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Cloud-Radio Access Network (C-RAN) is a promising network architecture to reduce energy consumption and the increasing number of base station deployment costs in mobile networks. However, the necessity of enormous fronthaul bandwidth between a remote radio head and a baseband unit (BBU) calls for novel solutions. One of the solutions introduces the edge-cloud layer in addition to the centralized cloud (CC) to keep resources closer to the radio units (RUs). Then, split the BBU functions between the center cloud (CC) and edge clouds (ECs) to reduce the fronthaul bandwidth requirement and to relax the stringent end-to-end delay requirements. This paper expands this architecture by combining it with renewable energy sources in CC and ECs. We explain this novel system and formulate a mixed-integer linear programming (MILP) problem, which aims to reduce the operational expenditure of this system. Due to the NP-Hard property of this problem, we solve the smaller instances by using a MILP Solver and provide the results in this paper. Moreover, we propose a faster online heuristic to find solutions for high user densities. The results show that make splitting decisions by considering renewable energy provides more cost-effective solutions to mobile network operators (MNOs). Lastly, we provide an economic feasibility study for renewable energy sources in a CRAN architecture, which will encourage the MNOs to use these sources in this architecture.

This book suggests an ecosystem approach to broadband policy that could help in the design of strategies, policies, and programs that support network expansion, have the potential to transform economies, improve the quality and range of services, enable application development, and broaden adoption among users. To identify emerging best practices to nurture this ecosystem, this volume analyzes the Republic of Korea and other leading broadband markets. It identifies three building blocks to support the growth of the broadband ecosystem: defining visionary but flexible strategies, using competition to promote market growth, and facilitating demand. An important but often neglected building block is demand facilitation. This includes raising awareness about the benefits of broadband and improving affordability and accessibility for the largest number of users. Successful countries have often focused on creating a suite of useful applications that increase the relevance of broadband to the widest base of users. Programs to mainstream information and communication technology (ICT) use in education, health, or government have been common.

Maintaining a high overall network reliability remains one of the most critical requirements for advanced metering infrastructures (AMIs) in smart grid. Ensuring reliable networks not only determines the robust communications of an AMI, but also guarantees assured information delivery in the access network. To prevent any communication failures, incremental designs based on legacy networks should be carried out in advance to improve the overall redundancy. Current communication architecture of an AMI follows a traditional access network structure with a tree‐based topology, which does not always satisfy high robustness and is prone to network failures. To address the challenge, this study conducts a reliability study of the access network in an AMI. Specifically, this study first examines the basic network topology adopted in an AMI access network and its underlying connectivity issues. Secondly, this study proposes two practical solutions as parts of incremental network design to improve the communication robustness of existing communication architectures. Thirdly, mathematical models are formulated to solve network connectivity problems, for maintaining a high overall network reliability, while minimising the communication deployment cost at the same time. Simulation results are provided from the aspects of minimal path sets and minimal cut sets to demonstrate the redundancy analysis.

Open Air Interface (OAI) alliance recently introduced a new disaggregated Open Radio Access Networks (O-RAN) framework for next generation telecommunications and networks. This disaggregated architecture is open, automated, software defined, virtual, and supports the latest advanced technologies like Artificial Intelligence (AI) Machine Learning (AI/ML). This novel intelligent architecture enables programmers to design and customize automated applications according to the business needs and to improve quality of service in fifth generation (5G) and Beyond 5G (B5G). Its disaggregated and multivendor nature gives the opportunity to new startups and small vendors to participate and provide cheap hardware software solutions to keep the market competitive. This paper presents the disaggregated and programmable O-RAN architecture focused on automation, AI/ML services, and applications with Flexible Radio access network Intelligent Controller (FRIC). We schematically demonstrate the reinforcement learning, external applications (xApps), and automation steps to implement this disaggregated O-RAN architecture. The idea of this research paper is to implement an AI/ML enabled automation system for software defined disaggregated O-RAN, which monitors, manages, and performs AI/ML-related services, including the model deployment, optimization, inference, and training.

With the increasing number of Internet of Things (IoT) devices, a huge amount of latency-sensitive and computation-intensive IoT applications have been injected into the network. Deploying mobile edge computing (MEC) servers in cloud radio access network (C-RAN) is a promising candidate, which brings a number of critical IoT applications to the edge network, to reduce the heavy traffic load and the end-to-end latency. The MEC server’s deployment mechanism is highly related to the user allocation. Therefore, in this paper, we study hierarchical deployment of MEC servers and user allocation problem. We first formulate the problem as a mixed integer nonlinear programming (MINLP) model to minimize the deployment cost and average latency. In terms of the MINLP model, we then propose an enumeration algorithm and approximate algorithm based on the improved entropy weight and TOPSIS methods. Numerical results show that the proposed algorithms can reduce the total cost, and the approximate algorithm has lower total cost comparing the heaviest-location first and the latency-based algorithms.

<p>A comprehensive evaluation of Fi-Wi, enabling readers to design links using power budget calculations, channel estimation, and equalization algorithms</p> <p>This book provides a detailed study of radio over fiber (ROF)-based wireless communication systems, otherwise called fiber wireless (Fi-Wi) systems. It is an emerging hot topic, where the abundant bandwidth of optical fiber is combined directly with the flexibility and mobility of wireless networks to provide broadband connectivity. The book provides substantial material on the ROF part of the complete Fi-Wi system, including new research results on compensation methods.</p> <p>The early chapters provide the fundamental knowledge required for a non-expert engineering professional as well as senior/graduate-level students to learn the topic from scratch. The latter part of the book covers advanced topics useful for researchers and senior students. Therefore, this book provides a comprehensive understanding of the system for readers who will gain enough knowledge to design Fi-Wi links of their own by learning how to develop Fi-Wi channel estimation and equalization algorithms. This concept is completely novel in the current literature and has been patented by the author.</p> <p>In the increasingly demanding telecommunications profession, engineers are expected to have knowledge in both optical and wireless communications and expected design combined/hybrid systems. Hence, the book is written in such a way that both optical and wireless professionals will be able to easily understand and perceive the concepts</p>

With the development of energy harvesting technologies and smart grid, the future trend of radio access networks will present a multi‐source power supply. In this article, joint renewable energy cooperation and resource allocation scheme of the fog radio access networks (F‐RANs) with hybrid power supplies (including both the conventional grid and renewable energy sources) is studied. In this article, our objective is to maximize the average throughput of F‐RAN architecture with hybrid energy sources while satisfying the constraints of signal to noise ratio (SNR), available bandwidth, and energy harvesting. To solve this problem, the dynamic power allocation scheme in the network is studied by using Q‐learning and Deep Q Network respectively. Simulation results show that the proposed two algorithms have low complexity and can improve the average throughput of the whole network compared with other traditional algorithms. With the development of energy harvesting technologies and smart grid, the future trend of radio access networks will present a multi‐source power supply. In this article, joint renewable energy cooperation and resource allocation scheme of the fog radio access networks (F‐RANs) with hybrid power supplies (including both the conventional grid and renewable energy sources) is studied. In this article, our objective is to maximize the average throughput of F‐RAN architecture with hybrid energy sources while satisfying the constraints of signal to noise ratio (SNR), available bandwidth, and energy harvesting. To solve this problem, the dynamic power allocation scheme in the network is studied by using Q‐learning and Deep Q Network respectively. Simulation results show that the proposed two algorithms have low complexity and can improve the average throughput of the whole network compared with other traditional algorithms.

To access the passive optical network, total link loss is a major concern. An upcoming challenge is to minimize upstream and downstream losses to increase the link power budget. Homogeneous multicore fiber offers the possibility to minimize the link losses without significantly adding multiple feeder fibers. This paper demonstrates the first trench-assisted 19-core homogenous multicore fiber, utilizing a single splitter/combiner at each end of multicore fiber (MCF) to eliminate upstream and downstream losses. The various applications can be realized in various segments of optical communication, including terrestrial, submarine, and access networks. Today’s communication access networks are very price-sensitive and very space-sensitive. So using MCF in bidirectional transmission gives an outstanding reduction of total link loss that is almost 18 dB less as compared to standard single-mode fiber in optical access networks with the least amount of satisfactory inter-core crosstalk.

Many Internet of Things (IoT) services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah. However, an authentication and key management (AKM) mechanism for resource constrained IoT devices using IEEE 802.11ah has not been proposed as yet. We therefore propose a new AKM mechanism for an IoT access network, which is based on IEEE 802.11 key management with the IEEE 802.1X authentication mechanism. The proposed AKM mechanism does not require any pre-configured security information between the access network domain and the IoT service domain. It considers the resource constraints of IoT devices, allowing IoT devices to delegate the burden of AKM processes to a powerful agent. The agent has sufficient power to support various authentication methods for the access point, and it performs cryptographic functions for the IoT devices. Performance analysis shows that the proposed mechanism greatly reduces computation costs, network costs, and memory usage of the resource-constrained IoT device as compared to the existing IEEE 802.11 Key Management with the IEEE 802.1X authentication mechanism.