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    COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    LPR-Trie: A Fast IPv6 Routing Lookup Algorithm with Virtual Nodes
    Wenlong Chen, Diya Liu, Jiacheng Wang, Xiaolan Tang
    2022, 19(10): 1-11.
    Abstract ( )   Knowledge map   Save
    The number of IPv6 routes in todays backbone routers has grown rapidly, which has put tremendous pressure on route lookup and storage. Based on the analysis of IPv6 address prefix length and distribution characteristics, this paper proposes an IPv6 route lookup architecture called LPR-Trie. The core idea of the algorithm is to utilize more spaces and accelerate routing lookup. Moreover, we put forward the concept of virtual nodes, and leverage the link between virtual nodes and ordinary nodes to accelerate routing lookup. We provide the longest prefix routing entry (LPR) calculation algorithm to achieve the longest prefix match. The experimental results show that the virtual node mechanism increases the search speed up to 244%, and the virtual nodes have better stability by setting an appropriate keep-alive time according to the characteristics of actual traffic. This paper shows that our design improves the routing lookup speed and have better memory utilization.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks
    Yunus Dursun, Fang Fang, Zhiguo Ding
    2022, 19(10): 12-20.
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    Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Channel Capacity Optimization Based on Riemannian Trust Region Algorithm in IRS-Aided Communication System
    Jinzhi Liu, Dan Wang, Jiamin Liang, Zhiqiang Mei
    2022, 19(10): 21-37.
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    Intelligent Reflecting Surface (IRS) can offer unprecedented channel capacity gains since it can reconfigure the signal propagation environment. We decide to maximize the channel capacity by jointly optimizing the transmit-power-constrained precoding matrix at the base station and the unit-modulus-constrained phase shift vector at the IRS in IRS-assisted multi-user downlink communication. We first convert the resulting non-convex problem into an equivalent problem, then use the alternate optimization algorithm. While fixing the phase shift vector, we can obtain the optimal precoding matrix directly by adopting standard optimization packages. While fixing the precoding matrix, we propose the Riemannian Trust-Region (RTR) algorithm to solve this optimization problem. And the key of the RTR algorithm is the solution of the trust-region sub-problem. We first adopt the accurate solution based on Newton's (ASNT) method to solve this sub-problem, which can obtain the global solution but cannot guarantee that the solution is optimal since the initial iteration point is difficult to choose. Then, we propose the Improved-Polyline (IPL) method, which can avoid the difficulty of the ASNT method and improve convergence speed and calculation efficiency. The numerical results show that the RTR algorithm has more significant performance gains and faster convergence speed compared with the existing approaches.
  • COMMUNICATIONS THEORIES & SYSTEMS
    A Multi-Agent Reinforcement Learning-Based Collaborative Jamming System: Algorithm Design and Software-Defined Radio Implementation
    Luguang Wang, Fei Song, Gui Fang, Zhibin Feng, Wen Li, Yifan Xu, Chen Pan, Xiaojing Chu
    2022, 19(10): 38-54.
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    In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Capacity Analysis and Sum Rate Maximization for the SCMA Cellular Network Coexisting with D2D Communications
    Yukai Liu, Wen Chen
    2022, 19(10): 55-68.
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    Sparse code multiple access (SCMA) is the most concerning scheme among non-orthogonal multiple access (NOMA) technologies for 5G wireless communication new interface. Another efficient technique in 5G aimed to improve spectral efficiency for local communications is device-to-device (D2D) communications. Therefore, we utilize the SCMA cellular network coexisting with D2D communications for the connection demand of the Internet of things (IOT), and improve the system sum rate performance of the hybrid network. We first derive the information-theoretic expression of the capacity for all users and find the capacity bound of cellular users based on the mutual interference between cellular users and D2D users. Then we consider the power optimization problem for the cellular users and D2D users jointly to maximize the system sum rate. To tackle the non-convex optimization problem, we propose a geometric programming (GP) based iterative power allocation algorithm. Simulation results demonstrate that the proposed algorithm converges fast and well improves the sum rate performance.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Deep Learning-Based AMP for Massive MIMO Detection
    Yang Yang, Shaoping Chen, Xiqi Gao
    2022, 19(10): 69-77.
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    Low-complexity detectors play an essential role in massive multiple-input multiple-output (MIMO) transmissions. In this work, we discuss the perspectives of utilizing approximate message passing (AMP) algorithm to the detection of massive MIMO transmission. To this end, we need to efficiently reduce the divergence occurrence in AMP iterations and bridge the performance gap that AMP has from the optimum detector while making use of its advantage of low computational load. Our solution is to build a neural network to learn and optimize AMP detection with four groups of specifically designed learnable coefficients such that divergence rate and detection mean squared error (MSE) can be significantly reduced. Moreover, the proposed deep learning-based AMP has a much faster converging rate, and thus a much lower computational complexity than conventional AMP, providing an alternative solution for the massive MIMO detection. Extensive simulation experiments are provided to validate the advantages of the proposed deep learning-based AMP.
  • COMMUNICATIONS THEORIES & SYSTEMS
    An MAC Layer Algorithm Based on Power Line-Wireless Dual Media Channels and Multiplexing
    Zhixiong Chen, Leixin Zhi, Peiru Chen, YinCheng Qi
    2022, 19(10): 78-91.
    Abstract ( )   Knowledge map   Save
    Hybrid networking of power line and wireless communication can complement each other, save construction costs, improve network robustness, and has important value on the Internet of Things and smart grid. For power line and wireless dual-interface devices, a media access control (MAC) layer algorithm for hybrid communication is proposed, in which the stations can compete for two channels simultaneously. When a station obtains two channels at the same time, the station will randomly select a channel for transmission to ensure fairness, and reset the counters when the transmission is successful. In this paper, the performance of the unsaturated traffic model and counter spatial division is considered, and the throughput under coupling conditions is obtained. Finally, the simulation results show that the MAC layer algorithm based on the hybrid communication network can make full use of the advantages of the two channels, improve system throughput, and reduce transmission delay.
    • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    AvoidPwd: A Mnemonic Password Generation Strategy Based on Keyboard Transformation
    Shunying Lyu, Qian Yao, Jianhua Song
    2022, 19(10): 92-101.
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    Identity authentication is the first line of defense for network security. Passwords have been the most widely used authentication method in recent years. Although there are security risks in passwords, they will be the primary method in the future due to their simplicity and low cost. Considering the security and usability of passwords, we propose AvoidPwd, which is a novel mnemonic password generation strategy that is based on keyboard transformation. AvoidPwd helps users customize a “route” to bypass an “obstacle” and choose the characters on the “route” as the final password. The “obstacle” is a certain word using any language and the keys adjacent to the “obstacle” are typed with the “Shift” key. A two-part experiment was conducted to examine the memorability and security of the AvoidPwd strategy with other three password strategies and three leaked password sets. The results showed that the passwords generated by the AvoidPwd strategy were more secure than the other leaked password sets. Meanwhile, AvoidPwd outperformed the KbCg, SpIns, and Alphapwd in balancing security and usability. In addition, there are more symbols in the character distribution of AvoidPwd than the other strategies. AvoidPwd is hopeful to solve the security problem that people are difficult to remember symbols and they tend to input letters and digits when creating passwords.
  • NETWORKS & SECURITY
    TDLens: Toward an Empirical Evaluation of Provenance Graph-Based Approach to Cyber Threat Detection
    Rui Mei, Hanbing Yan, Qinqin Wang, Zhihui Han, Zhuohang Lyu
    2022, 19(10): 102-115.
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    To combat increasingly sophisticated cyber attacks, the security community has proposed and deployed a large body of threat detection approaches to discover malicious behaviors on host systems and attack payloads in network traffic. Several studies have begun to focus on threat detection methods based on provenance data of host-level event tracing. On the other side, with the significant development of big data and artificial intelligence technologies, large-scale graph computing has been widely used. To this end, kinds of research try to bridge the gap between threat detection based on host log provenance data and graph algorithm, and propose the threat detection algorithm based on system provenance graph. These approaches usually generate the system provenance graph via tagging and tracking of system events, and then leverage the characteristics of the graph to conduct threat detection and attack investigation.
    For the purpose of deeply understanding the correctness, effectiveness, and efficiency of different graph-based threat detection algorithms, we pay attention to mainstream threat detection methods based on provenance graphs. We select and implement 5 state-of-the-art threat detection approaches among a large number of studies as evaluation objects for further analysis. To this end, we collect about 40GB of host-level raw log data in a real-world IT environment, and simulate 6 types of cyber attack scenarios in an isolated environment for malicious provenance data to build our evaluation datasets. The crosswise comparison and longitudinal assessment interpret in detail these detection approaches can detect which attack scenarios well and why. Our empirical evaluation provides a solid foundation for the improvement direction of the threat detection approach.
  • NETWORKS & SECURITY
    Modeling and Analysis of the Decentralized Interactive Cyber Defense Approach
    Ming Liu, Ruiguang Li, Weiling Chang, Jieming Gu, Shouying Bai, Jia Cui, Lu Ma
    2022, 19(10): 116-128.
    Abstract ( )   Knowledge map   Save
    Powered by the Internet and the ever-increasing level of informatization, the cyberspace has become increasingly complex and its security situation has become increasingly grim, which requires new adaptive and collaborative defense technologies. In this paper, we introduced an extended interactive multi-agent decision model for decentralized cyber defense. Based on the significant advantages of the cooperative multi-agent decision-making, the decentralized interactive decision model DI-MDPs and the corresponding interaction and retrieval algorithms are proposed. Then, we analyzed the interactive decision by the calculation and update processes of three matrices, the stability and evolutionary equilibrium of the proposed model are also analyzed. Finally, we evaluated the performance of the proposed algorithms based on open data sets and standard test environments, the experimental results shown that the proposed work will be more applicable in cyber defense.
  • NETWORKS & SECURITY
    Convolutional Neural Network-Based Deep Q-Network (CNN-DQN) Resource Management in Cloud Radio Access Network
    Amjad Iqbal, Mau-Luen Tham, Yoong Choon Chang
    2022, 19(10): 129-142.
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    The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a prominent framework in the 5G mobile network to meet the above requirements by deploying low-cost and intelligent multiple distributed antennas known as remote radio heads (RRHs). However, achieving the optimal resource allocation (RA) in CRAN using the traditional approach is still challenging due to the complex structure. In this paper, we introduce the convolutional neural network-based deep Q-network (CNN-DQN) to balance the energy consumption and guarantee the user quality of service (QoS) demand in downlink CRAN. We first formulate the Markov decision process (MDP) for energy efficiency (EE) and build up a 3-layer CNN to capture the environment feature as an input state space. We then use DQN to turn on/off the RRHs dynamically based on the user QoS demand and energy consumption in the CRAN. Finally, we solve the RA problem based on the user constraint and transmit power to guarantee the user QoS demand and maximize the EE with a minimum number of active RRHs. In the end, we conduct the simulation to compare our proposed scheme with nature DQN and the traditional approach.
  • NETWORKS & SECURITY
    Low-Cost Tiled Display System Architecture with Dynamic Web-Based Management
    Daniel Marfil, Fernando Boronat, F. Javier Pastor, Anna Vidal
    2022, 19(10): 143-168.
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    In this paper, a scalable hardware and software architecture for tiled display systems (a.k.a. videowalls), which can be implemented by using low-cost devices, together with a dynamic web-based management and configuration service are proposed. It has been designed to support both stored and live broadcast/broadband content, in mosaic or warp distributions. The displays and devices can be dynamically configured via web in different ways: the displays can create a single display of a larger size; or they can be configured in a customized way in order to playout different media contents in different display combinations. As display renderers, low-cost devices are proposed as the main hardware element to obtain affordable videowall systems. As a proof of concept, two prototypes have been implemented, including an accurate synchronization mechanism based on a Master/Slave control scheme and aggressive and smooth playout adjustment techniques. To evidence the good performance of the prototypes and configuration service, both objective and subjective evaluations have been conducted regarding synchronization accuracy and usability. On the one hand, the mean values of the asynchronies between the video playout processes in each display are kept below 25ms (i.e., frame accuracy). On the other hand, the obtained usability score in the System Usability Scale (SUS) test has been 88.65, which is considered as excellent.
  • NETWORKS & SECURITY
    Bitcoin Network Measurement and a New Approach to Infer the Topology
    Ruiguang Li, Jiawei Zhu, Dawei Xu, Fudong Wu, Jiaqi Gao, Liehuang Zhu
    2022, 19(10): 169-179.
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    Bitcoin has made an increasing impact on the world's economy and financial order, which attracted extensive attention of researchers and regulators from all over the world. Most previous studies had focused more on the transaction layer, but less on the network layer. In this paper, we developed BNS(Bitcoin Network Sniffer), which could find and connect nodes in the Bitcoin network, and made a measurement in detail. We collected nearly 4.1 million nodes in 1.5 hours and identified 9,515 reachable nodes. We counted the reachable nodes' properties such as: service type, port number, client version and geographic distribution. In addition, we analyzed the stability of the reachable nodes in depth and found nearly 60% kept stable during 15 days. Finally, we proposed a new approach to infer the Bitcoin network topology by analyzing the Neighbor Addresses of Adjacent Nodes and their timestamps, which had an accuracy over 80%.
    • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    LoS MIMO Transmission for LEO Satellite Communication Systems
    Lingxuan Li, Tingting Chen, Wenjin Wang, Xiaohang Song, Li You, Xiqi Gao
    2022, 19(10): 180-193.
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    To provide global service with low latency, the broadband low earth orbits (LEO) satellite constellation based communication systems have become one of the focuses in academic and industry. To allow for wideband access for user links, the feeder link of LEO satellite is correspondingly required to support high throughput data communications. To this end, we propose to apply line-of-sight (LoS) multiple-input multiple-output (MIMO) transmission for the feeder link to achieve spatial multiplexing by optimizing the antenna arrangement. Unlike the LoS MIMO applications for static scenarios, the movement of LEO satellites make it impractical to adjust the optimal antenna separation for all possible satellite positions. To address this issue, we propose to design the antenna placement to maximize the ergodic channel capacity during the visible region of the ground station. We first derive the closed-form probability distribution of the satellite trajectory in visible region. Based on which the ergodic channel capacity can be then calculated numerically. The antenna placement can be further optimized to maximize the ergodic channel capacity. Numerical results verify the derived probability distribution of the satellite trajectory, and show that the proposed LoS MIMO scheme can significantly increase the ergodic channel capacity compared with the existing SISO one.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Research and Optimization of GPSR Routing Protocol for Vehicular Ad-Hoc Network
    Jianming Cui, Liang Ma, Ruirui Wang, Ming Liu
    2022, 19(10): 194-206.
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    Security is one of the most critical issues to Vehicular Ad-hoc Networks (VANETs) since the information transmitted is asynchronous and distributed. Vulnerability and instability are two of the challenges remain to be addressed by the research community and the industry. In this paper, we first proposed a trust reliability based model and extended the GPSR protocol to TM-GPSR protocol. Then, we improved the LET-GPSR protocol based on the link connection time prediction. On this basis, combined the decision index of the TM-GPSR and LET-GPSR protocols, we proposed the RC-GPSR routing protocol. We built the standard testing platform on the NS2 and SUMO, the average end-to-end delay and packet delivery rate of GPSR protocol and the three updates protocols under different node density, node speed, and malicious node ratio are simulated and evaluated. The results showed that under the same conditions, compared with GPSR protocol, RC-GPSR protocol has a lower average end-to-end delay and a higher packet delivery rate, which effectively improves the link stability and security.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Enabling Efficient Caching in High Mobility UAV Communications Network under Limited Backhaul
    Yan Wu, Jiandong Li, Junyu Liu, Min Sheng, Chenxi Zhao
    2022, 19(10): 207-219.
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    Due to flexible deployment, unmanned aerial vehicle (UAV) mounted aerial access points are capable of expanding the coverage capabilities of existing terrestrial base stations (TBSs). Different from TBSs, however, UAV access points (UAPs) are of high mobility in horizontal and vertical dimensions, which may deteriorate the coverage performance. Worsestill, the mobility of UAPs would as well increase the pressure of wireless backhaul. In this light, we investigate the performance of the cache-enabled UAV communications network (CUCN) in terms of network spatial throughput (ST) by analyzing the line of sight (LoS) connections and non-line of sight (NLoS) connections. It is found that the network ST is exponentially decreased with the square of UAP altitude. Furthermore, contrary to intuition, a large cache size may deteriorate the network ST when UAPs are over-deployed. The reason is that a large cache size increases the hit probability, which may increase the activation of UAPs and consequently result in complicated interference. Aiming to maximize the network ST, we optimize the cache strategy under limited backhaul. Remarkably, the results show that network ST could be substantially improved by the optimized cache strategy and the performance degeneration brought by UAP high mobility could be even eliminated especially when the UAP altitude is high.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Data Concurrent Transmission MAC Protocol for Application Oriented Underwater Acoustic Sensor Networks
    Xiaoxiao Zhuo, Hong Yang, Meiyan Liu, Yan Wei, Guanding Yu, Fengzhong Qu
    2022, 19(10): 220-237.
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    With the increasing demand for marine exploration, underwater acoustic sensor networks (UASNs) are prone to have the characteristics of large-scale, long term monitoring, and high data traffic load. Underwater media access control (MAC) protocols, which allow multiple users to share the common medium fairly and efficiently, are essential for the performance of UASNs. However, the design of MAC protocols is confronted with the challenges of spatial unfairness, data eruption, and low energy efficiency. In this paper, we propose a novel data concurrent transmission (DCT) MAC protocol, which is able to exploit long propagation delay and conduct concurrent transmission. Specifically, we present the theoretical performance analysis of the proposed MAC protocol in detail and give an analytical solution of the success concurrent transmission probability between nodes. In addition, simulation results are provided to demonstrate that our proposed protocol is appropriate for UASNs and can significantly improve the performance in terms of network throughput and energy consumption. Finally, we give some typical future applications of UASNs and discuss the demands on MAC protocol design.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    An Adaptive User Service Deployment Strategy for Mobile Edge Computing
    Gang Li, Jingbo Miao, Zihou Wang, Yanni Han, Hongyan Tan, Yanwei Liu, Kun Zhai
    2022, 19(10): 238-249.
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    Mobile edge computing (MEC) is a cloud server running at the edge of a mobile network, which can effectively reduce network communication delay. However, due to the numerous edge servers and devices in the MEC, there may be multiple servers and devices that can provide services to the same user simultaneously. This paper proposes a user-side adaptive user service deployment algorithm ASD (Adaptive Service Deployment) based on reinforcement learning algorithms. Without relying on complex system information, it can master only a few tasks and users. In the case of attributes, perform effective service deployment decisions, analyze and redefine the key parameters of existing algorithms, and dynamically adjust strategies according to task types and available node types to optimize user experience delay. Experiments show that the ASD algorithm can implement user-side decision-making for service deployment. While effectively improving parameter settings in the traditional Multi-Armed Bandit algorithm, it can reduce user-perceived delay and enhance service quality compared with other strategies. %it can reduce user experience delay and enhance service quality compared with other comparison algorithm strategies.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    BUES: A Blockchain-Based Upgraded Email System
    Dawei Xu, Fudong Wu, Liehuang Zhu, Ruiguang Li, Jiaqi Gao, Yijie She
    2022, 19(10): 250-264.
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    This paper focuses on the improvement of traditional email system architecture with the help of blockchain technology in the existing network environment. The improved system architecture can better improve the security and stability of the system. The email content is extracted and stored in the blockchain network to achieve regulatory traceability between the email service provider and the higher-level organization. In turn, A Blockchain-based Upgraded Email System(BUES) is proposed. The defects of the existing traditional email system are addressed. Firstly, the threat model of the traditional email system is analyzed, and solutions are proposed for various threats. Then a system architecture consisting of the blockchain network, email servers, and users are constructed. The implementation of BUES is carried out, and the related experimental process and algorithm steps are given. After the experimental analysis, it is shown that BUES can ensure the security, reliability, efficiency, and traceability of email transmission.