Microstrip Patch Antenna Parameter Optimization Prediction Model using Machine Learning Techniques

Microstrip patch antenna (MPA) plays key role in the wireless communication. The research is continuing going to design and optimization of the antenna for various advance application such as 5G and IOT. Artificial intelligence based techniques such as machine learning is also capable to optimize the parameter values and make prediction model based on the given dataset. This research paper shows the machine learning based techniques to optimize the microstrip patch antenna parameters with the performance improvement in terms of accuracy, Mean Squared Error, and Mean Absolute Error. The antenna optimization process may be greatly accelerated using this data-driven simulation technique. Additionally, the advantages of evolutionary learning and dimensionality reduction methods in antenna performance analysis are discussed. To analyze the antenna bandwidth and improve the performance parameters is the main concern of this work.

Hybridization strategy for microstrip antenna optimization

In the exploding growth of radio mobile and wireless communication applications, microstrip antennas with its advantages of low cost and flexible fabrications, emerge as the most suitable candidate. The direct antenna synthesis could, however do not result in the optimal antenna configuration, and therefore a possible alternative is considering the problem of optimizing the antenna as a system of uncertainty, in which each set of geometrical parameters returns a totally different response; the best set, i.e. the one that gives the best antenna performances, can be obtained using global optimizers, as evolutionary algorithms. The main drawback of this approach is that it is really time and memory consuming. In this article, a technique based on the hybridization between Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN)is introduced with the aim of reducing this nimerical cost and implemented to optimize a dual-annular ring proximity coupled feed antenna

Hybridization strategy for microstrip antenna optimization

In the exploding growth of radio mobile and wireless communication applications, microstrip antennas with its advantages of low cost and flexible fabrications, emerge as the most suitable candidate. The direct antenna synthesis could, however do not result in the optimal antenna configuration, and therefore a possible alternative is considering the problem of optimizing the antenna as a system of uncertainty, in which each set of geometrical parameters returns a totally different response; the best set, i.e. the one that gives the best antenna performances, can be obtained using global optimizers, as evolutionary algorithms. The main drawback of this approach is that it is really time and memory consuming. In this article, a technique based on the hybridization between Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN)is introduced with the aim of reducing this nimerical cost and implemented to optimize a dual-annular ring proximity coupled feed antenna

Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO

Cell-free massive MIMO is emerging as a promising technology for future wireless communication systems, which is expected to offer uniform coverage and high spectral efficiency compared to classical cellular systems. We study in this paper how cell-free massive MIMO can support federated edge learning. Taking advantage of the additive nature of the wireless multiple access channel, over-the-air computation is exploited, where the clients send their local updates simultaneously over the same communication resource. This approach, known as over-the-air federated learning (OTA-FL), is proven to alleviate the communication overhead of federated learning over wireless networks. Considering channel correlation and only imperfect channel state information available at the central server, we propose a practical implementation of OTA-FL over cell-free massive MIMO. The convergence of the proposed implementation is studied analytically and experimentally, confirming the benefits of cell-free massive MIMO for OTA-FL

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

Reconfigurability in Wireless Networks: Applications of Machine Learning for User Localization and Intelligent Environment

With the rapid development of machine learning (ML) and deep learning (DL) methodologies, the theoretical foundation of leveraging DL in wireless network reconfigurability and channel modeling is studied and summarized. While deep learning based methods have been applied in a few wireless network use cases, there is still much to be explored in many wireless channel modeling scenarios such as predicting channel state information (CSI), and configuring intelligent surface for optimum performance. In this paper, we perform an extensive research on the application of deep learning methods in reconfigurable wireless modeling problems which contains two scenarios. In the first scenario, a user transmitter was moving randomly within a campus area, and at certain spots sending wireless signals that were received by multiple antennas. We constructed an active deep learning architecture to predict user locations from received signals after dimensionality reduction, and analyzed 4 traditional query strategies for active learning to improve the efficiency of utilizing labeled data. We proposed a new location based query strategy that considers both spatial density and model uncertainty when selecting samples to label. We show that the proposed query strategy outperforms all the existing strategies. In the second scenario, a reconfigurable intelligent surface (RIS) containing 4096 tunable cells reflects signals sending from a transmitter to users in an office for better performance. We use the training data of one user's received signals under different configurations to learn the impact behavior of the RIS on the wireless channel. Based on the context and experience from the first scenario, we built a DL neural network that maps RIS configurations to received signal estimations. In a second phase back propagation, the loss function was customized towards our final evaluation formula in order to obtain the optimum configuration array for a user. A further research on the identification of line-of-sight (LOS) and none line-of-sight (NLOS) users has been conducted, which enabled us to prioritize NLOS users over LOS users in our loss function to maximize the final evaluation goal. We built a customized DL pipeline that automatically learns the behavior of RIS on received signals, and generates the optimal RIS configuration array for each of the 50 test users

Narrowband IoT: from the end device to the cloud. An experimental end-to-end study

This thesis is about a novel study and experimentation of a Cloud IoT application, communicating over a NB-IoT Italian network. So far there no been presented studies, which are about the interactions between the NB-IoT network and the cloud. This thesis not only fill this gap but also shows the use of Cognitive Services to interact, through the human voice, with the IoT application. Compared with other types of mobile networks, NB-IoT is the best choice