China Communications

Archive

  • Select all
    |
    Guest Editorial
  • Guest Editorial
    Performance Analysis for Large Intelligent Surface Assisted Vehicular Networks
    Yiyang Ni, Yaxuan Liu, Jin Zhou, Qin Wang, Haitao Zhao, Hongbo Zhu
    2021, 18(3): 1-17.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Large intelligent surface (LIS) is considered as a new solution to enhance the performance of wireless networks[1]. LIS comprises low-cost passive elements which can be well controlled. In this paper, a LIS is invoked in the vehicular networks. We analyze the system performance under Weibull fading. We derive a novel exact analytical expression for outage probability in closed form. Based on the analytical result, we discuss three special scenarios including high SNR case, low SNR case, as well as weak interference case. The corresponding approximations for three cases are provided, respectively. In order to gain more insights, we obtain the diversity order of outage probability and it is proved that the outage probability at high SNR depends on the interference, threshold and fading parameters which leads to 0 diversity order. Furthermore, we investigate the ergodic achievable rate of LIS-assisted vehicular networks and present the closed-form tight bounds. Similar to the outage performance, three special cases are studied and the asymptotic expressions are provided in simple forms. A rate ceiling is shown for high SNRs due to the existence of interference which results 0 high SNR slope. Finally, we give the energy efficiency of LIS-assisted vehicular network. Numerical results are presented to verify the accuracy of our analysis. It is evident that the performance of LIS-assisted vehicular networks with optimal phase shift scheme exceeds that of traditional vehicular networks and random phase shift scheme significantly.
  • Guest Editorial
    Passive Beamforming Design for Intelligent Reflecting Surface Assisted MIMO Systems
    Chenghao Feng, Wenqian Shen, Xinyu Gao, Jianping An
    2021, 18(3): 18-28.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Intelligent reflecting surfaces (IRSs) constitute passive devices, which are capable of adjusting the phase shifts of their reflected signals, and hence they are suitable for passive beamforming. In this paper, we conceive their design with the active beamforming action of multiple-input multiple-output (MIMO) systems used at the access points (APs) for improving the beamforming gain, where both the APs and users are equipped with multiple antennas. Firstly, we decouple the optimization problem and design the active beamforming for a given IRS configuration. Then we transform the optimization problem of the IRS-based passive beamforming design into a tractable non-convex quadratically constrained quadratic program (QCQP). For solving the transformed problem, we give an approximate solution based on the technique of widely used semidefinite relaxation (SDR). We also propose a low-complexity iterative solution. We further prove that it can converge to a locally optimal value. Finally, considering the practical scenario of discrete phase shifts at the IRS, we give the quantization design for IRS elements on basis of the two solutions. Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks.
  • Guest Editorial
    Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication Systems in Mobility Scenarios
    Zhendong Mao, Mugen Peng, Xiqing Liu
    2021, 18(3): 29-38.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Reconfigurable intelligent surface (RIS) can manipulate the wireless propagation environment by smartly adjusting the amplitude/phase in a programmable panel, enjoying the improved performance. The accurate acquisition of the instantaneous channel state information (CSI) in the cascaded RIS chain makes an indispensable contribution to the performance gains. However, it is quite challenging to estimate the CSI in a time-variant scenario due to the limited signal processing capability of the passive elements embedded in a RIS pannel. In this work, a channel estimation scheme for the RIS-assisted wireless communication system is proposed, which is demonstrated to perform well in a time-variant scenario. The cascaded RIS channel is modeled as a state-space model based upon the mobility situations. In addition, to fully exploit the time correlation of channel, Kalman filter is employed by taking the prior information of channels into account. Further, the optimal reflection coefficients are derived according to the minimum mean square error (MMSE) criterion. Numerical results show that the proposed methods exhibit superior performance if compared with a conventional channel estimation scheme.
  • Guest Editorial
    Secure Multigroup Multicast Communication Systems via Intelligent Reflecting Surface
    Weiping Shi, Jiayu Li, Guiyang Xia, Yuntian Wang, Xiaobo Zhou, Yonghui Zhang, Feng Shu
    2021, 18(3): 39-51.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    This paper considers a secure multigroup multicast multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS). Specifically, we aim to minimize the transmit power at Alice via jointly optimizing the transmit beamformer, artificial noise (AN) vector and phase shifts at the IRS subject to the secrecy rate constraints as well as the unit modulus constraints of IRS phase shifts. To tackle the optimization problem, we first transform it into a semidefinite relaxation (SDR) problem, and then alternately update the transmit beamformer and AN matrix as well as the phase shifts at the IRS. In order to reduce the high computational complexity, we further propose a low-complexity algorithm based on second-order cone programming (SOCP). We decouple the optimization problem into two sub-problems and optimize the transmit beamformer, AN vector and the phase shifts alternately by solving two corresponding SOCP sub-problem. Simulation results show that the proposed SDR and SOCP schemes require half or less transmit power than the scheme without IRS, which demonstrates the advantages of introducing IRS and the effectiveness of the proposed methods.
  • Guest Editorial
    Secrecy Rate Analysis for Reconfigurable Intelligent Surface-Assisted MIMO Communications with Statistical CSI
    Jie Liu, Jun Zhang, Qi Zhang, Jue Wang, Xinghua Sun
    2021, 18(3): 52-62.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In this paper, a reconfigurable intelligent surface (RIS)-assisted MIMO wireless secure communication system is considered, in which a base station (BS) equipped with multiple antennas exploits statistical channel state information to communicate with a legitimate multi-antenna user, in the presence of an eavesdropper, also equipped with multiple antennas. We firstly obtain an analytical expression of the ergodic secrecy rate based on the results of large-dimensional random matrix theory. Then, a jointly alternating optimization algorithm with the method of Taylor series expansion and the projected gradient ascent method is proposed to design the transmit covariance matrix at the BS, as well as the diagonal phase-shifting matrix to maximize the ergodic secrecy rate. Simulations are conducted to demonstrate the accuracy of the derived analytical expressions, as well as the superior performance of our proposed algorithm.
  • Guest Editorial
    Joint Beamforming Optimization for Reconfigurable Intelligent Surface-Enabled MISO-OFDM Systems
    Keming Feng, Xiao Li, Yu Han, Yijian Chen
    2021, 18(3): 63-79.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In this paper, we investigate the reconfigurable intelligent surface (RIS)-enabled multiple-input-single-output orthogonal frequency division multiplexing (MISO-OFDM) system under frequency-selective channels, and propose a low-complexity alternating optimization (AO) based joint beamforming and RIS phase shifts optimization algorithm to maximize the achievable rate. First, with fixed RIS phase shifts, we devise the optimal closed-form transmit beamforming vectors corresponding to different subcarriers. Then, with given active beamforming vectors, near-optimal RIS reflection coefficients can be determined efficiently leveraging fractional programming (FP) combined with manifold optimization (MO) or majorization-minimization (MM) framework. Additionally, we also propose a heuristic RIS phase shifts design approach based on the sum of subcarrier gain maximization (SSGM) criterion requiring lower complexity. Numerical results indicate that the proposed MO/MM algorithm can achieve almost the same rate as the upper bound achieved by the semidefinite relaxation (SDR) algorithm, and the proposed SSGM based scheme is only slightly inferior to the upper bound while has much lower complexity. These results demonstrate the effectiveness of the proposed algorithms.
  • Guest Editorial
    RIS-Aided Constant-Envelope Beamforming for Multiuser Wireless Power Transfer: A Max-Min Approach
    Huiyuan Yang, Chang Cai, Xiaojun Yuan, Yingchang Liang
    2021, 18(3): 80-90.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    As a revolutionary hardware technology that can reconfigure the propagation environment, reconfigurable intelligent surfaces (RISs) have been regarded as a promising solution to enhance wireless networks. In this paper, we consider a multiuser multiple-input single-output (MISO) wireless power transfer (WPT) system, which is assisted by several RISs. In order to improve energy efficiency and reduce hardware cost, we consider that the energy transmitter (ET) in the WPT system is equipped with a constant-envelope analog beamformer, instead of a digital beamformer. Focusing on user fairness, we study a minimum received power maximization problem by jointly optimizing the ET beamforming and the RIS phase shifts, subject to the constant-envelope constraints. We iteratively solve this non-convex max-min problem by leveraging both the successive convex approximation (SCA) method and the alternating direction method of multipliers (ADMM) algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm and show attractive performance gain brought by RISs.
    • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Multi-Source Spinal Coding for Coded Caching Multicast Transmissions in Wireless Networks
    Aimin Tang, Xudong Wang
    2021, 18(3): 91-104.
    Abstract ( )   Knowledge map   Save
    Recently, coded caching has been treated as a promising technique to alleviate the traffic burden in wireless networks. To support high efficient coded caching multicast transmissions, the time-varying heterogeneous channel conditions need to be considered. In this paper, a practical and novel multi-source spinal coding (MSSC) scheme is developed for coded caching multicast transmissions under heterogeneous channel conditions. By exploring joint design of network coding and spinal coding (SC), MSSC can achieve unequal link rates in multicast transmissions for different users. Moreover, by leveraging the rateless feature of SC in our design, MSSC can well adapt the link rates of all users in multicast transmissions without any feedback of time-varying channel conditions. A maximum likelihood (ML) based decoding process for MSSC is also developed, which can achieve a linear complexity with respect to the user number in the multicast transmission. Simulation results validate the effectiveness of the MSSC scheme. Compared to the existing scheme, the sum rate of MSSC in multicast transmissions is improved by about 20%. When applying MSSC in coded caching systems, the total transmission time can be reduced by up to 48% for time-varying channels.
  • COMMUNICATIONS THEORIES & SYSTEMS
    APPLSS: Adaptive Privacy Preserved Location Sharing Scheme Based on Attribute-Based Encryption
    Yiliang Han, Shuaishuai Zhu, Yu Li, Xi Lin
    2021, 18(3): 105-121.
    Abstract ( )   Knowledge map   Save
    Unauthorized access to location information in location-based service is one of the most critical security threats to mobile Internet. In order to solve the problem of quality of location sharing while keeping privacy preserved, adaptive privacy preserved location sharing scheme called APPLSS is proposed, which is based on a new hierarchical ciphertext-policy attribute-based encryption algorithm. In the algorithm, attribute authority sets the attribute vector according to the attribute tags of registration from the location service providers. Then the attribute vector can be adaptively transformed into an access structure to control the encryption and decryption. The APPLSS offers a natural hierarchical mechanism in protecting location information when partially sharing it in mobile networks. It allows service providers access to end user’s sensitive location more flexibly, and satisfies a sufficient-but-no-more strategy. For end-users, the quality of service is obtained while no extra location privacy is leaked. To improve service response performance, outsourced decryption is deployed to avoid the bottlenecks of the service providers and location information providers. The performance analysis and experiments show that APPLSS is an efficient and practical location sharing scheme.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Improving the Covertness in the Physical-Layer Authentication
    Ning Xie, TianXing Hu
    2021, 18(3): 122-131.
    Abstract ( )   Knowledge map   Save
    A well-designed Physical-Layer Authentication (PLA) scheme should consider three properties: covertness, robustness, and security. However, the three properties always cause some dilemmas, e.g., higher covertness leading to lower robustness. This paper concerns the problem of improving the covertness without sacrificing the robustness. This problem is important because of the following reasons: reducing the errors in recovered source message, improving the security, and ease of constructing a multi-factor authentication system. In this paper, we propose three covert PLA schemes to address the problem. In the first scheme, we improve the covertness by reducing the modification ratio on the source message based on an encoding mechanism. In the second scheme, we improve the covertness by optimizing the superimposing angle, which maximizes the minimum distance between the tagged symbols and the boundary line of the demodulation decision for the source message. In the third scheme, referred to as the hybrid scheme, we further improve the covertness by jointly using the advantages of both the above two schemes. Our experimental results show that when the SNR at a legitimate receiver is 25 dB, as compared with the prior scheme, the first scheme improves the covertness by 17.74%, the second scheme improves the covertness by 28.79%, and the third scheme improves the covertness by 32.09%, while they have similar robustness as that of the prior scheme.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Energy-Efficient Resource Allocation for Heterogeneous Network with Grouping D2D
    Jing Cao, Xin Song, Siyang Xu, Zhigang Xie, Yanbo Xue
    2021, 18(3): 132-141.
    Abstract ( )   Knowledge map   Save
    In this paper, a new communication model is built named grouping D2D (GD2D). Different from the traditional D2D coordination, we proposed GD2D communication in licensed and unlicensed spectrum simultaneously. We formulate a resource allocation problem, which aims at maximizing the energy efficiency (EE) of the system while guaranteeing the quality-of-service (Qos) of users. To efficiently solve this problem, the non-convex optimization problem is first transformed into a convex optimization problem. By transforming the fractional-form problem into an equivalent subtractive-form problem, an iterative power allocation algorithm is proposed to maximize the system EE. Moreover, the optimal closed-form power allocation expressions are derived by the Lagrangian approach. Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Learning to Optimize for Resource Allocation in LTE-U Networks
    Guanhua Chai, Weihua Wu, Qinghai Yang, Runzi Liu, Kyung Sup Kwak
    2021, 18(3): 142-154.
    Abstract ( )   Knowledge map   Save
    This paper proposes a deep learning (DL) resource allocation framework to achieve the harmonious coexistence between the transceiver pairs (TPs) and the Wi-Fi users in LTE-U networks. The nonconvex resource allocation is considered as a constrained learning problem and the deep neural network (DNN) is employed to approximate the optimal resource allocation decisions through unsupervised manner. A parallel DNN framework is proposed to deal with the two optimization variables in this problem, where one is the licensed power allocation unit and the other is the unlicensed time fraction occupied unit. Besides, to guarantee the feasibility of the proposed algorithm, the Lagrange dual method is used to relax the constraints into the DNN training process. Then, the dual variable and the DNN parameter are alternating update via the batch-based gradient decent method until the training process converges. Numerical results show that the proposed algorithm is feasible and has better performance than other general algorithms.
    • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    Adaptive Secure Transmission for Wireless Powered Communication Networks
    Yong Wang, Weiwei Yang, Tao Zhang, Yong Chen, Xiaohui Shang, Quan Wang
    2021, 18(3): 155-173.
    Abstract ( )   Knowledge map   Save
    This paper investigates secure transmission in a wireless powered communication network (WPCN) with an energy harvesting (EH) source configured with multiple antennas. In the WPCN, the EH source harvests energy from the radio frequency (RF) signals broadcasted by a power beacon (PB), and purely relies on the harvested energy to communicate with the destination in the presence of passive eavesdroppers. It is noteworthy that the RF-EH source is equipped with a finite energy storage to accumulate the harvested energy for the future usage. Moreover, due to energy-constraint and complexity-limitation, the multi-antenna source is only configured with a single RF-chain. To enhance the security for the WPCN, we propose two adaptive transmission schemes, i.e., energy-aware transmit antenna selection (EATAS) and energy-aware differential spatial modulation (EADSM). According to the energy status and the channel quality, the source adaptively decides whether to transmit confidential information or harvest RF energy. To evaluate the secrecy performance of the proposed schemes, we first study the evolution of the energy storage, and then derive the analytical expressions of connection outage probability (COP), secrecy outage probability (SOP) and efficient secrecy throughput (EST). Numerical results demonstrate that our proposed schemes outperform the existing schemes, i.e., time-switching based TAS (TS-TAS) and accumulate-then-transmit (ATT). And, increasing the transmit power of the PB or the capacity of the source's energy storage is helpful to improve the secrecy performance. Moreover, there exists an optimal transmission rate for each proposed scheme to achieve best secrecy performance.
  • NETWORKS & SECURITY
    GPS Spoofing Attack Detection in Smart Grids Based on Improved CapsNet
    Yuancheng Li, Shanshan Yang
    2021, 18(3): 174-186.
    Abstract ( )   Knowledge map   Save
    This paper analyzes the influence of the global positionong system (GPS) spoofing attack (GSA) on phasor measurement units (PMU) measurements. We propose a detection method based on improved Capsule Neural Network (CapsNet) to handle this attack. In the improved CapsNet, the gated recurrent unit (GRU) is added to the front of the full connection layer of the CapsNet. The improved CapsNet trains and updates the network parameters according to the historical measurements of the smart grid. The detection method uses different structures to extract the temporal and spatial features of the measurements simultaneously, which can accurately distinguish the attacked data from the normal data, to improve the detection accuracy. Finally, simulation experiments are carried out on IEEE 14-, IEEE 118-bus systems. The experimental results show that compared with other detection methods, our method is proved to be more efficient.
    • SIGNAL PROCESSING
  • SIGNAL PROCESSING
    Graph Laplacian Matrix Learning from Smooth Time-Vertex Signal
    Ran Li, Junyi Wang, Wenjun Xu, Jiming Lin, Hongbing Qiu
    2021, 18(3): 187-204.
    Abstract ( )   Knowledge map   Save
    In this paper, we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as “time-vertex signal”. To realize this, we first represent the signals on a joint graph which is the Cartesian product graph of the time- and vertex- graphs. By assuming the signals follow a Gaussian prior distribution on the joint graph, a meaningful representation that promotes the smoothness property of the joint graph signal is derived. Furthermore, by decoupling the joint graph, the graph learning framework is formulated as a joint optimization problem which includes signal denoising, time- and vertex- graphs learning together. Specifically, two algorithms are proposed to solve the optimization problem, where the discrete second-order difference operator with reversed sign (DSODO) in the time domain is used as the time-graph Laplacian operator to recover the signal and infer a vertex-graph in the first algorithm, and the time-graph, as well as the vertex-graph, is estimated by the other algorithm. Experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively infer meaningful time- and vertex- graphs from noisy and incomplete data.
  • SIGNAL PROCESSING
    WiFi CSI Gesture Recognition Based on Parallel LSTM-FCN Deep Space-Time Neural Network
    Zhiling Tang, Qianqian Liu, Minjie Wu, Wenjing Chen, Jingwen Huang
    2021, 18(3): 205-215.
    Abstract ( )   Knowledge map   Save
    In this study, we developed a system based on deep space-time neural networks for gesture recognition. When users change or the number of gesture categories increases, the accuracy of gesture recognition decreases considerably because most gesture recognition systems cannot accommodate both user differentiation and gesture diversity. To overcome the limitations of existing methods, we designed a one-dimensional parallel long short-term memory-fully convolutional network (LSTM-FCN) model to extract gesture features of different dimensions. LSTM can learn complex time dynamic information, whereas FCN can predict gestures efficiently by extracting the deep, Abstract: features of gestures in the spatial dimension. In the experiment, 50 types of gestures of five users were collected and evaluated. The experimental results demonstrate the effectiveness of this system and robustness to various gestures and individual changes. Statistical analysis of the recognition results indicated that an average accuracy of approximately 98.9$\%$ was achieved.
  • SIGNAL PROCESSING
    Inter-Carrier Interference-Aware Sparse Time-Varying Underwater Acoustic Channel Estimation Based on Fast Reconstruction Algorithm
    Zhengqiang Yan, Xinghai Yang, Lijun Sun, Jingjing Wang
    2021, 18(3): 216-225.
    Abstract ( )   Knowledge map   Save
    In this paper, a fast orthogonal matching pursuit (OMP) algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic (UWA) channel estimation. The channel estimation consists of calculating amplitude, delay and Doppler scaling factor of each path using the received multi-path signal. This algorithm, called as OIP-FOMP, can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels. In this algorithm, repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance. Efficient QR decomposition is used to estimate the path amplitude, and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix. Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm, without any loss of accuracy.
    • EMERGING TECHNOLOGLES & APPLICATIONS
  • EMERGING TECHNOLOGLES & APPLICATIONS
    Integrated Route Planning and Resource Allocation for Connected Vehicles
    Quan Yuan, Bo Chen, Guiyang Luo, Jinglin Li, Fangchun Yang
    2021, 18(3): 226-239.
    Abstract ( )   Knowledge map   Save
    Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities. As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly, it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds. Existing methods usually perform resource allocation in a fairly effective but still reactive manner, which is subject to the capacity of nearby edge clouds. To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity, we proactively balance the edge computing demands across edge clouds by appropriate route planning. In this paper, route planning and resource allocation are jointly optimized to enhance intelligent driving. We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality. In large-scale optimization, backpressure algorithm is used to conduct route planning and load balancing across edge clouds. In small-scale optimization, game-theoretic multi-agent learning is exploited to perform regional resource allocation. The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.
  • EMERGING TECHNOLOGLES & APPLICATIONS
    Resource Allocation for Massive Machine Type Communications in the Finite Blocklength Regime
    Baoquan Yu, Dan Wu, Yueming Cai, Yan Wu, Zhongwu Xiang
    2021, 18(3): 240-250.
    Abstract ( )   Knowledge map   Save
    Massive machine type communications (mMTC) have been regarded as promising applications in the future. One main feature of mMTC is short packet communication. Different from traditional long packet communication, short packet communication suffers from transmission rate degradation and a significant error rate is introduced. In this case, traditional resource allocation scheme for mMTC is no longer applicable. In this paper, we explore resource allocation for cellular-based mMTC in the finite blocklength regime. First, to mitigate the load of the base station (BS), we establish a framework for cellular-based mMTC, where MTCGs reuse the resources of cellular users (CUs), aggregate the packets generated by MTCDs, and forward them to the BS. Next, we adopt short packet theory to obtain the minimum required blocklength of a packet that transmits a certain amount of information. Then, by modeling the process of MTCGs-assisted communication as a queuing process, we derive the closed-form expression of the average delay of all MTCDs. Guided by this, we propose a joint power allocation and spectrum sharing scheme to minimize the average delay. Finally, the simulation results verify the correctness of the theoretical results and show that the proposed scheme can reduce the average delay efficiently.
  • EMERGING TECHNOLOGLES & APPLICATIONS
    Intelligent Task Offloading and Collaborative Computation over D2D Communication
    Cuili Jiang, Tengfei Cao, Jianfeng Guan
    2021, 18(3): 251-263.
    Abstract ( )   Knowledge map   Save
    In this paper, the problem of computation offloading in the edge server is studied in a mobile edge computation (MEC)-enabled cell networks that consists of a base station (BS) integrating edge servers, several terminal devices and collaborators. In the considered networks, we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading. First, a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power. Second, based on the Lyapunov stochastic optimization theory, the system stability problem is transformed into a queue stability issue, and the optimal computation offloading is obtained by solving these three sub-problems: task allocation control, task execution control and queue update, respectively. Moreover, rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency, energy consumption and time delay compared to the mobility-aware and migration-enabled approach, Full BS and Full local.