Future Trends in Fiber Optics Communication

Fiber optic systems are important telecommunication infrastructure for world-wide broadband networks. Wide bandwidth signal transmission with low delay is a key requirement in present day applications. Optical fibers provide enormous and unsurpassed transmission bandwidth with negligible latency, and are now the transmission medium of choice for long distance and high data rate transmission in telecommunication networks. This paper gives an overview of fiber optic communication systems including their key technologies, and also discusses their technological trend towards the next generation

5G Mobile Communications

This book provides a comprehensive overview of the emerging technologies for next-generation 5G mobile communications, with insights into the long-term future of 5G. Written by international leading experts on the subject, this contributed volume covers a wide range of technologies, research results, and networking methods. Key enabling technologies for 5G systems include, but are not limited to, millimeter-wave communications, massive MIMO technology and non-orthogonal multiple access. 5G will herald an even greater rise in the prominence of mobile access based upon both human-centric and machine-centric networks. Compared with existing 4G communications systems, unprecedented numbers of smart and heterogeneous wireless devices will be accessing future 5G mobile systems. As a result, a new paradigm shift is required to deal with challenges on explosively growing requirements in mobile data traffic volume (1000x), number of connected devices (10–100x), typical end-user data rate (10–100x), and device/network lifetime (10x). Achieving these ambitious goals calls for revolutionary candidate technologies in future 5G mobile systems. Designed for researchers and professionals involved with networks and communication systems, 5G Mobile Communications is a straightforward, easy-to-read analysis of the possibilities of 5G systems

Towards a framework for network management applications based on peer-to-peer paradigms

The Madeira project addresses a novel approach for the management of network elements of increasing number, heterogeneity and transience. As next-generation networks exhibit major challenges for today's centralized network management systems, we investigate the fesability of a peer-to-peer (P2P) approach, facilitating self-management and dynamic behaviour of elements within networks. In this short paper we give an overview of the system architecture developed in Madeira and descirbe the key conceptts, like Madeira platform services, Adaptive Management Components and poicies that provide the base for building distributed network management applications. Keywords - P2P technologies, self-managing networks, policy based management, Model Driven Architecture (MDA

Machine Learning Techniques for 5G and beyond

Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks. © 2013 IEEE

The role of the RPL routing protocol for smart grid communications

Advanced communication/networking technologies should be integrated in next-generation power systems (a.k.a. smart grids) to improve their resilience, efficiency, adaptability, and sustainability. Many believe that the smart grid communication infrastructure will emerge from the interconnection of a large number of small-scale networks organized into a hierarchical architecture covering larger geographic areas. In this article, first we carry out a thorough analysis of the key components of the smart grid communication architecture, discussing the different network topologies and communication technologies that could be employed. Special emphasis is given to the advanced metering infrastructure, which will be used to interconnect the smart meters deployed at customers\u27 premises with data aggregators and control centers. The design of scalable, reliable, and efficient networking solutions for AMI systems is an important research problem because these networks are composed of thousands of resource-constrained embedded devices usually interconnected with communication technologies that can provide only low-bandwidth and unreliable links. The IPv6 Routing Protocol for Low Power and Lossy Networks was recently standardized by the IETF to specifically meet the requirements of typical AMI applications. In this article we present a thorough overview of the protocol, and we critically analyze its advantages and potential limits in AMI applications. We also conduct a performance evaluation of RPL using a Contiki-based prototype of the RPL standard and a network emulator. Our results indicate that although average performance may appear reasonable for AMI networks, a few RPL nodes may suffer from severe unreliability issues and experience high packet loss rates due to the selection of suboptimal paths with highly unreliable links

Channel Modeling and Characteristics for 6G Wireless Communications

[EN] Channel models are vital for theoretical analysis, performance evaluation, and system deployment of the communication systems between the transmitter and receivers. For sixth-generation (6G) wireless networks, channel modeling and characteristics analysis should combine different technologies and disciplines, such as high-mobil-ity, multiple mobilities, the uncertainty of motion trajectory, and the non-stationary nature of time/frequency/space domains. In this article, we begin with an overview of the salient characteristics in the modeling of 6G wireless channels. Then, we discuss the advancement of channel modeling and characteristics analysis for next-generation communication systems. Finally, we outline the research challenges of channel models and characteristics in 6G wireless communications.This research was supported by the National Key R&D Program of China under grant 2018YFB1801101; the National Nature Science Foundation of China (No. 61771248 and 61971167); the Jiangsu Province Research Scheme of Nature Science for Higher Education Institution (No. 14KJA510001); and the Open Research Fund of the National Mobile Communications Research Laboratory, Southeast University (No. 2020D14).Jiang, H.; Mukherjee, M.; Zhou, J.; Lloret, J. (2021). Channel Modeling and Characteristics for 6G Wireless Communications. IEEE Network. 35(1):296-303. https://doi.org/10.1109/MNET.011.200034829630335

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies

6G for vehicle-to-everything (V2X) communications: Enabling technologies, challenges, and opportunities

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies