Earticle

Digital twin (DT) technologies have been increasingly important and useful for wireless communications. In particular, to support soaring wireless traffic with limited frequency spectrum assigned to legacy cellular systems, efficient operation and management of wireless resources as well as preemptively prediction of future spectrum usage are crucially important. For such purpose, DT networks for current fourth-generation (4G) and fifth-generation (5G) networks are constructed in this paper, by simultaneously utilizing measurement data from user equipments (UEs) and geographical information and characteristics of 4G and 5G base stations (BSs) within a specific observation area. Representative case studies are provided to demonstrate the usefulness of DT enabled cellular network management and prediction. As a real or near real time DT application, the impact and benefit of dual connectivity and dynamic spectrum sharing between 4G and 5G networks are analyzed. As a non-real time DT application, long-term improvement of 5G networks such as densification of BSs, implementation of advanced multiple input and multiple output technologies, and assignment of additional spectrum are analyzed and compared.

The femtocell overlaid cellular network (FOCN) has been used to enhance the capacity of existing cellular systems. To obtain the desired system performance, both cross-tier interference and co-tier interference in an FOCN need to be managed. This paper proposes an interference management scheme that adaptively constructs a femtocell cluster, which is a group of femtocell base stations that share the same frequency band. The performance evaluation shows that the proposed scheme can enhance the performance of the macrocell-tier and maintain a greater signal to interference-plus-noise ratio than the outage level can for about 99% of femtocell users.

In this paper, optimal parameters of a hopping pilot beacon are analyzed in a CDMA mobile cellular network. The hopping pilot beacon is used for inter-frequency handoff. It can reduce the number of pilot beacons needed for the inter-frequency handoff by transmitting neighbor frequency pilots periodically through a pilot beacon. The optimal parameters for transmission time and period of the hopping pilot bacon are derived by mathematical approach. It is highly recommended that the optimal values for the hopping pilot beacon under various operation environments.

The cellular communication network factor (CCN) family proteins regulate many biological events such as angiogenesis, tumor growth, placentation, implantation, and embryogenesis. The expression and function of CCN1, CCN2, and CCN3 at the maternal-conceptus interface are established in humans and rodents, but little is known about the role of CCN4 to CCN6 in the reproductive organs in any other species. Several studies in transcriptome analysis in pigs have shown that the expression of CCN4 and CCN6 increases in the endometrium during early pregnancy. However, their expression, regulation, and function in the endometrium throughout the estrous cycle and pregnancy have not been fully understood in pigs. Thus, we determined the expression, localization, and regulation of CCN4 and CCN6 during the estrous cycle and at the maternal-conceptus interface in pigs. We found that the levels of CCN4, but not CCN6, changed during the estrous cycle. The levels of CCN4 were greater during mid- to late pregnancy than in the early stage, and the levels of CCN6 were greatest on Day 15 of pregnancy. CCN4 and CCN6 were detected in conceptus tissues during early pregnancy and in chorioallantoic tissues during the later stage of pregnancy. CCN4 mRNA was mainly localized to epithelial cells, CCN6 mRNAs to epithelial and stromal cells in the endometrium. In endometrial explant cultures, CCN4 expression was increased by progesterone, and CCN6 expression by interferon-γ. These results suggest that CCN4 and CCN6 may play roles in the establishment and maintenance of pregnancy by regulating the endometrial epithelial cell functions in pigs.

In normal cells, cell to cell contact at full cell confluency inhibits cell growth. However, if cell to cell contact is being dissociated, normal cells reach their end of life by the phagocytosis of macrophage via 'programmed cell death', named 'apoptosis'. On the other hand, in cancer cells (especially, cancer stem cells), cell to cell contact can not inhibit cell growth. As a result, cancer stem cells can grow beyond full cell confluency. When cancer cells reach full cell confluency, they either stay at confluence stage or must be made transition from epithelial to mesenchymal form by cadherin switch(antiapoptotic program). These behavioral patterns of cancer stem cells are called "EMT"[2,20]. The underlying causes for such difference have not been known well until now. But this difference is assumed to be occurred by loss of cellular differentiation and coordination of proliferation[5]. As approaching the reason of such disorders with mechano-molecular biology, it was predicted using a mechanobiological model that if cellular unbalanced force is caused between cell to ECM and cell to cell adhesion, the molecular crosstalk is disintegrated between E-cadherin and EGFR Signaling Networks in cell to cell contacts and the attributes such as morphological change of cancer cells, motility, invasion, anticancer drug resistance, etc are acquired, and they were established by experimental method[5]. This study is to report the research results for an idea that the neural network development is a meaningful method of prediction system on cancer behavior patterns through such methods.

본 연구는 스웨덴의 에릭슨텔레콤사의 일본시장 진입사례를 네트워크상에 점진적 단계과정의 위치개발모델을 개발하여 묘사․분석하는 것이다. 본 연구는 Ericsson Telecom, Ericsson Radio Systems (ERA), 그리고 일본의 Nippon Ericsson, Ericsson Toshiba의 경험적 데이터를 이용하여 사례연구를 수행하였다. 본 연구에서는 네트워크 위치 개발의 효율적 접근을 하기 위해 에릭슨사가 다양한 자원교환의 네트워크 관계를 구축함으로써 국제적으로 관련 산업간 전략적 위치를 점유한 것을 발견하였으며, 또한 Nippon Ericsson과 Ericsson Toshiba가 기업 간 특정 네트워크를 형성함에 있어 기술개발을 위한 특화된 통신시스템의 광범위한 영역을 포함시키는 네트워크 관계를 개발하는데 경험이 부족함을 밝혀냈다. 이러한 부족한 점을 극복하고자, 에릭슨사는 성공적 경영활동 수행을 위하여 강력한 정치적, 사회적 네트워크의 특성을 활용하였다. 이런 특성은 에릭슨사가 일본시장을 진입하는데 중요한 요소이며, 에릭슨사가 어떻게 시장에서 강한 위치를 성취할 수 있었는가에 본질적으로 영향을 미치는 정부의 산업정책이었다. 본연구의 결과를 통하여 도출한 시사점은 에릭슨 텔레콤사가 다양한 행위자들과의 네트워크 관계구축을 통하여 개발한 전략적 위치는 자회사를 설립하는데 있어 진출시장의 위치 선정뿐만 아니라 그에 관련된 통신시스템 생산과정에서도 혁신적인 공헌을 하였다.

The purpose of this study is to describe and analyze the case of Ericsson Telecom entering into the Japanese market. This is accomplished using empirical data on Ericsson Telecom and Ericsson Radio Systems (ERA) in Sweden, and Nippon Ericsson and Ericsson Toshiba in Japan. The study develops a model for an incremental staging process of developing a network position. In accordance with the network approach, Ericsson obtains a strategic position in a national industrial network by building various kinds of network relationships. This contributes to the establishment of the subsidiary’s market position and also to the importance of innovations of system and production processes within the Ericsson Telecom. The results suggest that, to overcome the lack of network relationship experience, Nippon Ericsson and Ericsson Toshiba form a specific kind of industrial network. This network includes a wide range of system specializations for developing their own technology. This is seen as an effective approach for reaching a network position. The exchanges are shaped both by the activity and resource interdependencies in the various actors’ strategies. The paper concludes that the strong political and social character of networks provides some significant aspects on how Ericsson could achieve the strong position associated with successful operations. Some such aspects are government and industrial policies.

본 논문에서는 신경회로망과 유전자 알고리즘을 이용하여 셀룰러 무선채널 할당을 위한 최적화 알고리즘을 제안하였다. 채널할당 과정을 채널할당 문제에 내포된 제한사항들을 나타내는 에너지함수의 최소화 과정으로 규정하였다. 채널간, 인접채널, 사이트간의 세 가지 제한사항이 고려되었다. 최적의 채널할당을 위하여 신경회로망을 이용한 방식에서는 강제적인 채널 할당 및 셀 순서 변화 등의 기법이 개발되었고 유전자 알고리즘 방식에서는 자료구조와 적절한 유전연산자를 개발하였다. 실험결과로서, 두 최적화 방법의 채널할당률을 나타내었고 그 결과들을 비교하였다.

In this paper, two optimization algorithms based on artificial neural networks and genetic algorithms are proposed for cellular radio channel assignment problems. The channel assignment process is characterized as minimization of the energy function which represents constraints of the channel assignment problems. All three constraints such as the co-channel constraint, the adjacent channel constraint and the co-site channel constraint are considered. In the neural networks approach, certain techniques such as the forced assignment and the changing cell order are developed, and in the genetic algorithms approach, data structure and proper genetic operators are developed to find optimal solutions, As simulation results, the convergence rates of the two approaches are presented and compared.

In this paper, we investigate the interference scenario where multiple Device-to-Device (D2D) pairs reuse the spectrum resources allocated to one cellular user, and propose a novel algorithm for improving the signal-to-interference-plus-noise ratio (SINR) of D2D communication. We call this D2D SINR improvement algorithm as DSIA, and design it from the perspective of precoding and decoding. With the DSIA, each D2D pair involved in the network can obtain the corresponding precoding and decoding vectors which make the SINR of its receiver being maximized. In order to clearly describe the design procedure of the proposed algorithm, we first formulate an optimization problem with multiple variables to maximize the SINR of each D2D receiver, then simplify the objective function through mathematical derivation and seek out the relationship between the optimized variables which can transform the optimization problem to one of solving nonlinear equations, and finally propose the DSIA to work out the nonlinear equations. Numerical results show that the DSIA will enable D2D to achieve significant performance gains in terms of SINR and bit-error rate (BER) compared with the traditional spectrum orthogonal scheme.

To address the convergence issue of power control algorithms, a number of algorithms have been developed hat shape the dynamics of up-link power control for cellular network. Power algorithms based on fixed point iterations can be accelerated by the use of various methods, one of the simplest being the use of Newton iterations, however, this method has the disadvantage which not only needs derivatives of the cost function but also may be weak to noisy nvironment. we showed performance of the power control schemes to solve the fixed point problem under static or stationary channel. They proved goof performance to solve the fixed point problem due to their predictor based optimal control and quadratic convergence rate. Here, we apply the proposed power control schemes to the problem of the dynamic channel or to dynamic time varying link gains. The rigorous simulation results demonstrated the validity of our approach.

본 논문은 LTE-Advanced 시스템에서 셀룰러 상향링크 자원을 재사용하여 단말간 직접 통신 (D2D)을 수행하는 방법에 대해서 다룬다. 기존 문헌들은 일반적으로 D2D 송신 단말이 SC-FDMA 기법을 적용하여 데이터를 전송하는 것을 가정한다. 그러나, 기존 방법에서는 단말 신호들 간의 간섭 신호를 제거할 수 있는 방법이 거의 없기 때문에, 셀룰러 상향링크 통신 신호가 D2D 링크에 미치는 간섭이 큰 경우에는 D2D 통신을 적용할 수 없게 된다. 본 논문에서는 이런 경우에도 D2D 통신이 가능하도록 하기 위하여 D2D 송신 단말이 다중반송파 주파수분할 다중접속 (MC-CDMA) 기술을 적용하여 데이터를 전송하는 것을 제안한다. 또한, D2D 수신 단말에서는 최소 평균 제곱오차 (MMSE) 수신기를 적용함으로써 간섭 신호를 제거하는 것을 제안한다. 모의실험을 통해서 제안하는 방법이 기존 방법보다 더 우수한 BER 성능을 갖는다는 것을 보인다.

We treat device-to-device direct communication (D2D) by reusing cellular uplink resources in LTE- Advanced systems. Conventional schemes assume that D2D transmitter employs SC-FDMA scheme for the date transmission. In the conventional schemes, however, it is very difficult to reduce the interference between the users, D2D communication cannot be used when the interference caused by cellular uplink signal is high. In this paper, we propose D2D transmission scheme which employes MC-CDMA technology and D2D detection scheme which uses MMSE algorithm to reduce interference from the cellular uplink signal. Therefore, in the proposed scheme the D2D communication becomes possible even in the high interference scenario. Through simulation, we show that the proposed scheme has better BER performance than the conventional scheme.

본 연구에서는 차세대 CDMA 셀룰러 네트워크의 성능향상을 위한 멀티 데이터 전송 레이트 조절과 전력제어 문제를 결합한 문제를 제시하고 유전 알고리즘 접근에 기반한 새로운 해법을 제안하였다. 본 연구에서 제안한 알고리즘으로 많은 사용자를 보다 빨리 서비스 할 수 있고 SIR 제한값을 고려하면서 최적의 해를 얻을 수 있었다. 이 연구에서는 CDMA 셀룰러 네트워크의 데이터 전송 레이트를 최대화하는 적합도 문제와 가능한 모바일의 수를 최대화하는 적합도 문제 두 개를 고려하였으나 이 들을 결합한 문제나 다른 것 들에 대한 적합도 함수도 충분히 장착이 가능하다. 시뮬레이션 결과는 접근 방식의 유효성과 타당성을 보여준다.

In this work, we present a novel genetic approach to solve the problem of combining power control and data rate transmission adjustment for the performance enhancement of the next generation CDMA system. We obtained the optimal solution of multi rate and power control problem by compromising slightly on SIR limit values. The proposed algorithm was able to handle many more users with comparable or faster convergent service. While this paper considered two kinds of fitness function such as maximizing the total transmission data rate and maximizing the acceptable mobiles of CDMA cellular network, the evaluation function combining these two cases or others can be also easily implemented. The simulation results showed the effectiveness and validity of our approach.

A Location Register(LR) system manages each subscriber’s location information, which continuously changes in mobile cellular network. For this purpose, the LR database system provides backup management facilities. The objectives of this paper are to identify the problems of the current LR system through rigorous analysis, to suggest solutions to them. I proposed efficient backup methods that takes into account the characteristics of LR database transactions. In the proposed backup method, I use two kinds of dirty flags in order to solve the performance degradation problem caused by frequent registration- location operations.

Wi-Fi positioning system is widely being studied in many fields of smartphones. In an open air environment a GPS receiver is able to determine its position with a high level accuracy. But in inside a building, where the GPS signal is bad or unavailable or unappreciated. The position estimation from the GPS receiver is very erroneous and has no practical use. Wi-Fi triangulation system is applied in indoor location but faced signal coverage error. Therefore, we propose a method of indoor positioning that determines the user's current position with the nearby Wi-Fi routers and cellular network signal strength RSSI values. This Signal strength RSSI estimates the location fingerprints based on at least two wireless Access Points and an alternative Cellular network or three access points. The cellular network is an alternative backup of unreached Wi-Fi Access point where this Access point’s coverage is unknown or sparse. Predefined points are used to measure distance from a user could also generate advance level accuracy. Referring to the floor map, our method adjusts the real-time user's current location. Gravity sensor works on the back end to control Wi-Fi scanning enable or disable considering user movement for energy efficiency. We have implemented the proposed method on Android smart phones and our test results are discussed in this paper.

In this paper, we discuss joint detection schemes for an uplink cellular system when Base Station (BS) cooperation is possible for multi-cell users in a multi-cell scenario. Further, we analyze and evaluate the ML, ZF and ZF based SIC detection schemes. Although ML attains optimal performance, its complexity increases exponentially. Further, although ZF is simple, but exhibits the poor performance and SIC may give rise to error propagation. We propose a novel detection scheme, which combines the ZF detection and partial ML decoding scheme in order to improve the detection performance as well as to decrease the decoder complexity. Simulation results show that the proposed scheme attains nearly 2 dB reduction in the required SNR values to achieve the same BER performance. Further, the proposed scheme can be applied to MIMO detection for a single user and extended to other types of multi-user and multi-antenna based future smart BS cooperation.

Vehicle License Plate Images Segmentation is a substantial stage for developing an Automatic License Plate Recognition (ALPR) system. In this paper, it is considered an efficient segmentation algorithm for extracting vehicle license plate images using Cellular Neural Networks (CNN). The learning CNN templates values are formulated as an optimization problem to achieve the desired performances which can be found by means of Adaptive Fuzzy (AF) algorithm and Neuro-Fuzzy (NF) algorithm techniques. The main objective of the paper is to compare the performances of standard CNN, Adaptive Fuzzy (AF), and Neuro-Fuzzy (NF) on real data of several vehicle license plate images of standard Indonesia License Plates. The results are then compared with ideal vehicle license plate images. Quantitative analysis between ideal vehicle license plate images and segmented vehicle license plate images is presented in terms of Peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). From the performance analysis, the CNN template optimized by ANFIS algorithm is more recommended than the standard CNN edge detector or the CNN template optimized by Adaptive Fuzzy algorithm in vehicle license plate image segmentation. It is shown from the calculation that PSNR is 80% better than the standard CNN, and the resulted MSE and RMSE are 70% better than the standard CNN. Whereas the CNN template optimized by Adaptive Fuzzy algorithm achieves the PSNR 90% better than the standard CNN, but it yields the MSE and RMSE 40% worse than the standard CNN.

The engineering of the desired phenotypes in organisms of interest is a major goal in biotechnology and basic research towards engineering complex biological systems and understanding their natural counterparts. For the phenotypic engineering of microorganisms, we have developed novel artificial transcription factors (ATFs) capable of reprogramming innate gene expression circuits in Escherichia coli. These ATFs are composed of zinc finger (ZF) DNA-binding proteins, with distinct specificities, fused to an E. coli cyclic AMP receptor protein (CRP). By randomly assembling 40 different types of ZFs, we have constructed more than 6.4 x 104 ATFs that consist of 3 ZF DNA-binding domains and a CRP effector domain. Using these ATFs, we induced various phenotypic changes in E. coli and selected for industrially important traits, such as resistance to heat shock, osmotic pressure, and cold shock. Genes associated with the heat-shock resistance phenotype were then characterized. These results and the general applicability of this platform clearly indicate that novel ATFs are powerful tools for the engineering of complex regulatory transcription networks in microorganisms and can elicit new phenotypes in E. coli.