Archive

  • Select all
    |
    FEATURE TOPIC: CHAOS-BASED SECURE COMMUNICATIONS
  • FEATURE TOPIC: CHAOS-BASED SECURE COMMUNICATIONS
    Mengmeng Chen, Mingjiang Zhang, Shaoxiang Chen, Jianguo Zhang, Senlin Yan, Yuncai Wang
    2020, 17(5): 1-11.
    Abstract ( )   Knowledge map   Save
    A novel chaotic optical time-domain reflectometry (OTDR)-based approach was proposed for monitoring long-haul fiber communication systems with multiple fiber segments. The self-phase modulation and group velocity dispersion effects of the optical cable was considered in demonstrating the proof-of-concept experiment and simulation. In experiments, the correlation peaks are clearly obtained from the correlation trace between the reference and reflected (or scattered) light signals propagating in three optical-fiber segments. The technique affords a high spatial resolution of 2 m, and further long-haul fiber simulations indicate that the sensing distance can be more than 3300 km. Thus, the new proposed technique can be effectively applied for health monitoring of long-haul fiber communication systems.
  • FEATURE TOPIC: CHAOS-BASED SECURE COMMUNICATIONS
    Cengfei Chen, Kehui Sun*, Qiaoyun Xu
    2020, 17(5): 12-20.
    Abstract ( )   Knowledge map   Save
    To ensure the safe transmission of image information in communication, and improve the security performance of image encryption algorithm, we proposed a color image encryption algorithm with higher security based on chaotic system. Firstly, the 2-dimensional Cubic ICMIC modulation map (2D-CIMM) is designed, which has simple form, easy to construct, and high Spectral Entropy (SE) complexity. Secondly, the hash values of the original image are used to update the initial values of the 2D-CIMM map in real time, which increases the sensitivity of the image encryption algorithm to the plaintext and improves the finite precision effect. Finally, the permutation and diffusion processes of the encryption algorithm based on bit-level are performed. In addition, simulation and performance analysis show that the proposed algorithm has higher security.
  • FEATURE TOPIC: CHAOS-BASED SECURE COMMUNICATIONS
    Feifei Yang, Jun Mou*, Yinghong Cao, Ran Chu
    2020, 17(5): 21-28.
    Abstract ( )   Knowledge map   Save
    To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication.
  • FEATURE TOPIC: CHAOS-BASED SECURE COMMUNICATIONS
    Bin Lu, Xin Ge, Fenlin Liu
    2020, 17(5): 29-37.
    Abstract ( )   Knowledge map   Save
    A round function based on chaos is designed combining Feistel structure’s pseudo-randomness, chaotic system’s parameter sensitivity and image data characteristics. The round function composes of two parts--data transformation based on Feistel (abbreviated as FST) and sampling output based on chaos (abbreviated as SMP). FST bases on Feistel structure and several efficient operations including bitwise xor, permutation and circulating shift. SMP is a chaos based pseudo-random sampling algorithm. It is from theoretical analysis that the round function is a pseudo-random function. The upper bounds of the average maximum differential probability and average maximum linear probability are p2 and q2 respectively. Finally, the good pseudo-randomness of the round function is examined with the NIST random test. The design of this round function provides an important cryptographic component for the design of chaotic image encryption algorithm.
  • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Fatang Chen, Xiu Li, Yun Zhang, Yanan Jiang
    2020, 17(5): 38-49.
    Abstract ( )   Knowledge map   Save
    The initial cell search plays an important role during the process of downlink synchronization establishment between the User Equipment (UE) and the base station. In particular, the uncertainty of the synchronization signals on the frequency domain and the flexibility of frame structure configuration have brought great challenges to the initial cell search for the fifth-generation (5G) new radio (NR). To solve this problem, firstly, we analyze the physical layer frame structure of 5G NR systems. Then, by focusing on the knowledge of synchronization signals, the 5G NR cell search process is designed, and the primary synchronization signal (PSS) timing synchronization algorithm is proposed, including a 5G-based coarse synchronization algorithm and conjugate symmetry-based fine synchronization algorithm. Finally, the performance of the proposed cell search algorithm in 5G NR systems is verified through the combination of Digital Signal Processing (DSP) and personal computer (PC). And the MATLAB simulation proves that the proposed algorithm has better performance than the conventional cross-correlation algorithm when a certain frequency offset exists.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Hany S. Hussein, Shaimaa Hussein, Ehab Mahmoud Mohamed
    2020, 17(5): 50-64.
    Abstract ( )   Knowledge map   Save
    With a low resolution 1-bit ADC on its receiver (RX) side, MIMO with 1-bit ADC took a considerable step in the fulfillment of the hardware complexity constrains of the internet of things (IoT) PHY layer design. However, applying 1-bit ADC at MIMO RX results in severe nonlinear quantization error. By which, almost all received signal amplitude information is completely distorted. Thus, MIMO channel estimation is considered as a major barrier towards practical realization of 1-bit ADC MIMO system. In this paper, two efficient sparsity-based channel estimation techniques are proposed for 1-bit ADC MIMO systems, namely the low complexity sparsity-based channel estimation (LCSCE), and the iterative adaptive sparsity channel estimation (IASCE). In these techniques, the sparsity of the 1-bit ADC MIMO channel is exploited to propose a new adaptive and iterative compressive sensing (CS) recovery algorithm to handle the 1-bit ADC quantization effect. The proposed algorithms are tested with the state-of-the-art 1-bit ADC MIMO constant envelope modulation (MIMO-CEM). The 1-bit ADC MIMO-CEM system is chosen as it fulfills both energy and hardware complexity constraints of the IoT PHY layer. Simulation results reveal the high effectiveness of the proposed algorithms in terms of spectral efficiency (SE) and computational complexity. The proposed LCSCE reduces the computational complexity of the 1-bit ADC MIMO-CEM channel estimation by 86%, while the IASCE reduces it by 96% compared to the recent techniques of MIMO-CEM channel estimation. Moreover, the proposed LCSCE and IASCE improve the spectrum efficiency by 76 % and 73 %, respectively, compared to the recent techniques.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Xiumin Wang, Rui Gu, Jun Li, Qiangqiang Ma
    2020, 17(5): 65-77.
    Abstract ( )   Knowledge map   Save
    Soft-cancellation (SCAN) is a soft output iterative algorithm widely used in polar decoding. This algorithm has better decoding performance than reduced latency soft-cancellation (RLSC) algorithm, which can effectively reduce the decoding delay of SCAN algorithm by 50% but has obvious performance loss. A modified reduced latency soft-cancellation (MRLSC) algorithm is presented in the paper. Compared with RLSC algorithm, LLR information storage required in MRLSC algorithm can be reduced by about 50%, and better decoding performance can be achieved with only a small increase in decoding delay. The simulation results show that MRLSC algorithm can achieve a maximum block error rate (BLER) performance gain of about 0.4dB compared with RLSC algorithm when code length is 2048. At the same time, compared with the performance of several other algorithms under (1024, 512) polar codes, the results show that the throughput of proposed MRLSC algorithm has the advantage at the low and medium signal-to-noise ratio (SNR) and better BLER performance at the high SNR.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Zhanxian Liu, Rongke Liu*, Ling Zhao
    2020, 17(5): 78-88.
    Abstract ( )   Knowledge map   Save
    In this paper, we present a graphics processing unit (GPU)-based implementation of a weighted bit-reliability based (wBRB) decoder for non-binary LDPC (NB-LDPC) codes. To achieve coalesced memory accesses, an efficient data structure for the wBRB algorithm is proposed. Based on the Single-Instruction Multiple-Threads (SIMT) programming model, a novel mapping strategy with high intra-frame parallelism is presented to improve the latency and throughput performance. Moreover, by using Single-Instruction Multiple-Data (SIMD) intrinsics, four 8-bit message elements are packed into a 32-bit unit and simultaneously processed. Experimental results show that the proposed wBRB decoder provides good tradeoff between error performance and throughput for the codes with relatively large column degrees or high rates.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Farzaneh Abedi, Mohammad Gholami
    2020, 17(5): 89-109.
    Abstract ( )   Knowledge map   Save
    Multi-type quasi-cyclic (QC) low-density parity-check (LDPC) codes can be considered as multiple-edge protograph QC-LDPC codes having some advantages in the minimum Hamming distance bound over single-edge protograph codes or type-I QC-LDPC codes when the base matrices have the same size. In this paper, we investigate a class of multi-type QC-LDPC codes whose parity-check matrices contain just one block-row of circulants and we obtain the generator matrix of such codes in general form. Using the permutation arrays and defining injection arrays, we present a new approach to construct a class of high-rate type-I QC-LDPC codes with girth 6 from the constructed 4-cycle free multi-type QC-LDPC codes. In continue, for , some type- QC-LDPC codes with girth 6 are constructed explicitly such that the constructed codes are flexible in terms of rate and length. To the best of our knowledge, for , this is the first paper which deals with the explicit construction of type- QC-LDPC codes with girth 6 and high rates. Moreover, for , the constructed type- QC-LDPC codes have better (6,8)-cycle multiplicities than the codes with minimum achievable length recently constructed by cyclic difference families (CDFs). Simulation results show that the binary and non-binary constructed codes outperform the constituent underlying QC-LDPC codes.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Meijuan Zhang, Shibing Zhang, Zhihua Bao, Wei Wang, Xiaoge Zhang, Yonghong Chen
    2020, 17(5): 110-118.
    Abstract ( )   Knowledge map   Save
    In this paper, we investigate a joint beamforming and time switching (TS) design for an energy-constrained cognitive two-way relay (TWR) network. In the network, the energy-constrained secondary user (SU) relay employs TS protocol to harvest energy from the signals sent by the circuit-powered primary user (PU) transmitter, and then exploits the harvested energy to perform information forwarding. Our aim is to maximize the sum rate of SUs under the constraints of the data rate of PU, the energy harvesting and the transmit power of the SU relay. To determine the beamforming matrix and TS ratio, we decouple the original non-convex problem into two subproblems which can be solved by semidefinite relaxation and successive convex optimization methods. Furthermore, we derive closed form expressions of the optimal solutions for each subproblem, which facilitates the design of a suboptimal iterative algorithm to handle the original sum rate maximization problem. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.
  • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    Peng Zhao, Qinpei Zhao, Chenxi Zhang, Gong Su, Qi Zhang, Weixiong Rao
    2020, 17(5): 119-136.
    Abstract ( )   Knowledge map   Save
    The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.
  • NETWORKS & SECURITY
    Linyuan Yao, Ping Dong, Hongke Zhang, Xiaojun Wang
    2020, 17(5): 137-150.
    Abstract ( )   Knowledge map   Save
    Static assignment of IP addresses or identifiers can be exploited by an adversary to attack a network. However, existing dynamic IP address assignment approaches suffer from two limitations, namely: participation of terminals in the assignment and inadequate network server management. Thus, in this paper, we propose an Overall-transparent Dynamic Identifier-mapping Mechanism (ODIM) to manage the identifier of network nodes to defend against scanning and worm propagation in the Smart Identifier NETwork (SINET). We establish the selection and allocation constraints, and present selection and allocation algorithms to determine the constraints. The non-repetition probability and cover cycle allow us to evaluate the defense efficiency against scanning. We propose the probability for routing identifiers and derive the defense efficiency of ODIM against worm propagation. Simulation results and theoretical analysis show that the proposed method effectively reduces the detection probability of Routing IDentifiers (RIDs) and thus improves defense capabilities against worm propagation.
  • NETWORKS & SECURITY
    Liangchen Chen, Shu Gao, Baoxu Liu, Zhigang Lu, Zhengwei Jiang
    2020, 17(5): 151-167.
    Abstract ( )   Knowledge map   Save
    Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor (FEW-NNN) method is proposed to enhance the accuracy and efficiency of flow-based network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm (NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1) the feature weights of samples are calculated based on fuzzy entropy values, 2) the fuzzy memberships of samples are determined based on affinity among samples, and 3) K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class; that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CIC-IDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.
  • NETWORKS & SECURITY
    M. G. Aruna, K. G. Mohan
    2020, 17(5): 168-190.
    Abstract ( )   Knowledge map   Save
    Cloud computing, a recently emerged paradigm faces major challenges in achieving the privacy of migrated data, network security, etc. Too many cryptographic technologies are raised to solve these issues based on identity, attributes and prediction algorithms yet; these techniques are highly prone to attackers. This would raise a need of an effective encryption technique, which would ensure secure data migration. With this scenario, our proposed methodology Efficient Probabilistic Public Key Encryption (EPPKE) is optimized with Covariance Matrix Adaptation Evolution Strategies (CMA-ES). It ensures data integrity through the Luhn algorithm with BLAKE 2b encapsulation. This enables an optimized security to the data which is migrated through cloud. The proposed methodology is implemented in Open Stack with Java Language. It achieves better results by providing security compared to other existing techniques like RSA, IBA, ABE, PBE, etc.
  • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Tianchu Zhao, Sheng Zhou, Linqi Song, Zhiyuan Jiang, Xueying Guo, Zhisheng Niu
    2020, 17(5): 191-210.
    Abstract ( )   Knowledge map   Save
    By Mobile Edge Computing (MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Fangmin Xu*, Fan Yang, Chenglin Zhao, Sheng Wu
    2020, 17(5): 211-222.
    Abstract ( )   Knowledge map   Save
    Due to the rapid development of the maritime networks, there has been a growing demand for computation-intensive applications which have various energy consumption, transmission bandwidth and computing latency requirements. Mobile edge computing (MEC) can efficiently minimize computational latency by offloading computation tasks by the terrestrial access network. In this work, we introduce a space-air-ground-sea integrated network architecture with edge and cloud computing components to provide flexible hybrid computing service for maritime service. In the integrated network, satellites and unmanned aerial vehicles (UAVs) provide the users with edge computing services and network access. Based on the architecture, the joint communication and computation resource allocation problem is modelled as a complex decision process, and a deep reinforcement learning based solution is designed to solve the complex optimization problem. Finally, numerical results verify that the proposed approach can improve the communication and computing efficiency greatly.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Jie Zeng, Jiaying Sun, Binwei Wu, Xin Su
    2020, 17(5): 223-234.
    Abstract ( )   Knowledge map   Save
    With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation (5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored to meet the requirements of ultra-reliable and low latency communications (URLLC) in the maritime communication network (MCN). Mobile edge computing (MEC) can achieve high energy efficiency in MCN at the cost of suffering from high control plane latency and low reliability. In terms of this issue, the mobile edge communications, computing, and caching (MEC3) technology is proposed to sink mobile computing, network control, and storage to the edge of the network. New methods that enable resource-efficient configurations and reduce redundant data transmissions can enable the reliable implementation of computing-intension and latency-sensitive applications. The key technologies of MEC3 to enable URLLC are analyzed and optimized in MCN. The best response-based offloading algorithm (BROA) is adopted to optimize task offloading. The simulation results show that the task latency can be decreased by 26.5’ ms, and the energy consumption in terminal users can be reduced to 66.6%.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Hai Huang, Junsheng Mu, Xiaojun Jing
    2020, 17(5): 235-242.
    Abstract ( )   Knowledge map   Save
    Cooperative spectrum sensing appears popular currently due to its ability to solve the issue of hidden terminal and improve detection performance in Cognitive Radio Networks. Meanwhile, double threshold based energy detector has attracted much attention for its low computational complexity and superior performance. Motivated by this, a cooperative spectrum sensing scheme is proposed in this paper based on centralized double threshold in the maritime communication networks (MCN), where the energy value of received signal in each cognitive node is forwarded to the fusion center for final decision based on double thresholds. Additionally, the proposed scheme is further optimized for the decisions that the energy is within the scope of maximum threshold and minimum threshold. Simulation experiments verify the performance of the proposed method.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Jiandong Xie, Sa Xiao, Ying-Chang Liang, Li Wang, Jun Fang
    2020, 17(5): 243-265.
    Abstract ( )   Knowledge map   Save
    In intelligent transportation system (ITS), the interworking of vehicular networks (VN) and cellular networks (CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading (MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process (SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio. Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Haibo Zhang*, Zixin Wang, Kaijian Liu
    2020, 17(5): 266-283.
    Abstract ( )   Knowledge map   Save
    As an important application scenario of 5G, the vehicular network has a huge amount of computing data, which brings challenges to the scarce network resources. Mobile edge computing (MEC) sinks cloud services to the edge of network, which reduces the delay jitter caused by remote cloud computing. Software-defined networking (SDN) is an emerging network paradigm with the features of logic centralized control and programmability. In this paper, we construct an SDN-assisted MEC network architecture for the vehicular network. By introducing SDN controller, the efficiency and flexibility of vehicular network are improved, and the network state can be perceived from the global perspective. To further reduce the system overhead, the problem of vehicle to everything (V2X) offloading and resource allocation is proposed, where the optimal offloading decision, transmission power control, subchannels assignment, and computing resource allocation scheme are given. The optimization problem is transformed into three stages because of the heterogeneity of the offloaded tasks and the NP-hard property of the problem. Firstly, the analytic hierarchy process is used to select initial offloading node, then stateless Q-learning is adopted to allocate transmission power, subchannels and computing resources. In addition, the offloading decision is modeled as a potential game, and the Nash equilibrium is proved by the potential function construction. Finally, the numerical results show that the proposed mechanism can effectively reduce the system overhead and achieve better results compared with others’ algorithms.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Ruiqin Bai, Jumin Zhao, Dengao Li, Xiaoyu Lv, Qiang Wang, Biaokai Zhu
    2020, 17(5): 284-294.
    Abstract ( )   Knowledge map   Save
    To provide more intelligence service in the smart library, we need to better perceive the reader’s preferences. In addition to perceiving online records based on readers' search history and borrowing records, advanced information technologies give us more chance to perceive the behavior of readers in the actual reading process and further discover the need for reading. In this paper, we use CRFID and RNN deep learning network to recognize book motions in the reading process, so as to judge readers' need degree for the book, which can provide a basis for library book purchases and readers personalized service. In order to improve the recognition accuracy, we use the RSS as well as acceleration magnitude gathered from CRFID as the input data for RNN, and design a new encoding scheme. We trained and tested the deep learning network using real-world data, recorded during actual reading in our lab environment which mimics a typical reading room, from the experimental results, we conclude that our approach is feasible to recognize different reading phase to perceiving the needs of the readers.