Digital wireless electromagnetic interference (EMI) data acquisition system

Distributed wireless systems (DWSs) are emerging as the enabler for next-generation wireless applications. There is a consensus that DWS-based applications, such as pervasive computing, sensor networks, wireless information networks, and speech and data communication networks, will form the backbone of the next technological revolution. Simultaneously, with great economic, industrial, consumer, and scientific potential, DWSs pose numerous technical challenges. Among them, two are widely considered as crucial: autonomous localized operation and minimization of energy consumption. We address the fundamental problem of how to maximize the lifetime of the network using only local information, while preserving network connectivity. We start by introducing the care-free sleep (CS) Theorem that provides provably optimal conditions for a node to go into sleep mode while ensuring that global connectivity is not affected. The CS theorem is the basis for an efficient localized algorithm that decides which nodes will go to into sleep mode and for how long. We have also developed mechanisms for collecting neighborhood information and for the coordination of distributed energy minimization protocols. The effectiveness of the approach is demonstrated using a comprehensive study of the performance of the algorithm over a wide range of network parameters. Another important highlight is the first mathematical and Monte Carlo analysis that establishes the importance of considering nodes within a small number of hops in order to preserve energy.

This paper proposed the lightweight QoE driven adaptive invulnerability wireless communication control method, including the wireless communication terminal equipment with invulnerability antenna. According to the quality of wireless network channel quality, the lightweight QoE driven scheme was developed. According to user needs, the quality of the network, survivable ability of wireless communication terminal and wireless communication survivability requirements, we proposed three matching rules by considering the wireless hop number, data size and life cycle. The experiments demonstrated that the proposed scheme can optimize wireless communication terminal equipment and wireless opportunistic communication network construction, guarantee the user experience quality and improve wireless communication network survivability.

When the wireless communication network is interfered, the communication effect will be affected. In order to improve the interference signal processing effect and the identification accuracy of the interference signal, a wireless communication network interference suppression algorithm based on joint estimation is proposed. Using the deep learning method to identify the interference signal, obtain the effective interference signal of wireless communication network, improve the accuracy of interference signal identification, and track and parameter modulation the identified signal; The node model of wireless communication network is established, and the joint estimation method is used to suppress the interference signal for the nodes in the model. The interference suppression of wireless communication network is realized through the state estimation of single tone interference and narrowband interference. The experimental results show that the proposed algorithm has a high accuracy of interference signal recognition, the highest value reaches 98%, and the wireless communication data packet loss rate is low, the highest value is only 0.37, which verifies its interference suppression effect.

In order to improve the effective ability of sports, it is necessary to carry out sports timing training, construct the wireless communication network of sports timing training, and propose a sports timing training method based on wireless communication. A training model of motion timing in wireless communication network based on spatial interval equilibrium regulation and piecewise load distribution is constructed. The wireless communication transmission channel model of motion timing training is constructed, the channel adaptive equilibrium allocation of motion timing training in wireless communication network is carried out by using spatial equilibrium scheduling method, the orthogonal matching signal tracking model of motion timing training is established. Combined with the load block equilibrium allocation method of multiplex motion timing training, the optimal allocation of motion timing training is carried out. the effectiveness of motion timing training information transmission in wireless communication network is characterized by the number of endpoint paths, and the anti-interference ability of communication is improved by combining the interference suppression method of training environment. The simulation results show that the output quality of motion timing training in wireless communication network is high and the bit error rate (BER) is low, which improves the balanced distribution ability of motion timing training load in wireless communication network.

Base station location selection and network optimization are critical to improving the performance of wireless communication networks in terms of latency reduction. To this end, the article proposes leveraging a convolutional neural network (CNN) to improve the accuracy of base station location selection and network latency reduction. The CNN method, based on a three-dimensional representation including signal strength data set, network topology data set, and transmission path data set, is used to select base station location and optimize the multihop relay network for latency reduction. The article presents a following method: location selection and network optimization for the wireless communication network. First, it collects the experimental data set of base station location selection and network optimization, and then uses the training data to train the CNN model to extract features. Once the training is done, the article further optimizes the network parameters and configurations, and ultimately obtains the optimal base station location and network configuration while minimizing network latency. As a result, simulation results indicate that the CNN model has remarkable performance in base station location selection, as well as in network optimization. In summary, the feature extraction and processing ability of CNN are powerful, enabling it to effectively capture factors leading to delay, hence improving the performance of base station location selection and network optimization. The article also demonstrates that the CNN model can be adjusted according to different environments and scenario settings through dynamic tuning.

Wireless personal communication network is easily affected by intrusion data in the communication process, resulting in the inability to ensure the security of personal information in wireless communication. Therefore, this paper proposes a malicious intrusion data mining algorithm based on legitimate big data in wireless personal communication networks. The clustering algorithm is used to iteratively obtain the central point of malicious intrusion data and determine its expected membership. The noise in malicious intrusion data is denoised by objective function, and the membership degree of communication data is calculated. The change factor of the neighborhood center of gravity of malicious intrusion data in wireless personal communication network is determined, the similarity between the characteristics of malicious intrusion data by using the Markov distance was determined, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was completed. The experimental results show that the accuracy of mining malicious data is high and the mining time is short.

Wireless networks have a key role in operational deployment, mobility, the flexibility of network organization and the variety of possible applications, in many cases being the only economically justified solution. Based on the above, for the deployment of wireless networks, the correct theoretical design of the wireless network is an important issue: the required number of devices, their characteristics, placement, energy characteristics of channels, distribution routes and coverage areas, etc. Correct theoretical analysis will ensure error-free construction of the network for the successful implementation of the tasks and will not lead to the need to attract additional material costs. One of the key tasks of wireless communication construction is a reliable assessment of the energy characteristics of a wireless communication channel. Modern global manufacturers of telecommunications equipment build both backbone equipment and equipment to cover WLAN (wireless local area networks) areas, based on IEEE 802.11 standards. One of the most modern standards of the IEEE 802.11 group is the IEEE 802.11ac standard, which is characterized by several improved technical characteristics compared to its predecessor IEEE 802.11n and older versions. That is why the task of evaluating the performance of modern wireless communication equipment based on IEEE 802.11ac standards and analyzing the evaluation of the energy characteristics of the communication channel to predict the parameters and performance characteristics of a point-to-point, point-to-multipoint or other wireless transmission systems within a certain wireless communication network. The following results of solving this problem are presented in this paper: – theoretical evaluation of the energy characteristics of the wireless communication channel. – laboratory studies of wireless communication equipment of the 802.11ac standard based on simulating a communication line using attenuators. – verification of the results of laboratory studies in field conditions based on the model for assessing the quality of communication in a wireless channel created based on the 802.11ac standard.

A time-domain technique for the design of passive power line conducted electromagnetic interference (EMI) filters in the frequency range 150 kHz-30 MHz is described. A digital storage oscilloscope (DSO) with adequate sampling, storing and processing features is sufficient for the design using the proposed technique. Accordingly, Agilent's Infiniium Oscilloscope (Model 54810A) has been used. The signals from LISN are directly fed into the two channels of the DSO where they are added and subtracted to separate the CM and DM components, thereby eliminating the need for common-mode-differential mode (CM-DM) separator. These components are stored in the DSO. A specially designed filter design software (FDS), residing in the DSO, estimates the noise spectrum by computing the Bartlett and Welch periodograms. It also computes the filter component values. Thus, the sampling of the conducted noise, separation of CM and DM components, signal processing, and filter value computations are all done using one DSO. A spectrum analyzer is not required. Bartlett periodograms have been preferred over Welch periodograms due to low memory storage requirements of the former. The proposed technique has been applied to the design of power line filter for a switched mode power supply (SMPS), and satisfactory results have been obtained. The proposed measurement scheme is compact, economical, and convenient. All the details of this work are presented.

The challenges posed by the future wireless communication network, which will be a huge system with more complex structures, diverse functions, and massive communication ends, will be addressed by intelligent wireless communication technologies. These technologies are playing an increasingly important role in network architecture, computing architecture, resource allocation algorithm design, etc., thanks to the rapid development of artificial intelligence technologies, particularly the deep learning technologies, and their extensive application in various domains. In this paper, an endogenous intelligent architecture is developed to effectively clarify and understand in-depth the relationship among the factors by constructing wireless knowledge graph for the air interface transmission, the core network, as well as the network environment, and so on. Furthermore, the knowledge graph simultaneously reveals the structure and operation mechanism of the whole wireless communication networks. Cause tracing, intelligent optimization, and performance evaluation are sequentially implemented based on the knowledge graph, thus forming a complete closed-loop for endogenous intelligent wireless communication networks.

Aiming at the problems of traditional wireless communication network security vulnerability monitoring systems such as low monitoring accuracy and time-consuming, a machine learning-based intelligent monitoring system for wireless communication network security vulnerabilities is proposed. In the hardware design of the monitoring system, based on the overall architecture of the wireless communication network and the data characteristics of the wireless communication network, it is divided into a vulnerability data collection module, a vulnerability data scanning module, and a network security vulnerability intelligent monitoring module. In the vulnerability data collection module, the wireless data collector is used to collect vulnerability data in the vulnerability database, and according to the attributes of the vulnerability data, the XSS vulnerability detection plug-in is connected to the vulnerability scanner to scan for wireless communication network vulnerabilities; When the communication network vulnerability data signal is traced, the system session operation of monitoring the vulnerability data. The software part introduces the neural network algorithm in the machine learning intelligent algorithm to process the hidden data in the security vulnerability data. The experimental results show that the wireless communication network security vulnerability intelligent monitoring system based on machine learning can effectively improve the system monitoring accuracy and the efficiency of wireless communication network security vulnerability monitoring.

In order to meet the extremely heterogeneous requirements of the next generation wireless communication networks, research community is increasingly dependent on using machine‐learning solutions for real‐time decision‐making and radio resource management. Traditional machine learning employs fully centralized architecture in which the entire training data is collected at one node for example, cloud server, that significantly increases the communication overheads and also raises severe privacy concerns. Toward this end, a distributed machine‐learning paradigm termed as federated learning (FL) has been proposed recently. In FL, each participating edge device trains its local model by using its own training data. Then, via the wireless channels the weights or parameters of the locally trained models are sent to the central parameter server (PS), that aggregates them and updates the global model. On one hand, FL plays an important role for optimizing the resources of wireless communication networks, on the other hand, wireless communications is crucial for FL. Thus, a “bidirectional” relationship exists between FL and wireless communications. Although FL is an emerging concept, many publications have already been published in the domain of FL and its applications for next generation wireless networks. Nevertheless, we noticed that none of the works have highlighted the bidirectional relationship between FL and wireless communications. Therefore, the purpose of this survey article is to bridge this gap in literature by providing a timely and comprehensive discussion on the interdependency between FL and wireless communications.

The use of a wide range of radio stations of various generations in the Armed Forces of the RussianFederation has led to the impossibility of fully taking into account all their features when planningradio communications. For example, it becomes an extremely difficult task to take into account the entirelist of self-detected frequencies by the emissions of their own heterodynes of various radio stations,as well as frequencies prohibited for establishing and conducting radio communications. In this regard,the risks of lack of radio communication or violation of the rules for the use of the radio frequency rangehave increased many times, including in the units of military robotic complexes (RTK VN). The articlenotes that one of the biggest problems in creating a unified digital combat control system, the technicalbasis of which will be a wireless communication network - a radio communication network operating invarious frequency ranges, today is meeting the requirements of electromagnetic compatibility (EMC)and, in particular, meeting the requirements for the correct use of the radio frequency spectrum.To optimize the process of radio communication planning in the formations of the RTK VN, to increaseits efficiency and effectiveness, the article proposes to create and apply an automated system for monitoringthe assignment of radio frequencies based on a risk-based approach. It is shown that the riskorientedapproach when planning radio communications in the divisions of the RTK VN involves acomparative assessment of the nominal value of the assigned operating frequency with a list of selfdetectedfrequencies of each of the radio stations used, as well as with a list of prohibited frequencies forestablishing radio communications (frequencies for transmitting distress signals, etc.). It is reasonedthat this approach will reduce the risks of lack of radio communication or violation of the rules for usingthe radio frequency range. It will optimize the processes of automated planning of radio communicationsin the formations of the RTK VN and increase the efficiency and effectiveness of automated planningand management of radio communications during combat operations. The article describes thealgorithm of the automated control system for the assignment of radio frequencies. The software implementationof this algorithm involves the creation and storage in the appropriate memory area of libraries(databases) of the main prohibited frequencies defined by the legislation of the Russian Federation.In addition, these libraries (databases) store lists of self-reflected frequencies by the emissions of theirown heterodynes of all types of radio stations used, given in the technical description of each of theradio stations used.

Wireless communications play an important role in meeting the continuously increasing demands for transmission of important/private information, spanning credit card information, e -health data, control messages, etc. However, on the other hand, due to the broadcast nature of the wireless medium, wireless networks are subject to physical -layer threats such as eavesdropping, spoofing, and jamming attacks. In the fifth -generation (5G) wireless communication networks, information security is a critical issue [1]. Basically, security strategies today rely on cryptographic techniques and associated protocols. Nevertheless, these solutions have drawbacks, for example, standardized protections within public wireless networks are not secure enough, and many of their weaknesses are well known; even if enhanced protocols exist, they incur strong constraints and high additional costs for the users of public networks [2]. Therefore, physical -layer security (PLS), a new security method, which safeguards wireless communications based on the information theoretic approach has received significant research interest recently, especially in the 5G communication techniques.

Wireless networks are becoming ubiquitous and as the cost of equipment decreases and performance increases, it becomes both economically and technologically feasible to deploy wireless networks in power systems and industrial environments for a wide range of applications. They have advantage of providing diverse controlling features through a unified communication platform. Application of such networks in the smart grid/industrial environments is under active research and expected to become an integral part of the power system. This research propose novel technique smart grid communication in wireless 5G networks for monitoring and controlling management. Here the smart grid designing has been done based on wireless communication networks. The smart grid network for renewable energy has been controlled using Stackelberg equilibrium based SCADA (supervisory control and data acquisition) method. The control method based collected data has been monitored for detection of malicious activities in the network using supervised radial basis fuzzy systems. The experimental analysis has been carried out based on control system and network malicious activities. Here the control system based parameters analysed are Scalability of 65%, QoS of 71%, Power consumption of 41%, Network Efficiency of 92%. Then machine learning based malicious activities detection in terms of accuarcy of 96%, network security of 88%, throughput of 94%, Network delay of 41%. Proposed method supports interoperability of multiple types of inverters, is scalable and flexible, and transmits data over a secure communication channel.

This paper shows a development of an RFID (Radio Frequency IDentification) user authorization (access control system) using wireless ZigBee communication network. The system is designed with the topology of one Master and multiple Slave nodes. Idea is to manage a data base on the Master node which is connected to the Slave nodes through a wireless link. Slave node consists of an RFID reader, LCD touch screen and XBee wireless module. Master node communicates with the LabView application through an Ethernet interface. A program development is shown for both nodes, including all the peripheral modules. The proposed system's major advantage over the existing systems is wireless communications. Wireless network uses XBee wireless modules and can be formed as a mesh network, where an end node server as a router, thus extending the range of the network.