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  • Yunus Dursun, Fang Fang, Zhiguo Ding
    Received: 2020-09-17; Revised: 2021-06-04; Online: 2022-08-15
    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.
  • Amjad Iqbal, Mau-Luen Tham, Yoong Choon Chang
    Received: 2021-10-02; Revised: 2021-12-30; Online: 2022-08-15
    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.
  • Lingxuan Li, Tingting Chen, Wenjin Wang, Xiaohang Song, Li You, Xiqi Gao
    Received: 2021-01-31; Revised: 2021-03-29; Online: 2022-08-15
    Due to the potential in providing global broadband service with low latency, the broadband low earth orbits (LEO) satellite constellation based communication systems have currently become one of the major 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 utilize line-of-sight (LoS) multiple-input multiple-output (MIMO) technique for the feeder link to achieve spatial multiplexing by optimizing the arrangement of the widely spaced antennas. Unlike the LoS MIMO applications for static scenarios, the movement of LEO satellites make it impractical to adjust the antenna separation to maximize the channel capacity 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 for the LEO feeder link can significantly increase the ergodic channel capacity compared with the traditional SISO scheme.
    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.
  • Shunying Lyu, Qian Yao, Jianhua Song
    Received: 2021-06-09; Revised: 2021-11-05; Online: 2022-08-15
    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.
  • Rui Mei, Hanbing Yan, Qinqin Wang, Zhihui Han, Zhuohang Lyu
    Received: 2021-08-11; Revised: 2021-12-08; Online: 2022-08-15
    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.
  • Dawei Xu, Fudong Wu, Liehuang Zhu, Ruiguang Li, Jiaqi Gao, Yijie She
    Received: 2021-08-06; Revised: 2021-11-25; Online: 2022-08-15
    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.
  • Ruiguang Li, Jiawei Zhu, Dawei Xu, Fudong Wu, Jiaqi Gao, Liehuang Zhu
    Received: 2021-08-07; Revised: 2021-10-28; Online: 2022-08-15
    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$\%$.
  • Jianming Cui, Liang Ma, Ruirui Wang, Ming Liu
    Received: 2021-08-06; Revised: 2021-10-21; Online: 2022-08-15
    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.
  • Gang Li, Jingbo Miao, Zihou Wang, Yanni Han, Hongyan Tan, Yanwei Liu, Kun Zhai
    Received: 2021-08-13; Revised: 2021-10-21; Online: 2022-08-15
    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.
  • Ming Liu, Ruiguang Li, Weiling Chang, Jieming Gu, Shouying Bai, Jia Cui, Lu Ma
    Received: 2021-08-14; Revised: 2021-09-25; Online: 2022-08-15
    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.
  • Daniel Marfil, Fernando Boronat, F. Javier Pastor, Anna Vidal
    Received: 2021-04-28; Revised: 2021-06-29; Online: 2022-08-15
    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.
  • Yan Wu, Jiandong Li, Junyu Liu, Min Sheng, Chenxi Zhao
    Received: 2021-02-22; Revised: 2021-05-11; Online: 2022-08-15
    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.
  • Yong Li, Junli Li, Xiang Zhang, Guiming Wei
    Received: 2021-04-01; Revised: 2021-06-08; Online: 2022-08-15
    Over-the-air (OTA) testing is considered as the only feasible solution to evaluate radio performances of the fifth-generation (5G) wireless devices which feature two important technologies, i.e., massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave). The multi-probe anechoic chamber (MPAC) based OTA setup is able to emulate realistic multipath propagation conditions in a controlled manner. This paper investigates an MPAC OTA setup which is capable of evaluating the performances of 5G base stations as the devices-under-test (DUTs) which are equipped with dual-polarized antennas. Both end-to-end setup and probe configuration for the considered MPAC setup will be elaborated. Furthermore, since building a practical MPAC setup is expensive, time-consuming, and error-prone, an end-to-end software testbed is established for validation purpose to avoid technical risks before finalizing an MPAC setup. The architecture of the testbed is presented, which can emulate both the channel profiles perceived by the DUT and the physical-layer behaviors of the considered link conforming to 5G new radio (NR) specifications. Results show that the performances under the emulated channel agree well with those under the target channel, validating the accuracy and effectiveness of the MPAC method.
  • Xiaojun Wang, Yijie Ren, Weiguang Sun, Lin Liu, Xiaoshu Chen
    Received: 2020-12-01; Revised: 2021-04-29; Online: 2022-08-15
    Location-based services have become an important part of the daily life. Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment. In this paper, a single-site multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is modeled, from which an angle delay channel power matrix (ADCPM) is extracted. Considering the changing environment, auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints. When the scattering environment has changed beyond a certain extent, the robustness will not be able to make up for the positioning error. Under this circumstance, an updating of the fingerprint database is imperative. A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed. Simulation results show the desirable performance of the proposed methods.
  • Yimeng Feng, Yi Jiang, Mahesh K. Varanasi
    Received: 2021-08-25; Revised: 2021-10-24; Online: 2022-08-15
    Based on an analog radio frequency (RF) network, hybrid precoding (HPC) for massive MIMO can achieve very high spectral efficiencies with moderate hardware cost and power consumption. Despite the extensive research efforts in recent years, the practioners are still looking for HPCs that are efficient and easy-to-implement. In this paper, we present a new method termed as the universal hybrid precoding (UHP), which is nearly optimal, computationally efficient, and applicable to various types of RF network (thus, the name universal): the components of the network can be phase shifters (with finite or infinite resolutions), switches, or their combinations; the topology of the network can be fully-connected or partially-connected. Besides the standard UHP, we also propose a simplified version termed as sUHP to trade a negligible performance loss for much reduced computational complexity. The analysis shows that the computational complexity of the proposed UHP/sUHP is one to two orders of magnitude lower than the state-of-the-art methods. Simulation results verify the (near-)optimality of the proposed UHP scheme for various forms of the analog networks.
  • Xiaoyu Zhang, Wei Liu, Fangchun Yang
    Received: 2021-07-03; Revised: 2022-03-01; Online: 2022-08-15
    Multi-agent mobile applications play an essential role in mobile applications and have attracted more and more researchers' attention. Previous work has always focused on multi-agent applications with perfect information. Researchers are usually based on human-designed rules to provide decision-making searching services. However, existing methods for solving perfect-information mobile applications cannot be directly applied to imperfect-information mobile applications. Here, we take the Contact Bridge, a multi-agent application with imperfect information, for the case study. We propose an enhanced searching strategy to deal with multi-agent applications with imperfect information. We design a self-training bidding system model and apply a Recurrent Neural Network (RNN) to model the bidding process. The bridge system model consists of two parts, a bidding prediction system based on imitation learning to get a contract quickly and a visualization system for hands understanding to realize regular communication between players. Then, to dynamically analyze the impact of other players' unknown hands on our final reward, we design a Monte Carlo sampling algorithm based on the bidding system model (BSM) to deal with imperfect information. At the same time, a double-dummy analysis model is designed to efficiently evaluate the results of sampling. Experimental results indicate that our searching strategy outperforms the top rule-based mobile applications.
  • Ruirui Shao, Zhigeng Fang, Su Gao, Sifeng Liu, Weiqing You
    Received: 2021-03-19; Revised: 2021-08-21; Online: 2022-06-23
    The effectiveness evaluation of GEO satellite communication constellation not only characterizes the communication capability of the constellation but also provides a basis for optimizing the constellation structure. Whether due to information poverty or the complexity of the system, the impact of uncertain information on the effectiveness evaluation needs to be considered to ensure the accuracy of the evaluation results. To address this issue, this paper develops a model for evaluating the GEO satellite communication constellation's effectiveness in the context of poor information. Firstly, it analyses the GEO satellite communication constellation plus system based on communication links with an in-depth analysis of the constellation structure. Secondly, an equivalent transfer function algorithm based on the characteristic function and transfer probability is proposed with the help of graphical evaluation and review technique. Then, by analyzing the communication link importance connotation, the algorithm formula of communication link effectiveness and its importance is derived, and the constellation effectiveness and variance are found. Finally, the model constructed in this paper is used to evaluate the effectiveness of a GEO satellite communication constellation, further verifying the accuracy and validity of the model. Through comparative analysis, it is shown that the model can not only solving the effectiveness of the constellation but also analyzing the variation of its effectiveness. It lays a theoretical foundation for the analysis and optimization of the GEO satellite communication constellation effectiveness.
  • Zhixiong Chen, Zhengchuan Chen, Zhi Ren, Liang Liang, Wanli Wen, Yunjian Jia
    Received: 2021-03-03; Revised: 2021-08-21; Online: 2022-06-23
    Applications with sensitive delay and sizeable data volumes, such as interactive gaming and augmented reality, have become popular in recent years. These applications pose a huge challenge for mobile users with limited resources.Computation offloading is a mainstream technique to reduce execution delay and save energy for mobile users. However, computation offloading requires communication between mobile users and mobile edge computing (MEC) servers. Such a mechanism would difficultly meet users' demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for users. Caching task data can effectively reduce the data transmissions when users offload their tasks to the MEC server. The limited caching space at the MEC server calls for judiciously decide which tasks should be cached. Motivated by this, we consider the joint optimization of computation offloading and task caching in a cellular network. In particular, it allows users to proactively cache or offload their tasks at the MEC server. The objective of this paper is to minimize the system cost, which is defined as the weighted sum of task execution delay and energy consumption for all users. Aiming at establishing optimal performance bound for the system design, we formulate an optimization problem by jointly optimizing the task caching, computation offloading, and resource allocation. The problem is a challenging mixed-integer non-linear programming problem and is NP-hard in general. To solve it efficiently, by using convex optimization, Karmarkar's algorithm and the proposed fast search algorithm, we obtain an optimal solution of the formulated problem with manageable computational complexity. Extensive simulation results show that in comparison to some representative benchmark methods, the proposed solution can effectively reduce the system cost.
  • Fei Zhao, Jun He, Sanyou Zeng, Changhe Li, Qinghui Xu, Zhigao Zeng
    Received: 2021-05-07; Revised: 2021-08-07; Online: 2022-06-23
    Pattern synthesise of antenna arrays is usually complicated optimization problems, while evolutionary algorithms (EAs) are promising in solving these problems. This paper does not propose a new EA, but does construct a new form of optimization problems. The new optimization formulation has two differences from the common ones. One is the objective function is the field error between the desired and the designed, not the usual amplitude error between the desired and the designed. This difference is beneficial to decrease complexity in some sense. The second difference is that the design variables are changed as phases of desired radiation field within shaped-region, instead of excitation parameters. This difference leads to the reduction of the number of design variables. A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied, and the effectiveness and advantages of the proposed new optimization problems are validated. The results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.
  • Jue Wang, Shuaifeng Lu, Jing Wang, Zhenyu Jiang, Rui Tang, Ruifeng Gao, Yingdong Hu, Ye Li
    Received: 2021-06-27; Revised: 2021-10-13; Online: 2022-06-23
    Channel training in reconfigurable intelligent surface (RIS)-assisted communications is usually conducted in an on-off manner, resulting in unaffordable training time overhead when the number of RIS elements is large. In this paper, for correlated Rayleigh channels, we compare three typical training overhead reduction schemes, namely RIS element selection (Scheme 1), element grouping (Scheme 2), and statistical CSI-based phase shifts design (Scheme 3). For Scheme 1 and Scheme 2, we propose two algorithms to select RIS elements (or form element groups) and determine the optimal number of activated elements (or formed groups), based on the channel correlation information only; for Scheme 3, we consider a semi-definite programming-based approach in the literature, and propose an alternative dominant eigenvector-based method for determining the RIS phase shifts vector. Via extensive simulations, we compare the achievable ergodic rates of these schemes versus the signal-to-noise ratio, the channel correlation level, and the element number-to-coherent time ratio, respectively, and discuss possible switching of the three schemes over these system parameters. At last, operation regions of the considered training overhead reduction schemes are shown in the plane characterized by the system parameters, which provides useful guidelines for practical scheme determination.
  • Ningbo Zhang, Yajie Yan, Xuzhen Zhu, Jing Wang
    Received: 2021-06-29; Revised: 2021-12-09; Online: 2022-06-23
    User behavior prediction has become a core element to Internet of Things (IoT) and received promising attention in the related fields. Many existing IoT systems (e.g. smart home systems) have been deployed various sensors and the user's behavior can be predicted through the sensor data. However, most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction. Therefore, it is a challenge to provide an automatic behavior prediction model based on the original sensor data. To solve the problem, this paper proposed a novel automatic annotated user behavior prediction (AAUBP) model. The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining (DVSM) behavior recognition model and behavior prediction model based on the Long Short Term Memory (LSTM) network. To evaluate the model, we performed several experiments on a real-world dataset tuning the parameters. The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.
  • Youjia Chen, Yuekai Cai, Haifeng Zheng, Jinsong Hu, Jun Li
    Received: 2021-06-30; Revised: 2021-10-19; Online: 2022-06-23
    Scalable video coding (SVC) has been widely used in video-on-demand (VOD) service, to efficiently satisfy users' different video quality requirements and dynamically adjust video stream to time-variant wireless channels. Under the 5G network structure, we consider a cooperative caching scheme inside each cluster with SVC to economically utilize the limited caching storage. A novel multi-agent deep reinforcement learning (MADRL) framework is proposed to jointly optimize the video access delay and users' satisfaction, where an aggregation node is introduced helping individual agents to achieve global observations and overall system rewards. Moreover, to cope with the large action space caused by the large number of videos and users, a dimension decomposition method is embedded into the neural network in each agent, which greatly reduce the computational complexity and memory cost of the reinforcement learning. Experimental results show that: 1 the proposed value-decomposed dimensional network (VDDN) algorithm achieves an obvious performance gain versus the traditional MADRL; 2 the proposed VDDN algorithm can handle an extremely large action space and quickly converge with a low computational complexity.
  • Qiang Li, Pinyi Ren
    Received: 2021-06-30; Revised: 2021-12-02; Online: 2022-06-23
    Although the collaborative transmission of cellular network and device-to-device (D2D) pairs can improve spectrum utilization, it also results in the matual interference, which may be fatal for low-energy D2D pairs. Based on this, we propose in this paper a collaborative D2D transmission scheme with erergy harvesting (CDTEH) in a relay network, where D2D pairs are allowed to access the spectrum of relay network to accomplish their own transmission. In particular, the relay with energy harvesting is arranged to not only expand cellular transmission range but also assist D2D and cellular users to eliminate the mutual interference. To evaluate the performance, rate-energy (R-E) region is introduced. Based on the model, a data rate maximization problem of D2D pair is formulated, subject to a transmission demand of the cellular user and the optimal solution is acquired. Finally, numerical results are provided to validate the proposed scheme improves the data rate of D2D pair ensuring the cellular transmission requirement.
  • Jiachen Qian, Jue Wang, Shi Jin
    Received: 2021-06-10; Revised: 2021-12-14; Online: 2022-06-23
    Air-to-ground wireless channel modeling for unmanned aerial vehicle (UAV) communications has been widely studied. However, channel modeling for UAV swarm-enabled cooperative communication still needs investigation, where the impact of UAV positions on the spatial channel characteristics is of particular importance. In this paper, we consider a UAV swarm-enabled virtual multiple input multiple output (MIMO) system, where multiple single-antenna UAVs cooperatively transmit to multiple ground users (GUs). We establish a common coordinate system, as well as a UAV swarm-oriented coordinate system, to describe the relative positions of the GUs and the UAV elements, respectively. Based on the established coordinate systems, geometric ray superposition method is applied to describe the spatial channel matrix. The proposed modeling framework can be directly used to describe the line-of-sight and two-ray propagations, and can be extended for including more practical spatial features such as multipath scattering, inter-UAV blockage, and random UAV jittering, etc. Based on the proposed model, we further analyze the spatial correlation among the virtual MIMO links of GUs located at different positions. Via extensive simulations, we show that thanks to the flexible deployment of UAVs, the virtual MIMO array structure can be conveniently configured to get desired channel properties, such as the channel capacity, eigenvalue and condition number distribution, and spatial correlation distribution. This shows the possibility and importance of exploiting a new design dimension, i.e., the UAV swarm pattern, in such cooperative virtual MIMO systems.
  • Chang Liu, Sheng Wu, Chunxiao Jiang, Hongwen Yang
    Received: 2021-05-19; Revised: 2021-08-06; Online: 2022-06-23
    An asynchronous transmission scenario for non-orthogonal multiple access (NOMA) user signals with arbitrary phase offset is investigated in this paper. To improve the system performance in the user power-balanced conditions, we adopt a synthetic detection method at the receiver, i.e., the jointly optimal maximal likelihood detection aided triangular successive interference cancellation (JO ML-TSIC) method. Analytical bit error rate (BER) solutions are obtained for a two-user case with the optimal, intentional one-half symbol period time delay implemented between the user signals. Furthermore, closed-form BER solutions for the case using the triangular successive interference cancellation (TSIC) detection method are also derived for comparisons. Numerical results show that the JO ML-TSIC receiver for the asynchronous system outperforms the TSIC receiver as well as the synchronous successive interference cancellation (SIC) receiver in all the conditions concerned. The results also show that the superiority of the JO ML-TSIC receiver is strengthened when the signals experience flat Rayleigh fading channels compared to the TSIC and the synchronous SIC receivers.
  • Yu Hua, Yaru Fu, Qi Zhu
    Received: 2020-12-23; Revised: 2021-04-29; Online: 2022-06-23
    To accommodate the tremendous increase of mobile data traffic, cache-enabled device-to-device (D2D) communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers. As a result, cellular traffic can be offloaded and an enhanced system performance can be attainable. However, due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users, the requested contents are most likely not be proactively cached, inducing lower cache hit ratio. Recommendation system, on the other hand, is able to reshape users' request schema, mitigating the heterogeneity to some extent, and hence it can boost the gain of edge caching. In this paper, the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated, taking into account the constraints on the cache capacity budget and the total number of recommended files per user, in which the contents are sharing between the users that trust each other. The minimization problem is an integer non-convex and non-linear programming, which is in general NP-hard. Therewith, we propose a time-efficient joint recommendation and caching decision scheme. Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies.
  • Junliang Lin, Gongpu Wang, Zijian Zheng, Ruyi Ye, Ruisi He, Bo Ai
    Received: 2020-07-22; Revised: 2020-11-18; Online: 2022-06-23
    Relying on direct and converse piezoelectric effects, piezo-acoustic backscatter (PAB) technology reflects ambient acoustic signals to enable underwater backscatter communications at near-zero power, which was first realized through a prototype. In this paper, we propose a mathematical model of the PAB assisted underwater acoustic (UWA) communication, and address the sparse channel estimation problem. First, we present a five-stage backscatter process to derive the backscatter coefficient, and propose the channel model for the shallow-water communications. Then, we formulate the shallow-water acoustic channel estimation problem as a sparse vector recovery one according to the compressed sensing theory, and leverage the orthogonal matching pursuit (OMP) algorithm to obtain the channel estimator. Finally, simulation results are provided to corroborate our proposed studies.
  • Xue Yang, Changchun Bao, Zihao Cui
    Received: 2021-05-26; Revised: 2021-08-17; Online: 2022-06-23
    Generalized cross-correlation is considered as the most straightforward time delay estimation algorithm. Depending on various weighting function, different methods were derived and a straightforward method, named phase transform (PHAT) has been widely used. PHAT is well-known for its robustness to reverberation and its sensitivity to noise, which is partly due to the fact that PHAT distributes same weights to the frequencies dominated by signal or noise. To alleviate this problem, two weighting functions are proposed in this paper. By taking a posteriori signal-to-noise ratio (SNR) into account to classify reliable and unreliable frequencies, different weights could be assigned. The first proposed weighting function borrows the idea of binary mask and distributes same weights to frequencies in same set, whereas, the second one assigns weights based on coherence function. Experiments showed the robustness of proposed methods to reverberation and noise for improving the performance of time delay estimation through various criteria.
  • Shunan Han, Peng Liu, Guang Huang
    Received: 2020-09-27; Revised: 2021-12-06; Online: 2022-06-23
    The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively. With the increase of the codeword length and constraint length, the search space expands exponentially, and thus it limits the application of these methods in practice. To overcome the limitation, a novel identification method, which gets rid of exhaustive test, is proposed based on the cuckoo search algorithm by using soft-decision data. Firstly, by using soft-decision data, the probability that a parity check equation holds is derived. Thus, solving the parity check equations is converted to maximize the joint probability that parity check equations hold. Secondly, based on the standard cuckoo search algorithm, the established cost function is optimized. According to the final solution of the optimization problem, the generator matrix of recursive systematic convolutional code is estimated. Compared with the existing methods, our proposed method does not need to search for the generator matrix exhaustively and has high robustness. Additionally, it does not require the prior knowledge of the constraint length and is applicable in any modulation type.
  • Zhen Li, Mingchuan Yang, Gang Wang, Donglai Zhao
    Received: 2021-02-28; Revised: 2021-07-25; Online: 2022-06-23
    We analyze the performance of a two-way satellite-terrestrial decode-and-forward (DF) relay network over non-identical fading channels. In particular, selective physical-layer network coding (SPNC) is employed in the proposed network to improve the average end-to-end throughput performance. More specifically, by assuming that the DF relay performs instantaneous throughput comparisons before performing corresponding protocols, we derive the expressions of system instantaneous bit-error-rate (BER), instantaneous end-to-end throughput, average end-to-end throughput, single node detection (SND) occurrence probability and average end-to-end BER over non-identical fading channels. Finally, theoretical analyses and Monte Carlo simulation results are presented. Evaluations show that: 1) SPNC protocol outperforms the conventional physical-layer network coding (PNC) protocol in infrequent light shadowing (ILS), average shadowing (AS) and frequent heavy shadowing (FHS) Shadowed-Rician fading channels. 2) As the satellite-relay channel fading gets more severe, SPNC protocol can achieve more performance improvement than PNC protocol and the occurrence probability of SND protocol increases progressively. 3) The occurrence probability increase of SND has a beneficial effect on the average end-to-end throughput in low signal-to-noise ratio (SNR) regime, while the occurrence probability decrease of SND has a beneficial effect on the average end-to-end BER in high-SNR regime.
  • Yilin Wang, Weisheng He, Xuwei Fan, Lianfen Huang, Jie Yang, Yuliang Tang
    Received: 2021-03-17; Revised: 2021-10-21; Online: 2022-06-23
    With the rapid development of the Internet technology, millimeter wave (mmWave) will be used as a supplement to 5G low frequency bands to meet the extremely high system capacity requirements of 5G in hot spots. Although 5G mmWave communication can adapt to the needs of 5G network and carry a large amount of transmitted data, transmission stability has become one of the key technical issues of 5G network mmWave communication due to problems such as strong attenuation and poor penetration of mmWave. In order to improve the efficiency of the mmWave multi-hop transmission, we propose a 5G mmWave multi-hop transmission method based on network coding, which can adapt to the current wireless network environment, improve spectrum efficiency and increase network throughput. Based on MATLAB simulation experiments, it is verified that the proposed method can greatly improve the transmission efficiency and reduce the signal loss under the premise of ensuring the accurate signal transmission.
  • Ning Li, Qiaodi Zhu, Zhongliang Deng
    Received: 2020-06-08; Revised: 2021-03-18; Online: 2022-06-23
    The packet loss classification has always been a hot and difficult issue in TCP congestion control research. Compared with the terrestrial network, the probability of packet loss in LEO satellite network increases dramatically. What’s more, the problem of concept drifting is also more serious, which greatly affects the accuracy of the loss classification model. In this paper, we propose a new loss classification scheme based on concept drift detection and hybrid integration learning for LEO satellite networks, named LDM-Satellite, which consists of three modules: concept drift detection, lost packet cache and hybrid integration classification. As far, this is the first paper to consider the influence of concept drift on the loss classification model in satellite networks. We also innovatively use multiple base classifiers and a naive Bayes classifier as the final hybrid classifier. And a new weight algorithm for these classifiers is given. In ns-2 simulation, LDM-Satellite has a better AUC (0.9885) than the single-model machine learning classification algorithms. The accuracy of loss classification even exceeds 98%, higher than traditional TCP protocols. Moreover, compared with the existing protocols used for satellite networks, LDM-Satellite not only improves the throughput rate but also has good fairness.
  • Junyu Zhang, Chen Gong, Shangbin Li, Rui Ni, Chengjie Zuo, Jinkang Zhu, Ming Zhao, Zhengyuan Xu
    Received: 2020-01-17; Revised: 2020-04-24; Online: 2022-06-23
    Future wireless communication system embraces physical-layer signal detection with high sensitivity, especially in the microwave photon level. Currently, the receiver primarily adopts the signal detection based on semi-conductor devices for signal detection, while this paper introduces high-sensitivity photon-level microwave detection based on superconducting structure. We first overview existing works on the photon-level communication in the optical spectrum as well as the microwave photon-level sensing based on superconducting structure in both theoretical and experimental perspectives, including microwave detection circuit model based on Josephson junction, microwave photon counter based on Josephson junction, and two reconstruction approaches under background noise. In addition, we characterize channel modeling based on two different microwave photon detection approaches, including the absorption barrier and the dual-path Handury Brown-Twiss (HBT) experiments, and predict the corresponding achievable rates. According to the performance prediction, it is seen that the microwave photon-level signal detection can increase the receiver sensitivity compared with the state-of-the-art standardized communication system with waveform signal reception, with gain over 10dB.
  • Zhipeng Gao, Yan Yang, Chen Zhao, Zijia Mo
    Received: 2021-07-29; Revised: 2021-12-13; Online: 2022-06-23
    The rapid growth of modern mobile devices leads to a large number of distributed data, which is extremely valuable for learning models. Unfortunately, model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns, hindering artificial intelligence from empowering mobile devices. Moreover, these data are not identically and independently distributed (Non-IID) caused by their different context, which will deteriorate the performance of the model. To address these issues, we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation, named ADC-DL, which learns a shared model by collecting the synthetic samples generated on each device. To tackle the heterogeneity of data distribution, we propose an entropy topsis comprehensive tiering model for hierarchical clustering, which distinguishes clients in terms of their data characteristics. Subsequently, synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation. The procedure of dataset condensation can be adjusted adaptively according to the tier of the client. Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.
  • Dehui Wei, Jiao Zhang, Xuan Zhang, Chengyuan Huang
    Received: 2021-04-21; Revised: 2021-09-18; Online: 2022-06-23
    Congestion control (CC) is always an important issue in the field of networking, and the enthusiasm for its research has never diminished in both academia and industry. In current years, due to the rapid development of machine learning (ML), the combination of reinforcement learning (RL) and CC has a striking effect. However, These complicated schemes lack generalization and are too heavyweight in storage and computing to be directly implemented in mobile devices. In order to address these problems, we propose Plume, a high-performance, lightweight and generalized RL-CC scheme. Plume proposes a lightweight framework to reduce the overheads while preserving the original performance. Besides, Plume innovatively modifies the framework parameters of the reward function during the retraining process, so that the algorithm can be applied to a variety of scenarios. Evaluation results show that Plume can retain almost all the performance of the original model but the size and decision latency can be reduced by more than 50% and 20%, respectively. Moreover, Plume has better performances in some special scenes.
  • Xiaokang Zhou, Huiyun Xia, Shaochuan Wu
    Received: 2021-08-04; Revised: 2022-01-17; Online: 2022-06-23
    Cell-free network is a promising architecture with numerous merits in energy efficiency and macro diversity, which is easy and flexible to integrate with other communication technologies. However, its current network topology where access points (APs) are connected to a central processing unit (CPU) to jointly serve the users, causes huge burden to the fronthaul network. To deal with this problem, in this paper, we first combine thoughts in user-centric (UC) network where users are served by selected subset of APs. Then, we propose a successful transmission probability (STP) based AP clustering scheme to reduce the fronthaul capacity requirement (FCR). By using stochastic geometry and proper approximation methods, the approximated STP calculation expression is derived. Numerical simulations demonstrate that the obtained STP expression can provide a tight approximation compared to Monte Carlo simulation results under different system parameters while keeping the computation tractable. Furthermore, the relationship between the FCR and the STP threshold is formulated as a clustering optimization problem, which gives insights on clustering design in UC-CF network systems. We show by simulation results that the proposed scheme requires less fronthaul capacity than the original CF approach while ensuring the STP performance.
  • Hongyan Cui, Diyue Chen, Roy E. Welsch
    Received: 2021-04-21; Revised: 2021-04-21; Online: 2022-06-23
    In recent years, Delay Tolerant Networks (DTN) have received more and more attention. At the same time, several existing DTN routing algorithms generally have disadvantages such as poor scalability and inability to perceive changes in the network environment. This paper proposes an AdaptiveSpray routing algorithm. The algorithm can dynamically control the initial maximum message copy number according to the cache occupancy rate of the node itself, and the cache occupancy rate is added as an impact factor to the calculation of the probability of each node meeting the destination node. In the forwarding phase, the node will first compare the meeting probability of itself and the meeting node to the destination node, and then choose different forwarding strategies. The simulation shows that the AdaptiveSpray algorithm proposed in this paper has obvious advantages compared with the existing routing algorithms in terms of message delivery rate and average delay.
  • Lu Chen, Hongbo Tang, Wei You, Yi Bai
    Received: 2021-06-03; Revised: 2021-09-18; Online: 2022-06-23
    Resource-constrainted and located closer to users, edge servers are more vulnerable to Distributed Denial of Service (DDoS) attacks. In order to mitigate the impact of DDoS attacks on benign users, this paper designed a Resource-based Pricing Collaborative approach (RPC) in mobile edge computing. By introducing the influence of resource prices on requester in economics, a collaboration model based on resource pricing was established, and the allocation of user request was regarded as a game strategy to obtain the overall minimum offloading cost of the user in network. The article theoretically proved the existence and rationality of the Nash equilibrium. Finally, simulation results verified the effectiveness and feasibility of the proposed approach in two experimental scenes. Experimental results shows that RPC can effectively improve the network ability to mitigate DDoS attacks, and alleviate the adverse effects of server attacks under delay constraints.