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  • February 2020 Vol. 17 No. 2
      

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  • Xiting Peng, Kaoru Ota, Mianxiong Dong
    2020, 17(2): 1-13.
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    The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types. Current traffic analysis methods are executed on the cloud, which needs to upload the traffic data. Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes. However, traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model, which are not suitable for fog computing due to the poor computing power. In this study, we design a novel fog computing based traffic analysis system using broad learning. For one thing, fog computing can provide a distributed architecture for saving the bandwidth resources. For another, we use the broad learning to incrementally train the traffic data, which is more suitable for fog computing because it can support incremental updates of models without retraining all data. We implement our system on the Raspberry Pi, and experimental results show that we have a 98% probability to accurately identify these traffic data. Moreover, our method has a faster training speed compared with Convolutional Neural Network(CNN).
  • Ahmed B.Zaky, Joshua Zhexue Huang, Kaishun Wu, Basem M. ElHalawany
    2020, 17(2): 14-29.
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    Spectrum management and resource allocation(RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large-size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising gener- ative models, for enabling spectrum sharing in underlay device-to-device(D2D) communication by solving linear sum assignment problems(LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders(CVAE). The three proposed architecture are the convolutional neural network(CVAE-CNN) autoencoder, the feed-forward neural network(CVAE-FNN) autoencoder, and the hybrid(H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques.
  • Cuiran Li, Ling Liu, Jianli Xie
    2020, 17(2): 30-39.
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    In recent years, high-speed railways(HSRs) have developed rapidly with a high transportation capacity and high comfort level. A tunnel is a complex high-speed rail terrain environment. It is very important to establish an accurate channel propagation model for a railway tunnel environment to improve the safety of HSR operation. In this paper, a method for finite-state Markov chain(FSMC) channel modeling with least squares fitting based on non-uniform interval division is proposed. First, a path loss model is obtained according to measured data. The communication distance between the transmitter and receiver in the tunnel is non-uniformly divided into several large non-overlapping intervals based on the path loss model. Then, the Lloyd-Max quantization method is used to determine the threshold of the signal-to-noise ratio(SNR) and the channel state quantization value and obtain the FSMC state transition probability matrix. Simulation experiments show that the proposed wireless channel model has a low mean square error(MSE) and can accurately predict the received signal power in a railway tunnel environment.
  • Jianli Xie, Wenjuan Gao, Cuiran Li
    2020, 17(2): 40-53.
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    A network selection optimization algorithm based on the Markov decision process(MDP) is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment. Considering the different types of service requirements, the MDP model and its reward function are constructed based on the quality of service(QoS) attribute parameters of the mobile users, and the network attribute weights are calculated by using the analytic hierarchy process(AHP). The network handoff decision condition is designed according to the different types of user services and the time-varying characteristics of the network, and the MDP model is solved by using the genetic algorithm and simulated annealing(GA-SA), thus, users can seamlessly switch to the network with the best long-term expected reward value. Simulation results show that the proposed algorithm has good convergence performance, and can guarantee that users with different service types will obtain satisfactory expected total reward values and have low numbers of network handoffs.
  • Zhijuan Hu, Danyang Wang, Chenxi Li, Tingting Wang
    2020, 17(2): 54-65.
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    Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks. However, there exists redundancy among the spectrum data collected by a sensor node within a data collection period, which may reduce the data uploading efficiency. In this paper, we investigate the inter-data commonality detection which describes how much two data have in common. We define common segment set and divide it into six categories firstly, then a method to measure a common segment set is conducted by extracting commonality between two files. Moreover, the existing algorithms fail in finding a good common segment set, so Common Data Measurement(CDM) algorithm that can identify a good common segment set based on inter-data commonality detection is proposed. Theoretical analysis proves that CDM algorithm achieves a good measurement for the commonality between two strings. In addition, we conduct an synthetic dataset which are produced randomly. Numerical results shows that CDM algorithm can get better performance in measuring commonality between two binary files compared with Greedy-String-Tiling(GST) algorithm and simple greedy algorithm.
  • Fandi Lin, Jin Chen, Jiachen Sun, Guoru Ding, Ling Yu
    2020, 17(2): 66-80.
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    Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data. In practice, for a given spectrum band of interest, when facing relatively scarce historical data, spectrum prediction based on traditional learning methods does not work well. Thus, this paper proposes a cross-band spectrum prediction model based on transfer learning. Firstly, by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping, the similarity between spectrum bands has been verified. Next, the features, which mainly affect the performance of transfer learning in the crossband spectrum prediction, are explored by leveraging transfer component analysis. Then, the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated. Further, experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-the-art models when the historical spectrum data is limited.
  • Huichao Chen, Zheng Wang, Linyuan Zhang
    2020, 17(2): 81-92.
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    Drones, also known as mini-unmanned aerial vehicles(UAVs), are enjoying great popularity in recent years due to their advantages of low cost, easy to pilot and small size, which also makes them hard to detect. They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security. In this article, we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty. First, we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem. Then, we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image. Furthermore, to exploit more information and improve the detection performance, we develop a trajectory classification algorithm which converts the ?ight process of the drones in consecutive multiple sensing slots into trajectory images. In addition, simulations are provided to verify the proposed methods' performance under various parameter configurations.
  • Lixin Li, Youbing Hu, Huisheng Zhang, Wei Liang, Ang Gao
    2020, 17(2): 93-106.
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    In order to improve the physical layer security of the device-to-device(D2D) cellular network, we propose a collaborative scheme for the transmit antenna selection and the optimal D2D pair establishment based on deep learning. Due to the mobility of users, using the current channel state information to select a transmit antenna or establish a D2D pair for the next time slot cannot ensure secure communication. Therefore, in this paper, we utilize the Echo State Network(ESN) to select the transmit antenna and the Long Short-Term Memory(LSTM) to establish the D2D pair. The simulation results show that the LSTM-based and ESN-based collaboration scheme can effectively improve the security capacity of the cellular network with D2D and increase the life of the base station.
  • Pengwu Wan, Qiongdan Huang, Guangyue Lu, Jin Wang, Qianli Yan, Yufei Chen
    2020, 17(2): 107-116.
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    For the influence caused by multipath fading and non-line-of-sight(NLOS) transmission, it is challenging to accurately localize a moving signal source in complex environment by using the wireless sensor network(WSN) on the ground. In this paper, we establish a special WSN in the sky to address this challenge, where each sensor is loaded on an unmanned aerial vehicle(UAV) and the operation center of all the UAVs is fixed on the ground. Based on the analyzing of the optimal distribution and the position error calibration of all the sensors, we formulate the localization scheme to estimate the position of the target source, which combines the time difference of arrival(TDOA) method and the frequency difference of arrival(FDOA) method. Then by employing the semidefinite programming approach, we accurately obtain the position and velocity of the signal source. In the simulation, the validity of the proposed method is verified through the performance comparison.
  • Guangxin Lou, Hongzhen Shi
    2020, 17(2): 117-124.
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    With the continuous progress of The Times and the development of technology, the rise of network social media has also brought the “explosive” growth of image data. As one of the main ways of People's Daily communication, image is widely used as a carrier of communication because of its rich content, intuitive and other advantages. Image recognition based on convolution neural network is the first application in the field of image recognition. A series of algorithm operations such as image eigenvalue extraction, recognition and convolution are used to identify and analyze different images. The rapid development of artificial intelligence makes machine learning more and more important in its research field. Use algorithms to learn each piece of data and predict the outcome. This has become an important key to open the door of artificial intelligence. In machine vision, image recognition is the foundation, but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition. Predecessors have provided many model algorithms, which have laid a solid foundation for the development of artificial intelligence and image recognition. The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network. Different from full connection network, convolutional neural network does not use full connection method in each layer of neurons of neural network, but USES some nodes for connection. Although this method reduces the computation time, due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation, this paper improves the model to be a multi-level information fusion of the convolution calculation method, and further recovers the discarded feature information, so as to improve the recognition rate of the image. VGG divides the network into five groups(mimicking the five layers of AlexNet), yet it USES 3*3 filters and combines them as a convolution sequence. Network deeper DCNN, channel number is bigger. The recognition rate of the model was verified by 0RL Face Database, BioID Face Database and CASIA Face Image Database.
  • Dawei Wang, Wei Liang, Xiaoyu Hu, Daosen Zhai, Di Zhang
    2020, 17(2): 125-137.
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    In order to provide privacy provisioning for the secondary information, we propose an energy harvesting based secure transmission scheme for the cognitive multi-relay networks. In the proposed scheme, two secondary relays harvest energy to power the secondary transmitter and assist the secondary secure transmission without interfere the secondary transmission. Specifically, the proposed secure transmission policy is implemented into two phases. In the first phase, the secondary transmitter transmits the secrecy information and jamming signal through the power split method. After harvesting energy from a fraction of received radio-frequency signals, one secondary relay adopts the amplify-and-forward relay protocol to assist the secondary secure transmission and the other secondary relay just forwards the new designed jamming signal to protect the secondary privacy information and degrade the jamming interference at the secondary receiver. For the proposed scheme, we first analyze the average secrecy rate, the secondary secrecy outage probability, and the ergodic secrecy rate, and derive their closed-form expressions. Following the above results, we optimally allocate the transmission power such that the secrecy rate is maximized under the secrecy outage probability constraint. For the optimization problem, an AI based simulated annealing algorithm is proposed to allocate the transmit power. Numerical results are presented to validate the performance analytical results and show the performance superiority of the proposed scheme in terms of the average secrecy rate.
  • Shilian Zheng, Shichuan Chen, Peihan Qi, Huaji Zhou, Xiaoniu Yang
    2020, 17(2): 138-148.
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    Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the effects of noise power uncertainty. We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals. We also use transfer learning strategies to improve the performance for real-world signals. Extensive experiments are conducted to evaluate the performance of this method. The simulation results show that the proposed method performs better than two traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method. In addition, the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals. Furthermore, the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning. Finally, experiments under colored noise show that our proposed method has superior detection performance under colored noise, while the traditional methods have a significant performance degradation, which further validate the superiority of our method.
  • Yuxiang Hu, Ziyong Li, Julong Lan, Jiangxing Wu, Lan Yao
    2020, 17(2): 149-162.
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    Software-Defined Networking(SDN) adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources. However, due to the limitation of traditional routing strategies relying on manual configuration, SDN may suffer from link congestion and inefficient bandwidth allocation among flows, which could degrade network performance significantly. In this paper, we propose EARS, an intelligence-driven experiential network architecture for automatic routing. EARS adapts deep reinforcement learning(DRL) to simulate the human methods of learning experiential knowledge, employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment. The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions. Under the network architecture, we design the network utility function with throughput and delay awareness, differentiate flows based on their size characteristics, and design a DDPG-based automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows. To validate the network architecture, we implement it on a real network environment. Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes(e.g. OSPF, ECMP).
  • Kusi Ankrah Bonsu, Weiwei Zhou, Su Pan, Yan Yan
    2020, 17(2): 163-175.
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    In this paper, an expression for the user's achievable data rate in the multi-user multiple-input multiple-output(MU-MIMO) system with limited feedback(LF) of channel state information(CSI) is derived. The energy efficiency(EE) is optimized through power allocation under quality of service(QoS) constraints. Based on mathematical equivalence and Lagrange multiplier approach, an energy-efficient unequal power allocation(EEUPA) with LF of CSI scheme is proposed. The simulation results show that as the number of transmitting antennas increases, the EE also increases which is promising for the next generation wireless communication networks. Moreover, it can be seen that the QoS requirement has an effect on the EE of the system. Ultimately, the proposed EEUPA with LF of CSI algorithm performs better than the existing energy-efficient equal power allocation(EEEPA) with LF of CSI schemes.
  • Meijia Wang, Qingshan Li, Yishuai Lin
    2020, 17(2): 176-205.
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    Personalized search utilizes user preferences to optimize search results, and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data. However, the behavioral data are noisy because users often clicked some irrelevant documents to find their required information, and the new user cold start issue represents a serious problem, greatly reducing the performance of personalized search. This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results, mine the knowledge of user interests, user influence and user relationships from online social networks, and use this knowledge to optimize the results returned by search engines. The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model. The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.
  • Li Jin, Guoan Zhang, Jue Wang, Hao Zhu, Wei Duan
    2020, 17(2): 206-219.
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    As an important part of future 5G wireless networks, a vehicular network demands safety, reliability and connectivity. In this context, networking survivability is usually considered an important metric to evaluate network performance. In this paper, we propose a survivability model for vehicle communication networking based on dual cluster heads, wherein a backup cluster head(CH) will be activated if the primary CH fails, thereby effectively enhancing the network lifetime. Additionally, we introduce a software rejuvenation strategy for the prime CH to further improve the survivability of the entire network. Using the Probabilistic Symbolic Model Checker(PRISM), we verify and discuss the proposed survivability model via numerical simulations. The results show that network survivability can be effectively improved by introducing an additional CH and further enhanced by adopting the software rejuvenation technique.
  • Xinji Tian, Wenjie Jia
    2020, 17(2): 220-231.
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    To reduce the interference among small cells of Ultra-Dense Networks(UDN), an improved Clustering-Assisted Resource Allocation(CARA) scheme is proposed in this paper. The proposed scheme is divided into three steps. First, an Interference-Limited Clustering Algorithm(ILCA) based on interference graph corresponding to the interference relationship between Femtocell Base Stations(FBSs), is proposed to group FBSs into disjoint clusters, in which a pre-threshold is set to constrain the sum of interference in each cluster, and a Cluster Head(CH) is selected for each cluster. Then, CH performs a two-stage sub-channel allocation within its associated cluster, where the first stage assigns one sub-channel to each user of the cluster and the second stage assigns a second sub-channel to some users. Finally, a power allocation method is designed to maximize throughput for a given clustering and sub-channel configuration. Simulation results indicate that the proposed scheme distributes FBSs into each cluster more evenly, and significantly improves the system throughput compared with the existing schemes in the same scenario.
  • Liang Zhong, Xueqian Zheng, Yong Liu, Mengting Wang, Yang Cao
    2020, 17(2): 232-238.
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    This paper investigates the content placement problem to maximize the cache hit ratio in device-to-device(D2D) communications overlaying cellular networks. We consider offloading contents by users themselves, D2D communications and multicast, and we analyze the relationship between these offloading methods and the cache hit ratio. Based on this relationship, we formulate the content placement optimization as a cache hit ratio maximization problem, and propose a heuristic algorithm to solve it. Numerical results demonstrate that the proposed scheme can outperform existing schemes in terms of the cache hit ratio.
  • Sahil Sholla, Roohie Naaz Mir, Mohammad Ahsan Chishti
    2020, 17(2): 239-252.
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    The Internet of Things promises to offer numerous societal benefits by providing a spectrum of user applications. However, ethical ramifications of adopting such pervasive technology on a society-wide scale have not been adequately considered. Smart things endowed with artificial intelligence may carry out decisions that entail ethical consequences. It is assumed that the functioning of a smart device does not involve any ethical responsibility vis-a-vis its application context. Such a perspective may precipitate situations that endanger essential human values or cause physical or emotional harm. Therefore, it is necessary to consider the design of ethics within intelligent systems to safeguard human interests. In order to address these concerns, we propose a novel method based on Boolean algebra that enables a machine to exhibit varying ethical behaviour by employing the concept of ethics categories and ethics modes. Such enhancement of smart things offers a way to design ethically compliant smart devices and paves way for human friendly technology ecosystems.