Deep Learning-based Wireless Signal Classification in the IoT Environment

With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own physical characteristics with signal modulation and demodulation scheme.When there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication areas.This paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention mechanism.CNN can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence problem.Furthermore,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time.To accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention mechanism.As a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences.In a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 dB.The accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%.

Design and application of wireless signal strength measurement system on the near-ground

The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if not in a complicated topography.Testing results show that it is hardly to characterize the radio propagation properties for the antenna installed on the ground.In order to ensure a successful communication,the radio frequency(RF)wireless signal intensity monitor system was designed.We can get the wireless link transmission loss through measuring signal strength from received node.The test shows that the near-ground wireless signal propagation characteristics still can be characterized by the log distance propagation loss model.These results will conduce to studying the transmission characteristic of Near-Earth wireless signals and will predict the coverage of the earth's surface wireless sensor network.

Multi Multi-Task Learning with Dynamic Splitting for Open Open-Set Wireless Signal Recognition

Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class splitting(ICS)that splits samples of known classes to imitate unknown classes has achieved great performance.However,this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment.In this paper,we train a multi-task learning(MTL)net-work based on the characteristics of wireless signals to improve the performance in new scenes.Besides,we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples.To be specific,we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold.We conduct several experi-ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset,and the analytical results demonstrate the effective-ness of the proposed method.

WirelessHART无线传感器网络系统的硬件设计与测试

由于Zig Bee终端节点功耗高、精度低、通信距离短等原因,以Wireless HART标准为平台,设计了无线通信、主板电路以及传感器等模块。对设备节点关键参数、设备节点入网以及无线传感器网络系统整体性能进行测试。测试结果表明:设计的Wireless HART设备节点相比Zig Bee终端节点功耗低、精度高、通信距离明显增加,节点可快速加入网关与上位机组建的Wireless HART网络并且完成数据传输。在干扰信号环境下,可通过增加少许供电电压、调整天线增益和延长测试时间来减小信号扰动,提高系统抗扰性。

Signal Path Reckoning Localization Method in Multipath Environment

In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.

基于LoRa通信的无线发射台信号智能监测技术研究

为实现大面积无线发射台信号覆盖监测,有效地抵御监测过程中多径干扰和频率选择性衰减等干扰因素的影响,研究基于LoRa通信的无线发射台信号智能监测技术。在无线发射台发出信号后,转换成数字信号进行对比,识别出有效发射台信号后,通过级联型LoRa组网方式以及LoRa通信技术,将数据发送到服务器中存储,实现无线发射台信号的传输。利用循环位移因子对基带产生的Chirp信号完成调频扩频调制,扩展信号频谱,增强信号抗干扰能力以及传输可靠性,增强信号监测的效果。实验结果表明:该方法能够在距离LoRa终端设备2.5 km处依旧接收到发射台信号,丢包率为12%,能够准确地监测到无线发射台是否存在发射信号。

Grid-Based Localization Mechanism with Mobile Reference Node in Wireless Sensor Networks

Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.

Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix

A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However,it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property(St RIP) via regular low density parity check(RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that St RIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.

A Human Body Posture Recognition Algorithm Based on BP Neural Network for Wireless Body Area Networks

Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.

基于ZigBee通信网络的农机作业监测系统设计

首先,介绍了ZigBee通信网络技术,并设计了系统总体设计方案;然后,分别从转速测量模块、振动信号采集模块和割台高度检测设计了系统硬件部分;最后,针对设备节点和协调器两部分设计了ZigBee无线网络传输软件,实现了农机作业监测系统。测试结果表明:系统能够正确采集农机作业参数并通过无线网络发送到云平台,证明系统的可靠性和可行性。

基于电场耦合的电能信号并行传输系统串扰抑制方法

针对较大功率电场耦合无线电能传输系统信号双向高速传输的需求,该文提出一种基于部分能量通道的电能与信号并行传输系统的串扰抑制方法。通过对所提出的系统拓扑进行分析,给出了电能传输增益表达式与电能串扰增益表达式,并分析了阻波电路参数变化对电能串扰增益的影响;基于交流阻抗法及香农定理分别给出了信号传输增益及信道容量表达式,分析了信号支路参数对信道容量的影响;在综合考虑信号传输增益、信道容量及阻波电路参数敏感性的情况下,给出系统信号支路参数的设计方法。该方法能保证系统在较大功率下降低电能串扰,保障信号稳定传输,实现信号的双向高速传输。通过仿真与实验验证了所提出的拓扑和参数设计方法的有效性。搭建的实验装置实现了传输530.4 W的电能,23.28 Mbit/s信号正向传输速度和24.2428 Mbit/s信号反向传输速度。

A Study on Vehicle Detection and Tracking Using Wireless Sensor Networks

Wireless Sensor network (WSN) is an emerging technology and has great potential to be employed in critical situations. The development of wireless sensor networks was originally motivated by military applications like battlefield surveillance. However, Wireless Sensor Networks are also used in many areas such as Industrial, Civilian, Health, Habitat Monitoring, Environmental, Military, Home and Office application areas. Detection and tracking of targets (eg. animal, vehicle) as it moves through a sensor network has become an increasingly important application for sensor networks. The key advantage of WSN is that the network can be deployed on the fly and can operate unattended, without the need for any pre-existing infrastructure and with little maintenance. The system will estimate and track the target based on the spatial differences of the target signal strength detected by the sensors at different locations. Magnetic and acoustic sensors and the signals captured by these sensors are of present interest in the study. The system is made up of three components for detecting and tracking the moving objects. The first component consists of inexpensive off-the shelf wireless sensor devices, such as MicaZ motes, capable of measuring acoustic and magnetic signals generated by vehicles. The second component is responsible for the data aggregation. The third component of the system is responsible for data fusion algorithms. This paper inspects the sensors available in the market and its strengths and weakness and also some of the vehicle detection and tracking algorithms and their classification. This work focuses the overview of each algorithm for detection and tracking and compares them based on evaluation parameters.

Development of Welding Multi-Information Remote Wireless Monitoring System Based on STM32

A single sensor is used to obtain welding information in welding monitoring process, but this method has some shortcomings. In order to obtain more comprehensive and reliable welding information, this paper designed and built a welding multi-information wireless monitoring system with STM32-F407ZET6 as the control core and ALK8266 as the wireless transmission module. Real-time acquisition, transmission and display of electric arc signal and welding image information are realized in the monitoring system. This paper mainly introduces the hardware and software core of the monitoring system. At the same time, the signal collected by the monitoring system is compared with the original signal, and the accuracy of the remote monitoring system is tested. The monitoring system is used in welding test. The test results show that the accuracy of the monitoring system meets the requirements, and the on-line monitoring of electric arc signal and welding image can be realized in the welding process.

基于谐波通讯的无线电能与反向信号同步传输技术

针对现有无线电能与反向信号同步传输(simultaneous wireless power and reverse signal transmission,SWPRST)系统存在较大无功功率、负载电压易受信号传输发生波动或需要额外增加高频信号源等问题,提出一种基于谐波通讯的SWPRST技术,通过利用逆变器输出方波电压中的基波分量传输电能,三次谐波分量传输信号。不需要外加高频信号发射电路,实现了可靠的电能与反向信号同步传输。首先,给出基于谐波通讯的SWPRST系统结构,对其工作模式和基本原理进行分析;接着,建立系统等效数学模型,分析系统参数取值对信号与电能传输之间的互扰影响;然后,对信号的调制解调电路进行设计,分析信号检测通道参数对信号传输速率的影响;最后,搭建实验平台对理论分析进行验证,实验结果表明,该方法在有效实现了无线电能与反向信号同步传输的同时,信号无误码率传输速率可达5 kbps,同时系统具有无功小,输出负载电压几乎无波动(电压波动率0.33%)等优点。该方法采用谐波作为信号载体,为多频利用式实现电能与反向信号同步传输系统提供一种新的思路,具有较好的理论意义与实际工程应用价值。

无线网络覆盖方案仿真实验设计与实现

以无线信号传播模型为基础,以实现对某区域内无线网络覆盖为目标,设计了覆盖半径随无线参数变化的基础实验、基于三种经典启发式算法的无线网络覆盖方案提高实验和基于改进算法的拓展创新实验,形成了一个完整的实验系统。通过实验教学,学生可以了解到无线通信信号传播和网络覆盖理论知识,能够掌握无线网络覆盖方案设计方法,该实验是一个理论与实践相结合、有层次、有创新的综合设计性实验。

基于空时分组/混沌编码的多天线多基频通信编码研究

为了优化空时分组编码(STBC)和混沌编码,提升无线通信的性能与可靠性,采用多基频混沌编码(MFCC)结合空时分组编码技术搭建了一种双重编码模型,并对该模型的编码解码性能进行仿真分析,评估模型的编码解码性能。实验结果显示,当信噪比为10 dB时,新模型的误码率低至10-8,并且能在两基频条件下实现3548 b/s的高吞吐量。表明该编码解码模型不仅能降低误码率,还能有效提高基频的吞吐量,为促进无线通信技术的发展提供了有力支持。

WiFi与ZigBee双向移动跨网通信方法研究

物联网设备的爆发式增长推进了异构无线设备互联互通的进程,跨网通信技术(Cross-Technology Communication,CTC)允许同一频段下遵循不同底层协议的无线设备在无需网关的前提下实现直联,但移动状态下的双向跨网通信方法仍缺乏系统的研究.本文提出了一种基于能量感知的跨网通信方案——MobiCTC,它支持WiFi与Zig‑Bee设备移动状态下的双向跨网通信.WiFi到ZigBee方向,该方案利用RSSI(Received Signal Strength Indicator)作为解码信息,基于能级映射实现信息解码;ZigBee到WiFi方向,该方案采用CSI(Channel State Information)作为解码信息,充分挖掘CSI的幅度与相位信息,利用机器学习方法实现分类解码.最后,本文使用TelosB节点和USRP X310平台对MobiCTC方案进行了实验验证.实验结果表明,移动状态下WiFi到ZigBee方向的系统吞吐量为139.535 bps,较WiZig提高了1.82倍,符号错误率为0.016,与WiZig基本持平;ZigBee到WiFi方向的系统吞吐量为250 bps,较FreeBee提高了15.7%,符号错误率为0.0516,较ZigFi下降了23.21%.

Sensing, Signal Processing, and Communication for WBANs

A wireless body area network （WBAN） enables real-time monitoring of physiological signals and helps with the early detection of life-threatening diseases. WBAN nodes can be located on, inside, or in close proximity to the body in order to detect vital signals. Measurements from sensors are processed and transmitted over wireless channels. Issues in sensing, signal processing, and com-munication have to be addressed before WBAN can be implemented. In this paper, we survey recent advances in research on sig-nal processing for the sensor measurements, and we describe aspects of communication based on IEEE 802.15.6. We also discuss state-of-the-art WBAN channel modeling in all the frequencies specified by IEEE 802.15.6 as well as the need for new channel models for new different frequencies.

Radio Coverage Mapping in Wireless Sensor Networks

Radio coverage directly affects the network connectivity, which is the foundational issue to ensure the normal operation of the network. Many efforts have been made to estimate the radio coverage of sensor nodes. The existing approaches (often RSSI measurement-based), however, suffer from heavy measurement cost and are not well suitable for the large-scale densely deployed WSNs. NRC-Map, a novel algorithm is put forward for sensor nodes radio coverage mapping. The algorithm is based on the RSSI values collected by the neighbor nodes. According to the spatial relationship, neighbor nodes are mapping to several overlapped sectors. By use of the least squares fitting method, a log-distance path loss model is established for each sector. Then, the max radius of each sector is computed according to the path loss model and the given signal attenuation threshold. Finally, all the sectors are overlapped to estimate the node radio coverage. Experimental results show that the method is simple and effectively improve the prediction accuracy of the sensor node radio coverage.

基于5G网络的RedCap部署方案研究

RedCap作为轻量化的5G技术,是5G Rel-17版本定义的新终端类型,可有效兼顾行业对于技术性能和部署成本的并存需求。一般认为,这种轻量级5G技术的核心价值在于与5G NR网络优异特性的融合,即如何基于运营商已部署的5G无线网引入RedCap是其中的关键问题。结合运营商无线网现状,通过分析RedCap与无线网络侧相关的功能特性,提出引入RedCap后的无线网组网部署方案,包括无线网升级方案、参数配置方案以及信令开销策略等,可为国内各运营商在方案制定及工程实施上提供参考。