Adversarial attacks and defenses for digital communication signals identification

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

Learning-Based Joint Service Caching and Load Balancing for MEC Blockchain Networks

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among MECS.In addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads.In this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain networks.We formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)problem.To address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)approach.The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

Wi-Wheat+:Contact-free wheat moisture sensing with commodity WiFi based on entropy

In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time monitoring.The proposed system is based on Commodity WiFi and is easy to deploy.Leveraging WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel entropy.The feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.

基于轻量级深度神经网络的电磁信号调制识别技术

针对6G时代将会是移动通信与人工智能紧密结合的时代,产生数量庞大的边缘智能信号处理节点的趋势,提出了一种可部署于资源受限的边缘设备上的高效智能电磁信号识别模型。首先,通过绘制电磁信号的星座图将电磁信号具象为二维图像,并根据归一化点密度对星座图上色以实现特征增强;然后,使用二值化深度神经网络对其进行识别,在保证识别准确率的同时明显降低了模型存储开销以及计算开销。采用电磁信号调制识别问题进行验证,实验选取常用的8种数字调制信号,选择加性高斯白噪声为信道环境。实验结果表明,所提方案可以在信噪比为-6~6 d B的噪声条件下获得96.1%的综合识别率,网络模型大小仅为166 KB,部署于树莓派4B的执行时间为290 ms,相比于同规模的全精度网络,准确率提升了0.6%,模型缩减到1/26.16,运行时间缩减到1/2.37。

A survey of free space optical networks

Free Space Optical （FSO） networks, also known as optical wireless networks, have emerged as viable candidates for broadband wireless communications in the near future. The range of the potential application of FSO networks is extensive, from home to satellite. However, FSO networks have not been popularized because of insufficient availability and reliability. Researchers have focused on the problems in the physical layer in order to exploit the properties of wireless optical channels. However, recent technological developments with successful results make it practical to explore the advantages of the high bandwidth. Some researchers have begun to focus on the problems of network and upper layers in FSO networks. In this survey, we classify prospective global FSO networks into three subnetworks and give an account of them. We also present state-of- the-art research and discuss what kinds of challenges exist.

Directional neighbor discovery in mmWave wireless networks

The directional neighbor discovery problem,i.e,spatial rendezvous,is a fundamental problem in millimeter wave(mmWave)wireless networks,where directional transmissions are used to overcome the high attenuation.The challenge is how to let the transmitter and the receiver beams meet in space under deafness caused by directional transmission and reception,where no control channel,prior information,and coordination are available.In this paper,we present a Hunting based Directional Neighbor Discovery(HDND)scheme for ad hoc mmWave networks,where a node follows a unique sequence to determine its transmission or reception mode,and continuously r0-tates its directional beam to scan the neighborhood for other mmWave nodes.Through a rigorous analysis,we derive the conditions for ensured neighbor discovery,as well as a bound for the worst-case discovery time and the impact of sidelobes.We validate the analysis with extensive simulations and demonstrate the superior perfor-mance of the proposed scheme over several baseline schemes.

RFHUI:an RFID based human-unmanned aerial vehicle interaction system in an indoor environment

In this paper,we present an RFID based human and Unmanned Aerial Vehicle(UAV)Interaction system,termed RFHUI,to provide an intuitive and easy-to-operate method to navigate a UAV in an indoor environment.It relies on the passive Radio-Frequency IDentification(RFID)technology to precisely track the pose of a handheld controller,and then transfer the pose information to navigate the UAV.A prototype of the handheld controller is created by attaching three or more Ultra High Frequency(UHF)RFID tags to a board.A Commercial Off-The-Shelf(COTS)RFID reader with multiple antennas is deployed to collect the observations of the tags.First,the precise positions of all the tags can be obtained by our proposed method,which leverages a Bayesian filter and Channel State Information(CSI)phase measurements collected from the RFID reader.Second,we introduce a Singular Value Decomposition(SVD)based approach to obtain a 6-DoF(Degrees of Freedom)pose of the controller from estimated positions of the tags.Furthermore,the pose of the controller can be precisely tracked in a real-time manner,while the user moves the controller.Finally,control commands will be generated from the controller's pose and sent to the UAV for navigation.The performance of the RFHUI is evaluated by several experiments.The results show that it provides precise poses with 0.045m mean error in position and 2.5∘mean error in orientation for the controller,and enables the controller to precisely and intuitively navigate the UAV in an indoor environment.

A Survey of Multimedia Big Data

Multimedia big data brings tremendous challenges as well as opportunities for multimedia applications/services. In this paper, we present a survey and tutorial for multimedia big data. After discussing the characteristics of multimedia big data such as human-centricity, multimodality, heterogeneity, unprecedented volume, and so on, this paper provides an overview of the state-of-the-art of multimedia big data, reviews the latest related technologies, and discusses the technical challenges. We conclude this paper with a discussion of open problems and future directions.

Internet Multimedia Traffic Classification from QoS Perspective Using Semi-Supervised Dictionary Learning Models

To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.

MEASUREMENTS AND APPLICATIONS OF QoE FOR MULTIMEDIA COMMUNICATIONS

With the rapid development of smart devices and mobile networks,multimedia services will dominate most of data traffic in 4G/5G networks.Applications -such as conversational videos,online multimedia sharing,remote education,etc.have gained their popularity and will become more ubiquitous among customers.Tra-

RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.

Digital Communications and Networks

Guest editorial The emerging applications,suchas Augmented and Virtual Realities(AR/VR),InternetofThings(IoT),4K/8Kstreaming,raisestrongrequirementsto movecomputationfrom thecloudtotheedgestobecloser tousers.There are tremendous possibilities for the network edge,which may includeavariety ofentities,such as small datacenters,end devices,and resource-abundant network nodes.These together provide the network computation and intelligence to users.

Editorial： Special issue on ＂next generation wireless communication technologies＂

There have been considerable research efforts on developing the enabling technologies for the next generation of wireless communication systems all over the world, leading towards the future 5G and beyond 5G wireless systems. The key demands now are user-eentric （instead of the traditional carrier-centric） mobile applications, high mobile data traffic volume, large number of connected devices, long device/network lifetime, improved Quality of Services （QoS） and Quality of Experience （QoE） for users, i.e., high-transmission rate, low delay, and small jitter etc. In recent years, a lot of promising wireless technologies have been proposed or developed to improve the quality of wireless communications and to enable new wireless applications. Such technologies are focused on new spectrum such as millimeter wave, wider bandwidths, new modulation techniques, enhanced small cells, massive MIMO, and so on.

On distributed power control in full duplex wireless networks

In this paper, we first consider the problem of distributed power control in a Full Duplex （FD） wireless network consisting of multiple pairs of nodes, within which each node needs to communicate with its corresponding node. We aim to find the optimal transmition power for the FD transmitters such that the network-wide capacity is maximized. Based on the high Signal-to-Interference-Plus-Noise Ratio （SINR） approximation and a more general approximation method for logarithm functions, we develop effective distributed power control algorithms with the dual decomposition approach. We also extend the work to the general FD network scenario, which can be decomposed into subproblems of isolated nodes, paths, and cycles. The corresponding power control problem is then be solved with the distributed algorithm. The proposed algorithms are validated with simulation studies.

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems(6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system-Automatic Dependent Surveillance-Broadcast(ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods.Finally, we conclude this paper with a discussion of open problems in this area.

Enhancing the performance of future wireless networks with software-defined networking

To provide ubiquitous Internet access under the explosive increase of applications and data traffic,the current network architecture has become highly heterogeneous and complex,making network management a challenging task.To this end,software-defined networking(SDN) has been proposed as a promising solution.In the SDN architecture,the control plane and the data plane are decoupled,and the network infrastructures are abstracted and managed by a centralized controller.With SDN,efficient and flexible network control can be achieved,which potentially enhances network performance.To harvest the benefits of SDN in wireless networks,the software-defined wireless network(SDWN) architecture has been recently considered.In this paper,we first analyze the applications of SDN to different types of wireless networks.We then discuss several important technical aspects of performance enhancement in SDN-based wireless networks.Finally,we present possible future research directions of SDWN.

Multi-State-Space Reasoning Reinforcement Learning for Long-Horizon RFID-Based Robotic Searching and Planning Tasks

In recent years,reinforcement learning(RL)has shown high potential for robotic applications.However,RL heavily relies on the reward function,and the agent merely follows the policy to maximize rewards but lacks reasoning ability.As a result,RL may not be suitable for long-horizon robotic tasks.In this paper,we propose a novel learning framework,called multiple state spaces reasoning reinforcement learning(SRRL),to endow the agent with the primary reasoning capability.First,we abstract the implicit and latent links between multiple state spaces.Then,we embed historical observations through a long short-term memory(LSTM)network to preserve long-term memories and dependencies.The proposed SRRL’s ability of abstraction and long-term memory enables agents to execute long-horizon robotic searching and planning tasks more quickly and reasonably by exploiting the correlation between radio frequency identification(RFID)sensing properties and the environment occupation map.We experimentally validate the efficacy of SRRL in a visual game-based simulation environment.Our methodology outperforms three state-of-the-art baseline schemes by significant margins.