail Records) and buddy lists (friend lists) for the inspection. 3 Conventional scheme DOI: /comex Received June 7, 2015 Accepted July 2, 2015 Published July 31, 2015 Chaisamran et al. propose a voice-based SPIT detection scheme with a social trust [5]. This scheme always allows a call from a user in the callee s buddy list. Otherwise, i.e., if a call is from an unknown user, the system judges the legitimacy of call with an inferred trust value calculated from trust values of other users. Since multiple paths between an unknown caller u and a callee v may exist, the system chooses the maximum inferred trust T u!v as shown in Eq. (1). 240

19 IEICE Communications Express, Vol.4, No.7, T u!v ¼ max T path p u!v ; ð1þ p2p u!v where P u!v denotes a set of paths between u and v and T path p u!v indicates an inferred trust value calculated between users in a path p and is represented as Eq. (2). T path p u!v ¼ Y i2p T iðtþ : ð2þ In order to make a trust value reliable, each user is assigned a trust value from his/ her friend depending on the cumulative call duration. This will give a low trust value for spammers since they seldom receive calls. More specifically, a user i in the path p has its own trust value at time t as Eq. (3). T iðtþ ¼ R iðtþ þð1 ÞT iðt 1Þ ; where α denotes a weight variable ð 2½0; 1ŠÞ and a raw trust value R iðtþ at time t is represented as Eq. (4). C vðtþ Y n C j¼1 jðtþ ð3þ R iðtþ ¼ 1 ; ð4þ n where C jðtþ denotes the cumulative call duration that a user j calls to user v and n denotes the number of v s friends, respectively. Finally, the system compares the inferred trust value T u!v and the pre-defined threshold T th.ift u!v >T th, the system establishes a call request from a user u to v. Otherwise, it rejects a call request. 3.1 Shortcomings of the conventional scheme We argue that the scheme [5] raises false alarms for low-frequent legitimate users as time goes on. That is, the trust value of low-frequent users gradually decreases since they seldom receive calls. 4 Proposed scheme Here, we propose a two-stage SPIT detection scheme with BC and social trust in order for the system to correctly identify a call request from low-frequent legitimate users as a legitimate one. We use BC as a feature to allow a call from low-frequent users at the first stage. After that, we judge the legitimacy of a call by using the social trust-based approach [5]. DOI: /comex Received June 7, 2015 Accepted July 2, 2015 Published July 31, Introduction of BC In graph theory, BC indicates a user s centrality in the social network [6]. Formally, BC is defined as the ratio of the number of shortest paths from all users to all others that pass through that user. Let st denote the number of shortest paths from s 2 U to t 2 U, where U denotes a set of users in the entire network. Let st ðuþ denote the number of shortest paths from s 2 U to t 2 U that pass through u 2 U. By using st and st ðuþ, BCðuÞ, which is the BC of a user u, can be represented as Eq. (5). BCðuÞ ¼ X s u t2u st ðuþ st : ð5þ 241

20 IEICE Communications Express, Vol.4, No.7, Fig. 1. Toy example of SNS that consists of five users. The above description is rigid and difficult to understand why Eq. (5) indicates the centrality. Hence we calculate BC with an SNS that consists of five users with Fig. 1. In this example, we calculate two users BC, which are u 1 and u 3 and they are represented as BCðu 1 Þ¼ 2;3ðu 1 Þ 2;3 þ 2;4ðu 1 Þ 2;4 þ 2;5ðu 1 Þ 2;5 þ 3;4ðu 1 Þ 3;4 0 1 þ 0 1 þ 0 1 þ 0 1 þ 0 1 þ 0 1 ¼ 0 and BCðu 3Þ¼ 0 1 þ 1 1 þ 1 1 þ 1 1 þ 1 1 þ 0 1 þ 3;5ðu 1 Þ 3;5 ¼ ¼ 4. Therefore, þ 4;5ðu 1 Þ 4;5 BCðu 3 Þ > BCðu 1 Þ and thus the user u 3 is located more central than the user u 1. This matches the fact that the user u 1 is located at the edge of the entire network while the user u 3 is located in the center of the network in Fig. 1. We argue that BC for spammers does not increase. Since spammers call but seldom receive calls, they tend to be isolated at the edge of the entire network and hardly go through the shortest paths between users. Hence the numerator of Eq. (5) for spammers does not increase well. On the other hand, BC for legitimate users gradually increases with time even if users seldom call. This is because legitimate users gradually make connection with legitimate users and the number of paths that go through legitimate users increases. Therefore, the numerator in Eq. (5) for legitimate users gradually increases. Spammers may collude each other to increase the value of their BC, which is so-called Sybil attack. Although they can locally increase their BC, it is difficult to globally increase it. Hence spammers should account for large part of entire user to succeed the Sybil attack. In reality, spammers would be less compared to the legitimate users and thus BC for spammers is still small even if they collude. 4.2 Algorithm When receiving a call establish request from a caller u, the server first checks whether the caller u is in the callee s buddy list. If the caller is in callee s buddy list, they are assumed to be friends and thus the system establishes a call request. Otherwise, the system proceeds to the first detection stage that checks whether his/ her BCðuÞ is bigger than a pre-defined threshold BC th.ifbcðuþ > BC th, the system judges that the call is legitimate and establishes the call. Otherwise, the system calculates the inferred trust value T u!v by using Eqs. (1) (4) and checks whether the caller s inferred social trust from the callee vt u!v is bigger than a pre-defined threshold T th. If T u!v >T th, the system judges that the call is legitimate and establishes a call. Otherwise, the system rejects the call establishment request. DOI: /comex Received June 7, 2015 Accepted July 2, 2015 Published July 31,

21 IEICE Communications Express, Vol.4, No.7, Fig. 2. Characteristics of BC. Fig. 3. Detection accuracy. 5 Simulation results We evaluate the characteristics of BC and the detection accuracy by the computer simulation. We use the simulation parameters specified in [5] and set the other undefined parameters as N user ¼ 1;000 and BC th ¼ Characteristics of BC Fig. 2(a) shows the boxplot of an average BC values against legitimate callers and spammers when five months passed. As we can see from Fig. 2(a), BC for spammers is concentrated around 0 while BC for legitimate callers mostly ranges from 50 to 200. Fig. 2(b) shows the average BC for legitimate users with time. As we can see from Fig. 2(b), the BC for legitimate users gradually increases with time. DOI: /comex Received June 7, 2015 Accepted July 2, 2015 Published July 31, Detection accuracy Fig. 3(a) and Fig. 3(b) show the true positive rate and false positive rate versus elapsed time. The true positive rate denotes the ratio of correctly identified calls from spammers while the false positive rate is the ratio of mistakenly identified calls from legitimate users, respectively. We first discuss the true positive rate. From Fig. 3(a), we confirm that our scheme does not degrade the true positive rate. We then discuss the false positive rate. In Fig. 3(b), the false positive rate against the conventional scheme is getting worse with time. On the other hand, the false positive rate of the proposed scheme is within 2% and does not degrade with time. 243

22 IEICE Communications Express, Vol.4, No.7, The false positive rate seems to be irrespective of the ratio of spammers. This is because the scheme judges whether each call request (not caller ) is legitimate or not. From this result, we can say that the false positive rate can be remedied by using BC. 6 Conclusion We have pointed out that calls from low-frequent users are gradually identified as SPIT in the conventional scheme. To remedy this issue, we have proposed a twostage SPIT detection scheme with BC and social trust. By the computer simulation, it is shown that our scheme achieves low false positive rate (< 2%) without lowering true positive rate. Acknowledgments This work is partly supported by the Grant in Aid for Scientific Research (No ) from Ministry of Education, Sport, Science and Technology, Japan. DOI: /comex Received June 7, 2015 Accepted July 2, 2015 Published July 31,