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    NETWORK-CONNECTED UAV COMMUNICATIONS
  • NETWORK-CONNECTED UAV COMMUNICATIONS
    A Trajectory Prediction Based Intelligent Handover Control Method in UAV Cellular Networks
    Bo Hu, Hanzhang Yang, Lei Wang, Shanzhi Chen
    2019, 16(1): 1-14.
    Abstract ( )   Knowledge map   Save
    The airborne base station (ABS) can provide wireless coverage to the ground in unmanned aerial vehicle (UAV) cellular networks. When mobile users move among adjacent ABSs, the measurement information reported by a single mobile user is used to trigger the handover mechanism. This handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users, which may lead to handover misjudgments and even communication interrupts. In this paper, we propose an intelligent handover control method in UAV cellular networks. Firstly, we introduce a deep learning model to predict the user trajectories. This prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow pedestrians. Secondly, we propose a handover decision method, which can calculate the users’ corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel, to make handover decisions accurately. Finally, we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover control method. The simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.
  • NETWORK-CONNECTED UAV COMMUNICATIONS
    Distributed Transmit Beamforming for UAV to Base Communications
    Yin Lu, Jun Fang, Zhong Guo, J. Andrew Zhang
    2019, 16(1): 15-25.
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    Distributed transmit beamforming (DTB) is very efficient for extending the communication distance between a swarm of UAVs and the base, particularly when considering the constraints in weight and battery life for payloads on UAVs. In this paper, we review major function modules and potential solutions in realizing DTB in UAV systems, such as timing and carrier synchronization, phase drift tracking and compensation, and beamforming vector generation and updating. We then focus on beamforming vector generation and updating, and introduce a concatenated training scheme, together with a recursive channel estimation and updating algorithm. We also propose three approaches for tracking the variation of channels and updating the vectors. The effectiveness of these approaches is validated by simulation results.
  • NETWORK-CONNECTED UAV COMMUNICATIONS
    An Energy Efficient Design for UAV Communication With Mobile Edge Computing
    Lingyan Fan, Wu Yan, Xihan Chen, Zhiyong Chen, Qingjiang Shi
    2019, 16(1): 26-36.
    Abstract ( )   Knowledge map   Save
    This paper considers a UAV communication system with mobile edge computing (MEC). We minimize the energy consumption of the whole system via jointly optimizing the UAV’s trajectory and task assignment as well as CPU's computational speed under the set of resource constrains. To this end, we first derive the energy consumption model of data processing, and then obtain the energy consumption model of fixed-wing UAV’s flight. The optimization problem is mathematically formulated. To address the problem, we first obtain the approximate optimization problem by applying the technique of discrete linear state-space approximation, and then transform the non-convex constraints into convex by using linearization. Furthermore, a concave-convex procedure (CCCP) based algorithm is proposed in order to solve the optimization problem approximately. Numerical results show the efficacy of the proposed algorithm.
  • NETWORK-CONNECTED UAV COMMUNICATIONS
    MmWave Beamforming for UAV Communications with Unstable Beam Pointing
    Weizhi Zhong, Lei Xu, Qiuming Zhu, Xiaomin Chen, Jianjiang Zhou
    2019, 16(1): 37-46.
    Abstract ( )   Knowledge map   Save
    Millimeter wave (mmWave) communications of unmanned aerial vehicles (UAVs) have drawn dramatic attentions for its flexibility on a variety of applications. Recently, channel tracking base on the spatial features has been proposed to solve the problem of beam misalignments due to the UAV navigation. However, unstable beam pointing caused by the non-ideal beam tracking environment may impact the performance of mmWave systems significantly. In this paper, an improved beamforming method is presented to overcome this shortcoming. Firstly, the effect of the beam deviation is analyzed through the establishment of the equivalent data rate. Then, combining the quantification of spatial angle and the improved orthogonal matching pursuit (OMP) algorithm, an optimized beam corresponding to the beam deviation is obtained. Simulation results show that the optimized beam of the proposed approach can effectively improve the spectral efficiency without improving the complexity when the beam pointing is unstable.
  • NETWORK-CONNECTED UAV COMMUNICATIONS
    Joint Subcarrier and Power Allocation for Multi-UAV Systems
    Xin Guan, Yang Huang, Qingjiang Shi
    2019, 16(1): 47-56.
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    This paper investigates subcarrier and power allocation in a multi-UAV OFDM system. The study considers a practical scenario, where certain subcarriers are unavailable for dynamic subcarrier allocation, on account of pre-allocation for burst transmissions. We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation, so as to maximize the sum rate of the uplink transmission in the multi-UAV OFDM system. The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error (MSE) problem. By this means, the allocation problem can be solved by the alternating optimization method. Besides, aiming at a lower-complexity solution, we propose a heuristic allocation scheme, where subcarrier allocation and transmit power allocation are separately optimized. In the heuristic scheme, closed-form solution can be obtained for power allocation. Simulation results demonstrate that in the presence of stretched subcarrier resource, the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme, offering a higher sum rate.
  • NETWORK-CONNECTED UAV COMMUNICATIONS
    Pricing Based Power Control for Inter-Cell UAV-Assisted Vehicle-to-Vehicle Underlay Communication
    Qin Wang, Ye Chen, Shu Yin, Lei Tian, Yongan Guo
    2019, 16(1): 57-68.
    Abstract ( )   Knowledge map   Save
    The high-capacity vehicle-to-vehicle (V2V) communication provides a promising solution to support ubiquitous media streaming and content sharing among vehicles. To extend the V2V links to multiple cells and manage the inter-cell interference, we proposed an UAV-assisted inter-cell V2V communication model, in which a shared UAV node is placed in the center of V2V users. By charging the V2V users underlay spectrum access fee, the cellular network earn profit at the cost of encountering co-channel interference from V2V links. A Stackelberg game is formulated to model the interactions between the V2V links and the cellular links, which are the game follower and the leader respectively. Their utility functions are maximized in terms of accessing price as well as transmit power of V2V users and UAV relays. Simulation evaluations verify that the power-price tradeoff between V2V network and cellular network has significant potentials to enhance their utility.
    • REVIEW PAPERS
  • REVIEW PAPERS
    A Survey of Testing for 5G: Solutions, Opportunities, and Challenges
    Ping Zhang, Xiaoli Yang, Jianqiao Chen, Yuzhen Huang
    2019, 16(1): 69-85.
    Abstract ( )   Knowledge map   Save
    With the development of wireless communication technology, the fifth generation mobile communications system (5G) emerges at a historic moment and devotes itself to open the curtain of the information age. Recently, in order to satisfy the requirement of different applications, various advanced 5G technologies have been developed in full swing. However, before applying these 5G related technologies in practical systems, effective testing methods are needed to evaluate these technologies in a real, comprehensive, rapid and flexible manner. However, the testing methods are faced with new challenges along with the continuous development of the new 5G technologies. In this paper, we present a survey of 5G testing, including solutions and opportunities. In particular, two cases are considered, i.e., channel modelling and over-the-air (OTA) testing of antenna systems. Specifically, a non-stationary channel model is proposed to characterize and test massive multiple-input multiple-output (MIMO) channel. In addition, we propose two probe subset selection algorithms for three-dimensional (3D) OTA testing, which minimizes the number of probe antennas while ensuring the accuracy of the target channel emulation. Finally, future research directions and challenges on 5G testing are given.
    • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    VLC-Based Indoor Positioning Algorithm Combined with OFDM and Particle Filter
    Jin Wang, Haoxu Li, Xiaofeng Zhang, Rangzhong Wu
    2019, 16(1): 86-96.
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    Visible light communications (VLC) have recently attracted a growing interest and can be a potential solution to realize indoor positioning, however, the performance of existing indoor positioning system is limited by multipath distortion inside a room. In order to combat the effect of multipath distortion, this paper proposes an LED-based indoor positioning algorithm combined with hybrid OFDM (HOFDM), in which asymmetrically clipped optical OFDM (ACO-OFDM) is transmitted on the odd subcarriers while using pulse amplitude modulated discrete multitone (PAM-DMT) to modulate the imaginary part of each even subcarrier. In this scheme, we take a combined approach where a received-signal-strength (RSS) technique is employed to determine the location of the receiver and realize the 3-D positioning by Trust-region-based positioning. Moreover, a particle filter is used to further improve the positioning accuracy. Results confirm that this proposed positioning algorithm can achieve high accuracy even with multipath distortion, and the algorithm has better performance when combined with particle filter.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Robust Beamforming for Secured Wireless Power Transfer in MIMO Magnetic Resonant Coupling System: A Probabilistic Approach
    Ling Xing, Kaikai Deng, Feifei Gao
    2019, 16(1): 97-107.
    Abstract ( )   Knowledge map   Save
    Wireless power transfer (WPT) to support mobile and portable devices is an emerging wireless technique. Among all kinds of approaches, magnetic resonance coupling (MRC) is an excellent one for mid-range WPT, which provides better mobility, flexibility, and convenience due to its simplicity in hardware implementation and longer transmission distances. In this paper, we consider an MRC-WPT system with multiple power transmitters, one intended power receiver and multiple unintended power receivers. We investigate the probabilistic robust beamforming designs and provide efficient algorithms to achieve the local optimums under two different criteria, i.e., total source power minimization problem and min-max unintended receiving power restriction problem. As the problems are quite typical in robust design situations, our proposed robust beamformers can be conveniently applied to other probabilistic robust design problems, thus reduce the complexity as well as improve the beamforming performance. Numerical results demonstrate that the proposed algorithms can significantly improve the performance as well as the robustness of the WPT system.
  • COMMUNICATIONS THEORIES & SYSTEMS
    An Efficient Update Strategy for Content Synchronization in Content-Centric Networking (CCN)
    Nidhi Lal, Shishupal Kumar, Vijay Kumar Chaurasiya
    2019, 16(1): 108-118.
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    Content-centric Networking (CCN) is progressively flattering the substitutable approach to the Internet architecture through illuminating information (content) dissemination on the Internet with content forenames. The emergent proportion of Internet circulation has expectant adjusting Content-centric architecture to enhance serve the user prerequisites of accessing content. In recent years, one of the key aspects of CCN is ubiquitous in-network caching, which has been widely received great attention in research interest. One foremost shortcoming of in-network caching is that content producers have no awareness about where their content is put in storage. Because routers in CCN have caching capabilities, therefore, each and every content router can cache the content item in its storage capacity. This is problematic in the case in which a producer wishes to update or make the changes in its content item. In this paper, we present an approach regarding how to address this issue with a scheme called efficient content update (ECU). Our proposed ECU scheme achieves content update via trifling packets that resemble contemporary CCN communication messages with the use of additional table. We measure the performance of ECU scheme by means of simulations and make available a comprehensive exploration of its results.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Video Description with Integrated Visual and Textual Information
    Yue Wang, Jinlai Liu, Xiaojie Wang
    2019, 16(1): 119-128.
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    Video Description aims to automatically generate descriptive natural language for videos. Due to the large volume of multi-modal data and successful implementations of Deep Neural Networks (DNNs), a wide range of models have been proposed. However, previous models learn insufficient linguistic information or correlation between visual and textual modalities. In order to address those problems, this paper proposes an integrated model using Long Short-Term Memory (LSTM). This proposed model consists of triple channels in parallel: a primary video description channel, a sentence-to-sentence channel for language learning, and a channel to integrate visual and textual information. Additionally, the parallel three channels are connected by LSTM weight matrices during training. The VD-ivt model is evaluated on two publicly available datasets, i.e. Youtube2Text and LSMDC. Experimental results demonstrate that the performance of the proposed model outperforms those benchmarks.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Defending Against Link Failure in Virtual Network Embedding Using a Hybrid Scheme
    Jianjian Ai, Hongchang Chen, Zehua Guo, Guozhen Cheng
    2019, 16(1): 129-138.
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    Nowadays network virtualization is utterly popular. As a result, how to protect the virtual networks from attacking on the link is increasingly important. Existing schemes are mainly backup-based, which suffer from data loss and are helpless to such attacks like data tampering. To offer high security level, in this paper, we first propose a multipath and decision-making (MD) scheme which applies multipath simultaneously delivery and decision-making for protecting the virtual network. Considering different security requirement for virtual link, we devise a hybrid scheme to protect the virtual links. For the critical links, MD scheme is adopted. For the other links, we adopt the Shared Backup Scheme. Our simulation results indicate the proposed scheme can significantly increase the security level of the critical link high in the loss of less acceptance ratio.
    • SIGNAL PROCESSING FOR COMMUNICATIONS
  • SIGNAL PROCESSING FOR COMMUNICATIONS
    Improved Eigenstructure-Based 2D DOA Estimation Approaches Based on Nyström Approximation
    Lingwen Zhang, Siliang Wu, Guanze Peng, Wenkao Yang
    2019, 16(1): 139-147.
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    In this paper, we propose improved approaches for two-dimensional (2D) direction-of-arrival (DOA) estimation for a uniform rectangular array (URA). Unlike the conventional eigenstructure-based estimation approaches such as Multiple Signals Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), the proposed approaches estimate signal and noise subspaces with Nyström approximation, which only need to calculate two sub-matrices of the whole sample covariance matrix and avoid the need to directly calculate the eigenvalue decomposition (EVD) of the sample covariance matrix. Hence, the proposed approaches can improve the computational efficiency greatly for large-scale URAs. Numerical results verify the reliability and efficiency of the proposed approaches.
    • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    The Way to Apply Machine Learning to IoT Driven Wireless Network from Channel Perspective
    Wei Li, Jianhua Zhang, Xiaochuan Ma, Yuxiang Zhang, Hua Huang, Yongmei Cheng
    2019, 16(1): 148-164.
    Abstract ( )   Knowledge map   Save
    Internet of Things (IoT) is one of the targeted application scenarios of fifth generation (5G) wireless communication. IoT brings a large amount of data transported on the network. Considering those data, machine learning (ML) algorithms can be naturally utilized to make network efficiently and reliably. However, how to fully apply ML to IoT driven wireless network is still open. The fundamental reason is that wireless communication pursuits the high capacity and quality facing the challenges from the varying and fading wireless channel. So in this paper, we explore feasible combination for ML and IoT driven wireless network from wireless channel perspective. Firstly, a three-level structure of wireless channel fading features is defined in order to classify the versatile propagation environments. This three-layer structure includes scenario, meter and wavelength levels. Based on this structure, there are different tasks like service prediction and pushing, self-organization networking, self adapting largescale fading modeling and so on, which can be abstracted into problems like regression, classification, clustering, etc. Then, we introduce corresponding ML methods to different levels from channel perspective, which makes their interdisciplinary research promisingly.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Machine Learning for 5G and Beyond: From Model-Based to Data-Driven Mobile Wireless Networks
    Tianyu Wang, Shaowei Wang, Zhi-Hua Zhou
    2019, 16(1): 165-175.
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    During the past few decades, mobile wireless communications have experienced four generations of technological revolution, namely from 1G to 4G, and the deployment of the latest 5G networks is expected to take place in 2019. One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system. We believe that the answer lies in the huge volumes of data produced by the network itself, and machine learning may become a key to exploit such information. In this paper, we elaborate why the conventional model-based paradigm, which has been widely proved useful in pre-5G networks, can be less efficient or even less practical in the future 5G and beyond mobile networks. Then, we explain how the data-driven paradigm, using state-of-the-art machine learning techniques, can become a promising solution. At last, we provide a typical use case of the data-driven paradigm, i.e., proactive load balancing, in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    A Novel Automatic Modulation Classification Method Based on Dictionary Learning
    Kezhong Zhang, Li Xu, Zhiyong Feng, Ping Zhang
    2019, 16(1): 176-192.
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    Automatic Modulation Classification (AMC) is an important technology used to recognize the modulation type. A dictionary set was trained via signals with known modulation schemes in cooperative scenarios. Then we classify the modulation scheme of the signals received in the non-cooperative environment according to its sparse representation. Furthermore, we proposed a novel approach called Fast Block Coordinate descent Dictionary Learning (FBCDL). Moreover, the convergence of FBCDL was proved and we find that our proposed method achieves lower complexity. Experimental results indicate that our proposed FBCDL achieves better classification accuracy than traditional methods.