Resource Allocation In Next Generation Broadband Wireless Access Networks
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Resource Allocation in Next-Generation Broadband Wireless Access Networks
With the growing popularity of wireless networks in recent years, the need to increase network capacity and efficiency has become more prominent in society. This has led to the development and implementation of heterogeneous networks. Resource Allocation in Next-Generation Broadband Wireless Access Networks is a comprehensive reference source for the latest scholarly research on upcoming 5G technologies for next generation mobile networks, examining the various features, solutions, and challenges associated with such advances. Highlighting relevant coverage across topics such as energy efficiency, user support, and adaptive multimedia services, this book is ideally designed for academics, professionals, graduate students, and professionals interested in novel research for wireless innovations.
Access Networks
With the rapid growth of the Internet as well as the increasing demand for broadband services, access networks have been receiving growing investments in recent years. This has led to a massive network deployment with the goal of eliminating the ba- width bottleneck between end-users and the network core. Today many diverse te- nologies are being used to provide broadband access to end users. The architecture and performance of the access segment (local loop, wired and wireless access n- works, and even home networks) are getting increasing attention for ensuring quality of service of diverse broadband applications. Moreover, most access lines will no longer terminate on a single device, thus leading to the necessity of having a home network designed for applications that transcend simple Internet access sharing among multiple personal computers and enable multimedia support. Therefore, the access network and its home portion have become a hot investment pool from both a fin- cial as well as a research perspective. The aim of the annual International Conference on Access Networks (AccessNets) is to provide a forum that brings together scientists and researchers from academia as well as managers and engineers from the industry and government organizations to meet and exchange ideas and recent work on all aspects of access networks and how they integrate with their in-home counterparts. After Athens in 2006, Ottawa in 2007, and Las Vegas in 2008, this year AccessNets moved to Asia for the first time.
Green Machine Learning Protocols for Future Communication Networks
Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing, memory, communication, and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications, efforts are required toward designing new machine learning protocols and modifying the existing ones, which consume less energy, i.e., green machine learning protocols. In other words, novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols, this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication, federated learning-based models, and protocols for Beyond Fifth Generation networks, approaches for cloud-based communications, and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications.