Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system block that can be optimized to improve overall system performance. In this article, we provide a tutorial overview of reconfigurable intelligent surfaces (RIS) for wireless communications. We describe the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays. We discuss the channel models suitable for both implementations and examine the feasibility of obtaining accurate channel estimates. Furthermore, we discuss the aspects that differentiate RIS optimization from precoding for traditional MIMO arrays highlighting both the arising challenges and the potential opportunities associated with this emerging technology. Finally, we present numerical results to illustrate the power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and Networking (TCCN

An Investigation Of Cost-Benefit Dimensions Of 5G Networks For Agricultural Applications

The agricultural industry is facing unprecedented challenges in meeting the growing demand for food while minimizing its impact on the environment. To address these challenges, the industry is embracing technological advancements such as 5G networks to improve efficiency and productivity. However, the benefits of 5G technology must be weighed against the costs of implementing a suitable network. This paper presents cost-benefit dimensions that are needed to assess the economic feasibility of implementing 5G networks for several agricultural applications. The paper describes the costs of deploying and maintaining a 5G network and the benefits of several 5G-specific use cases, including precision agriculture, livestock monitoring, and swarm robotics. Using industry reports and case studies, the model quantifies the benefits of 5G networks, such as enabling new digital agricultural processes, increased productivity, and improved sustainability. It also considers the costs associated with equipment and infrastructure, as well as the challenges of deploying a network in rural areas. The results demonstrate that 5G networks can provide significant benefits to agricultural businesses and provide an overview about the cost factors. Both benefit and cost dimensions are analyzed for the 5G-specific agricultural use cases

Autonomous Model Update Scheme for Deep Learning-Based Network Traffic Classifiers

Network traffic classification is essential in network management and measurement in access networks, e.g., network intrusion detection, network resource allocation, etc. State-of-the-art Deep Learning based classifiers achieve high classification accuracy even when dealing with encrypted data packets. Such classifiers would need to be updated when a new application appears in the network traffic. However, it is challenging to build and label a dataset of the unknown application so that the current network traffic classifier can be updated. In this paper, we propose an autonomous model update scheme to (i) build a dataset of new application packets from active network traffic; and (ii) update the current network traffic classifier. In particular, the core of the proposed scheme is a discriminator includes a statistical filter and a Convolutional Neural Network (CNN) based binary classifier to filter and build a dataset of new application packets from active network traffic. Evaluation is conducted based on an open dataset (ISCX VPN-nonVPN dataset). The results demonstrated that our proposed autonomous classifier update scheme can successfully filter packets of a new application from network traffic and build a new training dataset. Moreover, the packet classifier can be effectively updated through transfer learning. The success of the proposed update scheme can be adopted in the access network for efficient and adaptive network measurement and management.https://ecommons.udayton.edu/stander_posters/2844/thumbnail.jp