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    Guest Editorial
  • Guest Editorial
    An Overview of Wireless Communication Technology Using Deep Learning
    Jiyu Jiao, Xuehong Sun, Liang Fang, Jiafeng Lyu
    2021, 18(12): 1-36.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    with the development of 5G, the future wireless communication network tends to be more and more intelligent. In the face of new service demands of communication in the future such as super-heterogeneous network, multiple communication scenarios, large number of antenna elements and large bandwidth, new theories and technologies of intelligent communication have been widely studied, among which Deep Learning (DL) is a powerful technology in artificial intelligence(AI). It can be trained to continuously learn to update the optimal parameters. This paper reviews the latest research progress of DL in intelligent communication, and emphatically introduces five scenarios including Cognitive Radio (CR), Edge Computing (EC), Channel Measurement (CM), End to end Encoder/Decoder (EED) and Visible Light Communication (VLC). The prospect and challenges of further research and development in the future are also discussed.
  • Guest Editorial
    Relay-Assisted Secure Short-Packet Transmission in Cognitive IoT with Spectrum Sensing
    Yong Chen, Yu Zhang, Baoquan Yu, Tao Zhang, Yueming Cai
    2021, 18(12): 37-50.
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    Cognitive Internet of Things (IoT) has attracted much attention due to its high spectrum utilization. However, potential security of the shortpacket communications in cognitive IoT becomes an important issue. This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming (MRC/ZFB) scheme to guarantee the secrecy performance of dual-hop short-packet communications in cognitive IoT. This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ratio (SNR) regime and the infinite blocklength. In addition, the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput. The numerical results verify the impact of the system parameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a secrecy constraint. It is shown that the proposed scheme achieves better secrecy performance than other benchmark schemes.
  • Guest Editorial
    Frequency-Hopping Frequency Reconnaissance and Prediction for Non-cooperative Communication Network
    Gao Li, Wei Wang, Guoru Ding, Qihui Wu, Zitong Liu
    2021, 18(12): 51-64.
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    The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication networks. Since the frequency-hopping (FH) sequence is usually generated by a certain model with certain regularity, the FH frequency is thus predictable. In this paper, we investigate the FH frequency reconnaissance and prediction of a non-cooperative communication network by effective FH signal detection, time-frequency (TF) analysis, wavelet detection and frequency estimation. With the intercepted massive FH signal data, long short-term memory (LSTM) neural network model is constructed for FH frequency prediction. Simulation results show that our parameter estimation methods could estimate frequency accurately in the presence of certain noise. Moreover, the LSTM-based scheme can effectively predict FH frequency and frequency interval.
  • Guest Editorial
    Passive Localization of Multiple Sources Using Joint RSS and AOA Measurements in Spectrum Sharing System
    Kang Li, Yutao Jiao, Yehui Song, Jinghua Li, Chao Yue
    2021, 18(12): 65-80.
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    In spectrum sharing systems, locating multiple radiation sources can efficiently find out the intruders, which protects the shared spectrum from malicious jamming or other unauthorized usage. Compared to single-source localization, simultaneously locating multiple sources is more challenging in practice since the association between measurement parameters and source nodes are not known. Moreover, the number of possible measurements-source associations increases exponentially with the number of sensor nodes. It is crucial to discriminate which measurements correspond to the same source before localization. In this work, we propose a centralized localization scheme to estimate the positions of multiple sources. Firstly, we develop two computationally light methods to handle the unknown RSS-AOA measurements-source association problem. One method utilizes linear coordinate conversion to compute the minimum spatial Euclidean distance summation of measurements. Another method exploits the long-short-term memory (LSTM) network to classify the measurement sequences. Then, we propose a weighted least squares (WLS) approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem. Numerical results demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial scenarios where the sources are in close proximity and the measurement noise is strong.
  • Guest Editorial
    Specific Emitter Identification for IoT Devices Based on Deep Residual Shrinkage Networks
    Peng Tang, Yitao Xu, Guofeng Wei, Yang Yang, Chao Yue
    2021, 18(12): 81-93.
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    Specific emitter identification can distinguish individual transmitters by analyzing received signals and extracting inherent features of hard-ware circuits. Feature extraction is a key part of traditional machine learning-based methods, but manual extraction is generally limited by prior professional knowledge. At the same time, it has been noted that the performance of most specific emitter identification methods degrades in the low signal-to-noise ratio (SNR) environments. The deep residual shrinkage network (DRSN) is proposed for specific emitter identification, particularly in the low SNRs. The soft threshold can preserve more key features for the improvement of performance, and an identity shortcut can speed up the training process. We collect signals via the receiver to create a dataset in the actual environments. The DRSN is trained to automatically extract features and implement the classification of transmitters. Experimental results show that DRSN obtains the best accuracy under different SNRs and has less running time, which demonstrates the effectiveness of DRSN in identifying specific emitters.
  • Guest Editorial
    Primary User Adversarial Attacks on Deep Learning-Based Spectrum Sensing and the Defense Method
    Shilian Zheng, Linhui Ye, Xuanye Wang, Jinyin Chen, Huaji Zhou, Caiyi Lou, Zhijin Zhao, Xiaoniu Yang
    2021, 18(12): 94-107.
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    The spectrum sensing model based on deep learning has achieved satisfying detection performence, but its robustness has not been verified. In this paper, we propose primary user adversarial attack (PUAA) to verify the robustness of the deep learning based spectrum sensing model. PUAA adds a carefully manufactured perturbation to the benign primary user signal, which greatly reduces the probability of detection of the spectrum sensing model. We design three PUAA methods in black box scenario. In order to defend against PUAA, we propose a defense method based on autoencoder named DeepFilter. We apply the long short-term memory network and the convolutional neural network together to DeepFilter, so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense. Extensive experiments are conducted to evaluate the attack effect of the designed PUAA method and the defense effect of DeepFilter. Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model. In addition, the experimental results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA without affecting the detection performance of the model.
  • Guest Editorial
    Intelligent Spectrum Management Based on Radio Map for Cloud-Based Satellite and Terrestrial Spectrum Shared Networks
    Ximu Zhang, Min Jia, Xuemai Gu, Qing Guo
    2021, 18(12): 108-118.
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    Cloud-based satellite and terrestrial spectrum shared networks (CB-STSSN) combines the triple advantages of efficient and flexible network management of heterogeneous cloud access (H-CRAN), vast coverage of satellite networks, and good communication quality of terrestrial networks. Thanks to the complementary coverage characteristics, anytime and anywhere high-speed communications can be achieved to meet the various needs of users. The scarcity of spectrum resources is a common problem in both satellite and terrestrial networks. In order to improve resource utilization, the spectrum is shared not only within each component but also between satellite beams and terrestrial cells, which introduces inter-component interferences. To this end, this paper first proposes an analytical framework which considers the inter-component interferences induced by spectrum sharing (SS). An intelligent SS scheme based on radio map (RM) consisting of LSTM-based beam prediction (BP), transfer learning-based spectrum prediction (SP) and joint non-preemptive priority and preemptive priority (J-NPAP)-based proportional fair spectrum allocation is than proposed. The simulation result shows that the spectrum utilization rate of CB-STSSN is improved and user blocking rate and waiting probability are decreased by the proposed scheme.
  • Guest Editorial
    Proactive Spectrum Monitoring for Suspicious Wireless Powered Communications in Dynamic Spectrum Sharing Networks
    Yu Zhang, Guojie Hu, Yueming Cai
    2021, 18(12): 119-138.
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    This paper studies the proactive spectrum monitoring with one half-duplex spectrum monitor (SM) to cope with the potential suspicious wireless powered communications (SWPC) in dynamic spectrum sharing networks. The jamming-assisted spectrum monitoring scheme via spectrum monitoring data (SMD) transmission is proposed to maximize the sum ergodic monitoring rate at SM. In SWPC, the suspicious communications of each data block occupy multiple independent blocks, with a block dedicated to the wireless energy transfer by the energy-constrained suspicious nodes with locations in a same cluster (symmetric scene) or randomly distributed (asymmetric scene) and the remaining blocks used for the information transmission from suspicious transmitters (STs) to suspicious destination (SD). For the symmetric scene, with a given number of blocks for SMD transmission, namely the jamming operation, we first reveal that SM should transmit SMD signal (jam the SD) with tolerable maximum power in the given blocks. The perceived suspicious signal power at SM could be maximized, and thus so does the corresponding sum ergodic monitoring rate. Then, we further reveal one fundamental trade-off in deciding the optimal number of given blocks for SMD transmission. For the asymmetric scene, a low-complexity greedy block selection scheme is proposed to guarantee the optimal performance. Simulation results show that the jamming-assisted spectrum monitoring schemes via SMD transmission achieve much better performance than conventional passive spectrum monitoring, since the proposed schemes can obtain more accurate and effective spectrum characteristic parameters, which provide basic support for fine-grained spectrum management and a solution for spectrum security in dynamic spectrum sharing network.
  • Guest Editorial
    Coalitional Game Based Joint Beamforming and Power Control for Physical Layer Security Enhancement in Cognitive IoT Networks
    Zhaoye Xu, Aiyan Qu, Kang An
    2021, 18(12): 139-150.
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    In this paper, the physical layer secure transmission in multi-antenna multi-user cognitive internet-of-thing (IoT) network is investigated, where the coalitional game based joint beamforming and power control scheme is proposed to improve the achievable security of cognitive IoT devices. Specifically, the secondary network consisting of a muti-antenna secondary transmitter, multiple secondary users (SUs), is allowed to access the licensed spectrum resource of primary user (PU) with underlay approach in the presence of an unauthorized eavesdropper. Based on the Merge-Split-Rule, coalitional game is formulated among distributed secondary users with cooperative receive beamforming. Then, an alternative optimization method is used to obtain the optimized beamforming and power allocation schemes by applying the up-downlink duality. The simulation results demonstrate the effectiveness of our proposed scheme in improving the SU's secrecy rate and system utility while guaranteeing PU's interference threshold.
    • COVER PAPER
  • COVER PAPER
    Evolution of Road Traffic Congestion Control: A Survey from Perspective of Sensing, Communication, and Computation
    Wenwei Yue, Changle Li, Guoqiang Mao, Nan Cheng, Di Zhou
    2021, 18(12): 151-177.
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    Road traffic congestion can inevitably degrade road infrastructure and decrease travel efficiency in urban traffic networks, which can be relieved by employing appropriate congestion control. According to different developmental driving forces, in this paper, the evolution of road traffic congestion control is divided into two stages. The ever-growing number of advanced sensing techniques can be seen as the key driving force of the first stage, called the sensing stage, in which congestion control strategies experienced rapid growth owing to the accessibility of traffic data. At the second stage, i.e., the communication stage, communication and computation capability can be regarded as the identifying symbols for this stage, where the ability of collecting finer-grained insight into transportation and mobility reality improves dramatically with advances in vehicular networks, Big Data, and artificial intelligence. Specifically, as the pre-requisite for congestion control, in this paper, existing congestion detection techniques are first elaborated and classified. Then, a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control, vehicle route guidance, and their combined techniques is provided. In this regard, the evolution of these strategies with continuous development of sensing, communication, and computation capability are also introduced. Finally, the paper concludes with several research challenges and trends to fully promote the integration of advanced techniques for traffic congestion mitigation in transportation systems.
    • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    An Area Optimization Based Cooperative Localization Algorithm with Node Selection
    Ke Han, Chongyu Zhang, Huashuai Xing, Yunfei Xu
    2021, 18(12): 178-195.
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    In recent years, position information has become a key feature to drive location and context aware services in mobile communication. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, due to weak signals, multipath or non-line-of-sight signal propagation, accurately and efficiently localizing targets in harsh indoor environments remains a challenging problem. To improve the performance in harsh environment with insufficient anchors, cooperative localization has emerged. In this paper, a novel cooperative localization algorithm, named area optimization and node selection based sum-product algorithm over a wireless network (AN-SPAWN), is described and analyzed. To alleviate the high computational complexity and build optimized cooperative cluster, a node selection method is designed for the cooperative localization algorithm. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algorithms in the harsh indoor environments.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Cubic Metric Reduction for Repetitive CAZAC Sequences in Frequency Domain
    Yajun Zhao, Juan Liu, Saijin Xie
    2021, 18(12): 196-207.
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    To meet the requirements of Occupied Channel Bandwidth (OCB) of unlicensed spectrum, in NR-based Access to Unlicensed Spectrum (NR-U) of 5G New Radio (NR) system, the channels of PRACH and PUCCH have to employ some frequency domain sequence repetition schemes. These repetition schemes cause serious Cubic Metric (CM) problems for these channels, although these two types of channels are composed of Constant Amplitude Zero Auto-correlation (CAZAC) sequences. Considering the properties of CAZAC sequences, which are used for PRACH and PUCCH (refer to PUCCH format 0 and format 1) in 5G NR system, in this paper, we propose some new schemes of CM reduction for these two channels taking into account the design principles to ensure the sequence performance of the auto-correlation and cross-correlation. Then the recommended CM reduction schemes are evaluated and the optimized parameters are further provided considering both CM performance and the complexity.
  • COMMUNICATIONS THEORIES & SYSTEMS
    The Dual-Polarized Staggered Stacked Patches Antenna
    Xinyu Cao, Jinling Zhang, Hongzhen Yang, Hourong Li
    2021, 18(12): 208-218.
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    A new dual-polarized staggered and stacked patches antenna with wide impedance bandwidth and high isolation is proposed. The antenna consists of two groups of radiation patches, in 7 layers, and uses the orthogonal adjacent coupling structure on staggered layer to excite a pair of linear polarization modes. Thanks to the staggered feeder mode, it has increased the isolation performance between ports and compressed the transverse size of the antenna. As a result of the combination of staggered stack-up between the patches and the stepped gradient shape of the main radiating patches, it has effectively expanded the impedance bandwidth of the antenna. The proposed antenna is simulated, fabricated and measured. The staggered feeding structure effectively reduces the cross-sectional area of the antenna, and greatly improves the isolation between feeding ports. The measurement results show that the impedance bandwidths for vertical and horizontal polarization modes are 40.2% (638-960 MHz) and 40.0% (645-968 MHz) respectively when the return loss is lower than -10 dB, and the isolation between feeding ports is better than -30 dB. Meanwhile, the antenna has a stable and symmetrical radiation pattern across the working band, therefore making it suitable to be used as antenna and antenna array element of mobile wireless communication base stations.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Novel DDS Based OFDM Transmitter Structure without IFFT and Interpolation Filter
    Sanjun Liu, Guangming Gan
    2021, 18(12): 219-229.
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    The traditional orthogonal frequency division multiplexing (OFDM) transmitter is implemented by inverse fast Fourier transform (IFFT), up-sampling and low pass shaping filter (LPSF), which occupy a large number of hardware resources and have long latency. To further meet the 5G and future 6G communication requirements, this paper proposes a novel direct digital synthesis (DDS) based OFDM transmitter structure that can replace these modules. Due to the strong parallelism of the system structure, it is very suitable for implementation on field programable gate array (FPGA) platform. After making two special simplifications to the primary structure, the refined structure becomes very simple compared with the traditional structures. Most attractively, the proposed structure has the following three advantages that i) the data transformation from frequency domain to time domain has zero latency, ii) the transformation length does not need to be an integer power of 2 and iii) the structure does not even need to use any multiplier, thus leading to low implementation complexity and high speed. Comparative experiments are carried out on Intel FPGA platform which show that our DDS based structure can save more than half of the resources compared with the traditional structures and can provide the same bit error rate (BER) performance under the condition without using any LPSF.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Spectrum Efficiency Maximization for Cooperative Power Beacon-Enabled Wireless Powered Communication Networks
    Wenjun Xu, Wei Chen, Yongjian Fan, Zhi Zhang, Xinxin Shi
    2021, 18(12): 230-251.
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    We consider a spectrum efficiency (SE) maximization problem for cooperative power beacon-enabled wireless powered communication networks (CPB-WPCNs), where each transmitter harvests energy from multi-antenna power beacons (PBs) and transmits data to the corresponding receiver. %Specifically, we consider the out-band wireless energy transfer scenario, where energy signals and information signals are transmitted over different frequency bands, meaning that the wireless energy transfer and the data transmission are performed simultaneously. For data transmission, both orthogonal transmission, i.e., the time splitting (TS) mode, and non-orthogonal transmission, i.e., the interference channel (IC) mode, are considered. Aiming to improve the system SE, the energy beamformers of PBs, the transmit power, and the transmit time duration of transmitters are jointly optimized. For the TS mode, the original non-convex problem is transformed into a convex optimization problem by means of variable substitution and semidefinite relaxation (SDR). The rank-one nature of this SDR is proved, and then a Lagrange-dual based fast algorithm is proposed to obtain the optimal solution with much lower complexity. For the IC mode, to conquer the strong non-convexity of the problem, a branch-reduce-and-bound (BRB) monotonic optimization algorithm is designed as a benchmark. Furthermore, a low-complexity distributed successive convex approximation (SCA) algorithm is presented. %, which enables parallel and efficient on-line implementation of the proposed scheme. Finally, simulation results validate the performance of the proposed algorithms, achieving optimality within only 1%$\sim$2% computation time compared to the CVX solver in the TS mode and achieving 98% of the optimal performance in the IC mode.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Multi-Objective Bacterial Foraging Optimization Algorithm Based on Effective Area in Cognitive Emergency Communication Networks
    Shibing Zhang, Xue Ji, Lili Guo, Zhihua Bao
    2021, 18(12): 252-269.
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    Cognitive emergency communication networks can meet the requirements of large capacity, high density and low delay in emergency communications. This paper analyzes the properties of emergency users in cognitive emergency communication networks, designs a multi-objective optimization and proposes a novel multi-objective bacterial foraging optimization algorithm based on effective area (MOBFO-EA) to maximize the transmission rate while maximizing the lifecycle of the network. In the algorithm, the effective area is proposed to prevent the algorithm from falling into a local optimum, and the diversity and uniformity of the Pareto-optimal solutions distributed in the effective area are used to evaluate the optimization algorithm. Then, the dynamic preservation is used to enhance the competitiveness of excellent individuals and the uniformity and diversity of the Pareto-optimal solutions in the effective area. Finally, the adaptive step size, adaptive moving direction and inertial weight are used to shorten the search time of bacteria and accelerate the optimization convergence. The simulation results show that the proposed MOBFO-EA algorithm improves the efficiency of the Pareto-optimal solutions by approximately $55%$ compared with the MOPSO algorithm and by approximately $60%$ compared with the MOBFO algorithm and has the fastest and smoothest convergence.
    • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    Relay Beamforming Design for Physical Layer Secure Communication via Line Search Algorithm
    Siqi Chen, Cong Sun
    2021, 18(12): 270-284.
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    This paper considers a physical layer security model in wireless communications. Two legitimate users communicate through several relays with the presence of an eavesdropper. We jointly design the relay beamforming weights and minimize the total relay transmit power, while ensuring users' Quality of Services and preventing the information being eavesdropped at the same time. The problem is a robust optimization problem, because of the imperfect channel state information from users and relays to the eavesdropper. First the original problem is simplified, where the high order robust terms are omitted. Then we design an iterative algorithm based on line search, by solving two Quadratically Constrained Quadratic Programming subproblems and a one-dimensional subproblem. Simulation results indicate that the proposed algorithm outperforms the state of the arts.
  • NETWORKS & SECURITY
    Three-Dimensional Trajectory Optimization for Secure UAV-Enabled Cognitive Communications
    Yuhan Jiang, Jia Zhu
    2021, 18(12): 285-296.
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    Unmanned aerial vehicles (UAVs) are envisioned as a promising means of providing wireless services for various complex terrains and emergency situations. In this paper, we consider a wireless UAV-enabled cognitive communication network, where a rotary-wing UAV transmits confidential information to a ground cognitive user over the spectrum assigned to primary users (PUs), while eavesdroppers attempt to wiretap the legitimate transmission. In order to enhance the secrecy performance of wireless communications, the secrecy rate (SR) of the UAV-enabled cognitive communication system is maximized through optimizing UAV three-dimensional (3D) flying trajectory while satisfying the requirements of UAV's initial and final locations and guaranteeing the constraint of maximum speed of UAV and the interference threshold of each PU. However, the formulated SR maximization (SRM) problem is non-convex. For the purpose of dealing with this intractable problem, we employ the difference of two-convex functions approximation approach to convert the non-convex optimization problem into a convex one, which is then solved through applying standard convex optimization techniques. Moreover, an iterative 3D trajectory optimization algorithm for SRM scheme is proposed to achieve the near-optimal 3D trajectory. Simulation results show that our proposed 3D trajectory optimization based SRM algorithm has good convergence, and the proposed SRM scheme outperforms the benchmark approach in terms of the SR performance.
    • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Integration of Communication and Computing in Blockchain-Enabled Multi-Access Edge Computing Systems
    Zhonghua Zhang, Jie Feng, Qingqi Pei , Le Wang, Lichuan Ma
    2021, 18(12): 297-314.
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    Blockchain and multi-access edge computing (MEC) are two emerging promising technologies that have received extensive attention from academia and industry. As a brand-new information storage, dissemination and management mechanism, blockchain technology achieves the reliable transmission of data and value. While as a new computing paradigm, multi-access edge computing enables the high-frequency interaction and real-time transmission of data. The integration of communication and computing in blockchain-enabled multi-access edge computing networks has been studied without a systematical view. In the survey, we focus on the integration of communication and computing, explores the mutual empowerment and mutual promotion effects between the blockchain and MEC, and introduces the resource integration architecture of blockchain and multi-access edge computing. Then, the paper summarizes the applications of the resource integration architecture, resource management, data sharing, incentive mechanism, and consensus mechanism, and analyzes corresponding applications in real-world scenarios. Finally, future challenges and potentially promising research directions are discussed and present in detail.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    An Efficient Three-Factor Privacy-Preserving Authentication and Key Agreement Protocol for Vehicular Ad-Hoc Network
    Tao Xu, Cheng Xu, Zisang Xu
    2021, 18(12): 315-331.
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    As an indispensable part of the Intelligent Transportation System (ITS), the vehicular ad-hoc network (VANET) has attracted widespread attention from academia and industry. In order to ensure the security of VANET, vehicles need to be authenticated before accessing the network. Most existing authentication protocols in VANET adopt the Trusted Authority (TA) with centralized structure which is responsible for the authentication tasks of all vehicles. However, the large-scale network consume a lot of computing resources, which leads to unacceptable delay in message transmission in VANET. For reducing the computational cost of TA, an efficient three-factor privacy-preserving authentication and key agreement protocol was proposed in our paper. Different from before, the RoadSide Unit (RSU) no longer acts as an intermediate node but is responsible for assisting user authentication, which lead to the computational cost of TA is very low. Through formal and informal analysis, our protocol demonstrates excellent security. Compared with previous studies, our work emerges advantages and superiorities in the following aspects: computational cost, communication cost, security properties and functions, message loss ratio, and message delay. These data and evidence indicate that our protocol is an ideal choice for large-scale VANET.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Medical Treatment Process Modeling Based on Process Mining and Treatment Patterns
    Liqin Yang, Guosheng Kang, Liang Zhang
    2021, 18(12): 332-349.
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    Clinical practice guidelines (CPGs) are statements relating to evidence-based and economically reasonable medical treatment processes (MTPs) for certain clinical circumstances. The executable MTPs in healthcare information systems can assist the clinical processes. In our previous work, several treatment patterns and their modeling proposals were proposed to reduce the effort spent in modeling MTPs. However, given a CPG document, all the process elements are modeled manually. Besides, business process mining can extract MTP models automatically from execution logs. However, the existing process mining algorithms focus on the control-flow of structured processes. This paper proposes an integrated framework for modeling executable MTPs based on process mining and treatment patterns, taking both the efficiency of mining and the quality of modeling into account. In this framework, an execution log processing approach is presented to identify the subsequences and decision points conforming to the treatment patterns and represent them with abstract activities. Experiments on a synthetic log of the non-secondary hypertension MTP and empirical findings demonstrate the effectiveness of our approach. The results show that the process mining in our approach framework can automatically generate more accurate MTP models, and the subprocess models based on treatment patterns make the models easy to understand.