An endogenous intelligent architecture for wireless communication networks

Future wireless communication networks tend to be intelligentized to accomplish the missions that cannot be preprogrammed. In the new intelligent communication systems, optimizing the network performance has become a challenge due to the ever-increasing complexity of the network environment. New theories and technologies for intelligent wireless communications have obtained widespread attention, among which deep reinforcement learning (DRL) is an excellent machine learning technology. DRL has great potential in enhancing the intelligence of wireless communication systems while overcoming the above challenge. This paper presents a review on applications of DRL in intelligent wireless communications with focuses on millimeter wave (mmWave), intelligent caching and unmanned aerial vehicle (UAV) scenarios. We first introduce the concepts and basic principles of single/multi-agent DRL techniques. Then, we review the related works where DRL algorithms are used to address emerging issues in wireless communications. These issues include mmWave communication, intelligent caching, UAV aided communication, and handover/access control in HetNets. Finally, critical challenges and future research directions of applying DRL in intelligent wireless communications are outlined.

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.

While modeling and analysis of network topology has been an active area of research in fixed networks, much less work has been done towards realistic modeling of wireless networks. The graph- based approach that has served as solid foundation for network science in the fixed domain is not natural for wireless communication networks, since their performance inherently depends on the spatial relationships between nodes. In this paper we apply techniques from spatial statistics literature to develop models of the spatial structure of the network for a variety of wireless network types. In particular, we construct models of television and radio transmitter distributions that have applications in, for example, cognitive wireless network applications. We use a stochastic approach based on fitting parametric location models to empirical data. Our results indicate that the so-called Geyer saturation model can accurately reproduce the spatial structure of a large variety of wireless network types, arising from both planned or chaotic deployments. The resulting models can be used in simulations or as basis of analytical calculations of different network properties, and we believe that the presented methodology can serve as a solid foundation for the emerging network science of wireless communication networks.

Intelligent vehicle platoon driving is a process of the flexible formation based on the coordination between vehicle and infrastructure in the intelligent vehicle-infrastructure system, which can increase the density of road traffic, simplify the complexity of traffic control, increase the controllability of traffic and ease the traffic jams. It has been constructed that the intelligent vehicle platoon drive simulation experiment system using the technology of one-tenth of intelligent vehicle and simulated road and wireless communication networks and so on. It also has been studied that the autonomous driving technology of intelligent vehicle and wireless communication between intelligent vehicles as well as the information interaction with the main console by the wireless communication module, providing technical and experimental basis for further research on vehicle platoon driving control under the coordination between vehicle and infrastructure.

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.

Intelligent question answering based on knowledge graphs has been a research hotspot in recent years. The realization of intelligent question answering based on knowledge graphs is introduced from four aspects: template-based, semantic analysis, deep learning, and knowledge graph embedding. problem. Combined with the current development of artificial intelligence technology, the focus is on intelligent question answering based on deep learning, which will help more researchers to devote themselves to intelligent question answering research, develop question answering systems suitable for different fields according to the needs of different industries, and improve the level of intelligent information service in society.

At present, the world is experiencing a significant transformation from the fifth generation (5G) wireless communication network to the sixth generation (6G) wireless communication network. An important goal is to achieve seamless global coverage of wireless signals. The combination of space network, air network and ground network has been proved to be effective, but the maritime network has not been taken into account. Therefore, how to effectively achieve the global coverage of wireless networks is a challenging topic. This paper first compares the performance of 5G and 6G, and analyzes the possibility of a wireless communication network with global coverage based on 6G technology. After that, we respectively analyze the typical characteristics of space network, air network, terrestrial network and maritime network, and then lead to the key enabling technologies to combine them. Based on the typical characteristics of the above heterogeneous networks and combined with the enabling technologies, we try to propose a 6Gsupported space-air-ground-sea integrated network (SAGSIN) architecture, to achieve seamless global wireless signal coverage. In the end, we rationally analyze a series of challenges and difficulties that may be faced in implementing the architecture.

This paper presents an ATM based transport architecture for next-generation multiservices personal communication networks (PCN). Such multimedia integrated services wireless networks are motivated by an anticipated demand for wireless extensions to future broadband networks. An ATM based wireless network concept capable of supporting a mix of broadband ISDN services including constant bit-rate (CBR), variable, bit-rate (VBR) and packet data transport is explored from an architectural viewpoint. The proposed system uses a hierarchical ATM switching network for interconnection of PCN microcells, each of which is serviced by high-speed, shared-access radio links based on ATM-compatible cell relay principles. Design issues related to the physical, media access control (MAC) and data link layers of the ATM-based radio link are discussed, and preliminary technical approaches are identified in each case. >

One of the most important types that used to transfer data between nodes without using wires is a “wireless communication network”, where the transmission of data is censored remotely by using electromagnetic waves such as radio waves that usually implemented in the physical layer of the network. Continuous improvements in wireless network technology have reduced the security and speed differences between types of networks (wired and wireless), but in turn, increased security threats to wirelessly transmitted data. Wireless networks are weak in terms of "privacy protection" because anyone who is within the coverage area of a wireless network can attempt to break into that network. Hacking incidents have been reported frequently in places with shared free networks, and it has been observed that the places of open distributed networks of the Internet are most at risk of complete penetration of your phone or PC data. To solve this problem, several programs have been developed that provide protection for wireless networks that differ in terms of security. Some of them did not provide sufficient protection for wireless networks, such as Wired Equivalent Privacy (WEP), and others made progress in preventing intrusions compared to their predecessors, such as Wi-Fi Protected Access (WPA).

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.

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.

With the rapid development of information technology, people's acquisition of tourism and other information is increasingly dependent on images and other information. Aiming at the low efficiency of traditional image retrieval methods in processing massive image data, an image retrieval method based on wireless communication network is proposed. Based on the salient area and wireless communication network combined with the hash method to extract local CNN features, simulate INRIA Holidays Data set and Oxford Buildings Data set, and calculate the accuracy and recall rate of search results based on the returned results of tourist attractions pictures. This article designs an experiment to verify the accuracy and recall rate of search results. By comparing the feature hash function to generate the hash code and the Hamming distance between each hash code in the image library, the image is queried, and the final search result is obtained: The more searches, the lower the accuracy and recall rate. This also proves to a certain extent that the CNN feature extraction technology can be used for travel image search, improving the search accuracy by 20%.The wireless communication network is still of great significance to the future social development. It is necessary to conduct in-depth research, not only the image retrieval of tourist attractions proposed in this article but also the potential value of wireless communication networks from multiple angles and more comprehensively.

With the advent of new wireless technologies, it is expected that the use of Machine-Type Communication (MTC) will significantly increase in next generation wireless networks. Wireless communication networks are considered to support MTC due to their availability and existing infrastructures. As these networks are designed and optimized in a way that they fit best for Human Type Communication (HTC), there is a need of an efficient radio re- source management (RRM) to accommodate MTC traffic without affecting the regular HTC traffic in the network. In this thesis, a continuous-time Markov chain (CTMC) model-based RRM scheme is proposed to analyze the impact of MTC traffic on HTC traffic in wireless communication networks, in terms of blocking probability and channel utilization. Numerical results are provided, demonstrating the effectiveness of the proposed RRM scheme in providing the quality of service (QoS) isolation between HTC and MTC traffic.

With the popularization of the computer network and the development of artificial intelligence technology, the traditional education industry has been reformed. In the past two years, online education has developed rapidly. The combination of the Internet and education enables students to study online at any time, no longer relying on the time and place requirements in traditional education. However, with the rapid development of online education, many problems have gradually emerged. In online education, with a large amount of knowledge and question banks, students are faced with a large number of choices. Therefore, positioning and tracking the knowledge level of students and realizing personalized online education have become the main problems facing the moment. Based on this, this study integrates deep learning and knowledge tracking technology to build a traditional cultural network online education model, aiming at accurately positioning students’ knowledge levels and recommending personalized question banks. The experimental results show that the average AUC of the model proposed in this study is 0.781, and the average accuracy rate is 0.886, which is significantly better than other online education models. Through the combination of deep learning and knowledge tracking technology, the research successfully provides a new and efficient model for personalized learning in the field of online education, which is of great guiding value for promoting further innovation and development of online education. In addition, the research also provides practical solution strategies for related fields, which have obvious practical significance and popularization value.

The rapid development of artificial intelligence technology and deep learning technology has also promoted the popularization and application of image style transfer in all walks of life. In the traditional ceramic industry, the application of image style transfer technology is still rare. This article is based on the style transfer training of ceramic blue and white images and uses three methods of rapid style transfer, direct style transfer and image pre-processing style transfer to conduct experiments. According to the results of image quality evaluation, it is concluded that the method suitable for the style transfer of ceramic blue and white images is the image pre-processing style transfer method.