About: Next-generation network is a research topic. Over the lifetime, 5712 publications have been published within this topic receiving 74686 citations. The topic is also known as: NGN.
Papers published on a yearly basis
Royal Institute of Technology1, University of Padua2, Bell Labs3, Ludwig Maximilian University of Munich4, Dresden University of Technology5, Chalmers University of Technology6, Technische Universität München7, RWTH Aachen University8, Kyoto University9, University of California, San Diego10, Helsinki University of Technology11
TL;DR: This article describes the scenarios identified for the purpose of driving the 5G research direction and gives initial directions for the technology components that will allow the fulfillment of the requirements of the identified 5G scenarios.
Abstract: METIS is the EU flagship 5G project with the objective of laying the foundation for 5G systems and building consensus prior to standardization. The METIS overall approach toward 5G builds on the evolution of existing technologies complemented by new radio concepts that are designed to meet the new and challenging requirements of use cases today?s radio access networks cannot support. The integration of these new radio concepts, such as massive MIMO, ultra dense networks, moving networks, and device-to-device, ultra reliable, and massive machine communications, will allow 5G to support the expected increase in mobile data volume while broadening the range of application domains that mobile communications can support beyond 2020. In this article, we describe the scenarios identified for the purpose of driving the 5G research direction. Furthermore, we give initial directions for the technology components (e.g., link level components, multinode/multiantenna, multi-RAT, and multi-layer networks and spectrum handling) that will allow the fulfillment of the requirements of the identified 5G scenarios.
TL;DR: It is argued that controlled link-sharing is an essential component that can provide gateways with the flexibility to accommodate emerging applications and network protocols.
Abstract: Discusses the use of link-sharing mechanisms in packet networks and presents algorithms for hierarchical link-sharing. Hierarchical link-sharing allows multiple agencies, protocol families, or traffic types to share the bandwidth on a link in a controlled fashion. Link-sharing and real-time services both require resource management mechanisms at the gateway. Rather than requiring a gateway to implement separate mechanisms for link-sharing and real-time services, the approach in the paper is to view link-sharing and real-time service requirements as simultaneous, and in some respect complementary, constraints at a gateway that can be implemented with a unified set of mechanisms. While it is not possible to completely predict the requirements that might evolve in the Internet over the next decade, the authors argue that controlled link-sharing is an essential component that can provide gateways with the flexibility to accommodate emerging applications and network protocols. >
TL;DR: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection.
Abstract: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.
TL;DR: A novel edge caching scheme based on the concept of content-centric networking or information-centric networks is proposed and evaluated, using trace-driven simulations to evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks.
Abstract: The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges.
TL;DR: An overview of major challenges in two-tier networks is provided and some pricing schemes for different types of device relaying are proposed.
Abstract: In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users? devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.
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