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.

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.

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.

Laser-assisted growth of hierarchically architectured 2D MoS2 crystals on metal substrate for potential energy applications

Recently, there has been substantial interest in the large-scale synthesis of hierarchically architectured transition metal dichalcogenides and designing electrodes for energy conversion and storage applications such as electrocatalysis, rechargeable batteries, and supercapacitors. Here we report a novel hybrid laser-assisted micro/nanopatterning and sulfurization method for rapid manufacturing of hierarchically architectured molybdenum disulfide (MoS2) layers directly on molybdenum sheets. This laser surface structuring not only provides the ability to design specific micro/nanostructured patterns but also significantly enhances the crystal growth kinetics. Micro and nanoscale characterization methods are employed to study the morphological, structural, and atomistic characteristics of the formed crystals at various laser processing and crystal growth conditions. To compare the performance characteristics of the laser-structured and unstructured samples, Li-ion battery cells are fabricated and their energy storage capacity is measured. The hierarchically architectured MoS2 crystals show higher performance with specific capacities of about 10 mAh cm-2, at a current rate of 0.1 mA cm-2. This rapid laser patterning and growth of 2D materials directly on conductive sheets may enable the future large-scale and roll-to-roll manufacturing of energy and sensing devices.

Airborne Sensor Network for Atmospheric Profiling

The need for higher spatial/temporal resolution in-situ atmospheric sensing has been established by both weather and climate researchers. In order to address this need, an airborne wireless sensor network called GlobalSense is currently being developed. GlobalSense is based on low-cost airborne probes that collect environmental data as they fall slowly through the atmosphere and on portable base stations that receive the data being collected. This paper presents an overview of this GlobalSense system as well as preliminary results from ground-based system testing.

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ELVIS Ⅱ^+的信号与系统实验设计及实现

目前"信号与系统"课程实验大多采用实验箱实现,其不足之处是学生不能直接触电子元器件、自由选择元件、搭建自行设计的电路。ELVIS Ⅱ+集成了12套常用虚拟仪器,具有原型开发板。本文将其用于信号与系统实验,代替传统实验箱以弥补其不足。本文介绍了所设计的实验内容及相应的实现方案,指出了实验中存在的一些问题,并提出了相应的解决方法。

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.

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.

A High-Speed DLL-Based Hybrid Phase Conjugator for 5G Beamforming

A delay-locked loop based hybrid phase conjugator (DLL-HPC) is presented as a possible solution for 5G beamforming. Theoretical background, unique capabilities, and experimental verification are presented. The proposed DLL-HPC provides backwards compatibility with existing beamforming protocols as well as sub-millisecond beamsteering and automatic mobile target tracking with zero communication overhead. A proof-of-concept DLL-HPC prototype has been constructed from commercially available components to operate in the 5G NR-FR1 band, indicating that the technique can be readily adopted with available technology.

Design and optimization of a 2.4 GHz RF front-end with an on-chip balun

A 2.4 GHz low-power,low-noise and highly linear receiver front-end with a low noise amplifier（LNA） and balun optimization is presented.Direct conversion architecture is employed for this front-end.The on-chip balun is designed for single-to-differential conversion between the LNA and the down-conversion mixer,and is optimized for the best noise performance of the front-end.The circuit is implemented with 0.35μm SiGe BiCMOS technology.The front-end has three gain steps for maximization of the input dynamic range.The overall maximum gain is about 36 dB.The double-sideband noise figure is 3.8 dB in high gain mode and the input referred third-order intercept point is 12.5 dBm in low gain mode.The down-conversion mixer has a tunable parallel R-C load at the output and an emitter follower is used as the output stage for testing purposes.The total front-end dissipation is 33 mW under a 2.85 V supply and occupies a 0.66 mm^2 die size.

Massively Deployable, Low-Cost Airborne Sensor Motes for Atmospheric Characterization

A low-cost airborne sensor mote has been designed for deployment en masse to characterize atmospheric conditions. The designed environmental sensing mote, or eMote, was inspired by the natural shape of auto-rotating maple seeds to fall slowly and gather data along its descent. The eMotes measure and transmit temperature, air pressure, relative humidity, and wind speed estimates alongside GPS coordinates and timestamps. Up to 2080 eMotes can be deployed simultaneously with a 1 Hz sampling rate, but the system capacity increases by 2600 eMotes for every second added between samples. All measured and reported data falls within accuracy requirements for reporting with both the World Meteorological Organization (WMO) and the National Oceanic and Atmospheric Administration (NOAA). This paper presents the design and validation of the eMote system alongside discussions on the implementation of a large-scale, low-cost sensor network. The eMote represents unprecedented in-situ atmospheric measurement capabilities with the ability to deploy more than 260 times the number of sensing units as the most comparable commercially available dropsonde.

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.

High-precision time domain reactive power measurement in the presence of interharmonics

When interharmonics exist in power system signals,large errors emerge in traditional time domain reactive power measurement.In this paper,we present a novel time domain integral method with good effect of restraining interharmonics,synchronization error,and white noise,as well as the principle of the selection of the sampling periods when employing this approach.The current signal and phase-shifted voltage signal are reconstructed after the harmonic components of signals are extracted,so that the interharmonics are filtered.The influence of the synchronization error on the measurement is reduced through removing the weight coefficients of the reactive components.In the simulation,we apply several cosine windows to the proposed method and analyze signals containing both harmonics and interharmonics.The results show that,in the presence of interharmonics,synchronization error,and white noise (with a fundamental signal-to-noise ratio of 40 dB) all together,the relative errors are within the magnitude of 10 4,which perfectly satisfies the practical requirement.

An asymmetric MOSFET-C band-pass filter with on-chip charge pump auto-tuning

An asymmetric MOSFET-C band-pass filter （BPF） with on chip charge pump auto-tuning is presented. It is implemented in UMC （United Manufacturing Corporation） 0.18 μm CMOS process technology. The filter system with auto-tuning uses a master-slave technique for continuous tuning in which the charge pump outputs 2.663 V, much higher than the power supply voltage, to improve the linearity of the filter. The main filter with third order low-pass and second order high-pass properties is an asymmetric band-pass filter with bandwidth of 2.730-5.340 MHz. The in-band third order harmonic input intercept point （ⅡP3） is 16.621 dBm, with 50Ω as the source impedance. The input referred noise is about 47.455 μVrms. The main filter dissipates 3.528 mW while the auto-tuning system dissipates 2.412 mW from a 1.8 V power supply. The filter with the auto-tuning system occupies 0.592 mm2 and it can be utilized in GPS （global positioning system） and Bluetooth systems.

A wideband RF amplifier for satellite tuners

This paper presents the design and measured performance ofa wideband amplifier for a direct conversion satellite tuner. It is composed of a wideband low noise amplifier （LNA） and a two-stage RF variable gain amplifier （VGA） with linear gain in dB and temperature compensation schemes. To meet the system linearity requirement, an improved distortion compensation technique and a bypass mode are applied on the LNA to deal with the large input signal. Wideband matching is achieved by resistive feedback and an off-chip LC-ladder matching network. A large gain control range （over 80 dB） is achieved by the VGA with process voltage and temperature compensation and dB linearization. In total, the amplifier consumes up to 26 mA current from a 3.3 V power supply. It is fabricated in a 0.35μm SiGe BiCMOS technology and occupies a silicon area of 0.25 mm^2.

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.

Heterogeneous multi-player imitation learning

This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input dynamics.We propose a model-free data-driven inverse reinforcement learning(RL)algorithm for a leaner to find the cost functions of a N-player Nash expert system given the expert's states and control inputs.This allows us to address the imitation learning problem without prior knowledge of the expert's system dynamics.To achieve this,we provide a basic model-based algorithm that is built upon RL and inverse optimal control.This serves as the foundation for our final model-free inverse RL algorithm which is implemented via neural network-based value function approximators.Theoretical analysis and simulation examples verify the methods.