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Media Casting as a Service: Industries Convergence Opportunity and Caching Service for 5G Indoor gNB
Abstract
Fifth-Generation (5G) mobile networks are expected to perform according to the stringent performance targets assigned by standardization committees. Therefore, significant changes are proposed to the network infrastructure to achieve the expected performance levels. Network Function Virtualization, cloud computing and Software Defined Networks are some of the main technologies being utilised to ensure network design, with optimum performance and efficient resource utilization. The aforementioned technologies are shifting the network architecture into service-based rather device-based architecture. In this regard, we introduce Media Casting as a service (MCaaS) and IoRL-Cache service (IoRL-C). The former is a service that proposed as an integration solution between the broadcasting industry and Mobile Network Operators, while the latter proposed as a solution for improving IoRL small-cells caching efficiency. IoRL is an emerging 5G small-cell for indoor environment, which utilises mmWave and Visible Light Communications (VLC) as access technologies, while exploiting Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies to offer flexible and intelligent services to its clients. In this paper, we introduce MCaaS and IoRL-C services, we perform simulation work for testing IoRL-C as a proof of concept for both services. The network was simulated using OMNeT++ simulation tool, and we validated the services efficiency by comparing their performance with traditional deployments. We have also examined the cache service performance at different link lengths, and found out that IoRL-C is able to support IoRL small-cells with more than 50+Km separation distances.
... For outlier detection in the industrial internet of things (IIoT) system, GNB is used as mentioned in [28]. Also, in [29], [30], GNB is implemented in such detection problems. ...
Indoor network optimization is not a simple task due to the obstacles, interference, and attenuation of the signal in an environment. Intense noises can affect the intelligibility of the signal and reduce the coverage strength significantly which results in a poor user experience. Most of the existing works are associated with finding the location of the devices via different mathematical and generic algorithmic approaches, but very few are focused on implying machine learning algorithms. The purpose of this research is to introduce an integrated machine learning model to find maximum indoor coverage with a minimum number of transmitters. The users in the indoor environment also have been allocated based on the most reliable signal strength and the system is also capable of allocating new users. K-means clustering, K-nearest neighbor (KNN), support vector machine (SVM), and Gaussian Naïve Bayes (GNB) have been used to provide an optimized solution. It is found that KNN, SVM, and GNB obtained maximum accuracy of 100% in some cases. However, among all the algorithms, KNN performed the best and provided an average accuracy of 93.33%. K-fold cross-validation (Kf-CV) technique has been added to validate the experimental simulations and re-evaluate the outcomes of the machine learning models.
... For outlier detection in the industrial internet of things (IIoT) system, GNB is used as mentioned in [28]. Also, in [29], [30], GNB is implemented in such detection problems. ...
Indoor network optimization is not a simple task due to the obstacles, interference, and attenuation of the signal in an environment. Intense noises can affect the intelligibility of the signal and reduce the coverage strength significantly which results in a poor user experience. Despite having numerous shortcomings and vulnerabilities in the network, still many strategies have been implemented over the years to unravel the indoor coverage problem and come up with a robust solution. Most of the existing works are associated with finding the location of the devices via different mathematical and generic algorithmic approaches, but very few are focused on implying machine learning algorithms. The core purpose of this research is to introduce an integrated machine learning model to find maximum indoor coverage with a minimum number of transmitters. The users in the indoor environment also have been allocated based on the most reliable signal strength and the system is also capable of allocating new users. K-means clustering, K-nearest neighbor (KNN), Support Vector Machine (SVM), and Gaussian Naïve Bayes (GNB) have been used to provide an optimized solution. It is found that KNN, SVM, and GNB obtained maximum accuracy of 100% in some cases. However, among all the algorithms, KNN performed the best and provided an average accuracy of 93.33%. K-fold cross-validation (Kf-CV) technique has been added to validate the experimental simulations and re-evaluate the outcomes of the machine learning models.
This paper encompasses various topics for 5G-involved broadcasting under the common context converged distribution of 5G media. Particularly, two primary subtopics are addressed: An overlaid coexistence of 5G terrestrial broadcast and Advanced Television Systems Committee (ATSC) 3.0; and dual connectivity applications built on top of the convergence between digital broadcasting and 5G unicasting. In the first part, a viable, time-sharing co-transmission system is introduced to overlay 5G Broadcast and ATSC 3.0 frames in the same radio spectrum. The system is carefully designed to comply with current 5G and ATSC 3.0 standards, so that it does not require modifications in either ATSC 3.0 or 5G specifications. To help the future planning in a technical aspect, the reliability performance is in-depth compared between 5G Broadcast and ATSC 3.0 frames, and the motivation of tower overlaying is discussed as well. Subsequently in the 2
$^{nd}$
part, the convergence is in parallel explored with respect to the dual connectivity. To settle a broad-ranged consensus for future convergence, various possible use cases are proposed in an application aspect. Technical, conceptual proposals and implementations are presented, specifically related to edge caching; 8K streaming (with a working prototype); large-scale software updating; data security; and location tracking. We hereby point out how dual connectivity can contribute to realistic scenarios. In turn, the scope of convergence use cases is desirably expanded, so that would encourage the initiatives bridging 5G and broadcasting into practice.
Maritime communications, including inland waterways and sea communications, are the key technologies for ensuring communication and navigational safety during maritime activities. New mobile communication technologies, such as 5G and 6G, focus on the full coverage of land, sea, and space. However, current maritime communications primarily depend on satellite communication systems that have high costs, limited coverage, and low data rate. Thus, research on maritime communications based on broadband technology has garnered attention in recent years, and it requires a deeper understanding of the characteristics of wireless channels. In this paper, we outline the difference between the terrestrial and maritime propagation properties and present a survey of maritime communications in terms of the wireless channel measurements and modeling. Furthermore, the research challenges and directions that may be encountered in future maritime communications are discussed.
5G and edge computing have brought great changes to video industry. Interactive video is becoming an emerging application form of multimedia service, which provides attraction beyond typical scenarios like cloud gaming and remote virtual reality (VR), and puts forward great challenges in resource capacity, response latency, and function flexibility to its service system. In this paper, we propose an elastic system architecture with low latency features to accommodate generic interactive video applications on near user edges. To increase system flexibility, we firstly design a dynamic Directed Acyclic Graph (dDAG) model for efficient task representation. Secondly, based on the model, we present the elastic architecture together with its scalable workflow pipeline. Thirdly, we propose a set of novel latency measurement metrics to analyze and optimize the performance of an interactive video system. Based on the proposed approaches, we disassemble a real world free-viewpoint synthesis application and benchmark its performance with the metrics. Extensive experimental results show the flexibility of our system to handle the stochastic human interactions during a video service session, with less than 5 ms additional scheduling latency introduced. End to end latency is kept within 43 ms for complex functions, and 28 ms for simpler scenarios, which satisfies the restrictions of most interactive video applications provided by an edge. Client of the architecture serves as a pure video player, which is also friendly to power limited terminals such as 5G phones. Efficiency and stability analyses of the system show superiorities over existing work, and also reveal potential optimization directions for future research.
The Fifth generation of mobile broadband (BB) networks, popularly known as 5G, aims to revolutionize different vertical industries, including media broadcasting. Although it remains to be seen the real impact and timescale of the 5G revolution, 5G may represent an opportunity rather than a threat for broadcast (BC), especially when considering new approaches for BC and BB evolving into new converged platforms inter-connecting terrestrial BC and cellular/wired BB systems together [item 1) in the Appendix]. Hybrid BC-BB platforms are already a success in several countries, based, e.g., on HbbTV in EU, hybridcast in Japan and Ginga in Brazil. But the increasing importance of fixed and mobile BB IP delivery for television services, well-illustrated by the new IP-based ATSC 3.0 standard [item 2) in the Appendix] and the on-going DVB-I (Internet) specification, and the fact that the 5G system will support and be interoperable with non-3GPP access technologies, potentially allowing multiple access technologies to be used simultaneously for one or more services, opens the door to new convergence scenarios.
Edge caching enables mobile users to fetch contents from cache servers deployed at base stations or even closer to users, with the aim of avoiding latency and improving the overall user experience while reducing backhaul/fronthaul bandwidth use. While sophisticated edge caching policies have been extensively studied in recent works, the practical implementation issues of edge caching are rarely touched. In this article, we first provide an overview of the edge caching policies proposed in the existing literature. Then we present the architecture for edge caching, in which the mobile edge applications can be conveniently provided by chaining the service functions with the aid of edge computing techniques and virtualized resources. Next, we discuss the challenges in implementing the existing caching policies based on the proposed framework. Finally, we build a testbed to evaluate our architecture in a proof of concept and test it with typical caching scenarios. Our experiments show that the proposed framework allows guaranteeing the requirements of the edge caching services.
The explosive growth of video data traffic brings a great pressure on the future 5G networks. Deploying caches at the radio access network (RAN) and evolved packet core (EPC) in the 5G network can effectively relieve this pressure by reducing duplicate content retransmission. In this paper, we propose a new collaborative cache scheme for the future 5G wireless network. The proposed scheme aims to improve the data availability and access rate locally easing collaborative local resources of the 5G wireless hotspot network. We investigate the impact of different constraints on the collaborative cache scheme, including limited storage capacity, placement of content, content popularity, and distribution of content. By considering these constraints, we effectively improve the overall performance of the 5G wireless network in terms of transmission delay, local availability of contents and efficient utilization of caches within the network hotspot. Our proposed scheme provides an efficient utilization of the hotspot cache storage by using Zipf distribution and knapsack, and it is also able to entertain the user request partially available in the hotspot. Simulation results reveal that compared with conventional collaborative cache schemes, the proposed scheme for the 5G wireless network can significantly reduce transmission delay and increase hitratio within the hotspot.
With files proactively stored at base stations (BSs), mobile edge caching enables direct content delivery without remote file fetching, which can reduce the end-to-end delay while relieving backhaul pressure. To effectively utilize the limited cache size in practice, cooperative caching can be leveraged to exploit caching diversity, by allowing users served by multiple base stations under the emerging user-centric network architecture. This paper explores delay-optimal cooperative edge caching in large-scale user-centric mobile networks, where the content placement and cluster size are optimized based on the stochastic information of network topology, traffic distribution, channel quality, and file popularity. Specifically, a greedy content placement algorithm is proposed based on the optimal bandwidth allocation, which can achieve (1-1/e)-optimality with linear computational complexity. In addition, the optimal user-centric cluster size is studied, and a condition constraining the maximal cluster size is presented in explicit form, which reflects the tradeoff between caching diversity and spectrum efficiency. Extensive simulations are conducted for analysis validation and performance evaluation. Numerical results demonstrate that the proposed greedy content placement algorithm can reduce the average file transmission delay up to 50% compared with the non-cooperative and hit-ratio-maximal schemes. Furthermore, the optimal clustering is also discussed considering the influences of different system parameters.
The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: RAN, core network, and caching. We also present a general overview of 5G cellular networks composed of software defined network (SDN), network function virtualization (NFV), caching, and mobile edge computing (MEC) capable of meeting latency and other 5G requirements.
Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that extends cloud-computing capabilities to the edge of the Radio Access Network (RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper, we envision a collaborative joint caching and processing strategy for on-demand video streaming in MEC networks. Our design aims at enhancing the widely used Adaptive BitRate (ABR) streaming technology, where multiple bitrate versions of a video can be delivered so as to adapt to the heterogeneity of user capabilities and the varying of network connection bandwidth. The proposed strategy faces two main challenges: (i) not only the videos but their appropriate bitrate versions have to be effectively selected to store in the caches, and (ii) the transcoding relationships among different versions need to be taken into account to effectively utilize the processing capacity at the MEC servers. To this end, we formulate the collaborative joint caching and processing problem as an Integer Linear Program (ILP) that minimizes the backhaul network cost, subject to the cache storage and processing capacity constraints. Due to the NP-completeness of the problem and the impractical overheads of the existing offline approaches, we propose a novel online algorithm that makes cache placement and video scheduling decisions upon the arrival of each new request. Extensive simulations results demonstrate the significant performance improvement of the proposed strategy over traditional approaches in terms of cache hit ratio increase, backhaul traffic and initial access delay reduction.
This article analyzes the challenges and opportunities that the upcoming definition of future 5G mobile networks brings to the mobile broadband and broadcast industries to form a single converged network. It reviews the state-of-the-art in mobile and broadcast technologies and the current trends for convergence between both industries. This article describes the requirements and functionalities that the future 5G must address in order to make an efficient and flexible cellular-broadcasting convergence. Both industries would benefit from this convergence by exploiting synergies and enabling an optimum use of spectrum based on coordinated spectrum sharing.
The deployment of small cells is expected to gain huge momentum in the near
future, as a solution for managing the skyrocketing mobile data demand growth.
Local caching of popular files at the small cell base stations has been
recently proposed, aiming at reducing the traffic incurred when transferring
the requested content from the core network to the users. In this paper, we
propose and analyze a novel caching approach that can achieve significantly
lower traffic compared to the traditional caching schemes. Our cache design
policy carefully takes into account the fact that an operator can serve the
requests for the same file that happen at nearby times via a single multicast
transmission. The latter incurs less traffic as the requested file is
transmitted to the users only once, rather than with many unicast
transmissions. Systematic experiments demonstrate the effectiveness of our
approach, as compared to the existing caching schemes.
Integrating joint network function virtualization (NFV) and software-defined networks (SDNs) with digital televisions (TVs) into home environments, has the potential to provide smart TV services to users, and improve their quality of experience (QoE). In this regard, this paper focuses on one of the next generation services so-called follow me service (FMS). FMS is a service offered by 5gNB to user equipments (UEs) in indoor environments (e.g., home), it enables its clients to use their smart phones to select media content from content servers, then cast it on the nearest TV set (e.g., living room) and continue watching on the next TV set (e.g., kitchen) while moving around the indoor coverage area. FMS can be provisioned by utilizing UEs geolocation information and robust mechanisms for switching between multiple 5G radio access technologies (RATs), based on the intelligence of the SDN/NFV intelligent home IP gateway of the Internet of Radio Light (IoRL) project paradigm. In view that the actual IoRL system is at its early development stage, we step forward by using Mininet platform to integrate SDN/NFV virtualization into 5G multi-RAT scenario and provide performance monitoring with measurements for the identified service. Simulation results show the effectiveness of our proposal under various use case scenarios by means of minimizing the packet loss rate and improving QoE of the home users.
The emergence of two new technologies, namely, software defined networking (SDN) and network function virtualization (NFV), have radically changed the development of network functions and the evolution of network architectures. These two technologies bring to mobile operators the promises of reducing costs, enhancing network flexibility and scalability, and shortening the time-to-market of new applications and services. With the advent of SDN and NFV and their offered benefits, the mobile operators are gradually changing the way how they architect their mobile networks to cope with ever-increasing growth of data traffic, massive number of new devices and network accesses, and to pave the way toward the upcoming fifth generation networking. This survey aims at providing a comprehensive survey of state-of-the-art research work, which leverages SDN and NFV into the most recent mobile packet core network architecture, evolved packet core. The research work is categorized into smaller groups according to a proposed four-dimensional taxonomy reflecting the: 1) architectural approach, 2) technology adoption, 3) functional implementation, and 4) deployment strategy. Thereafter, the research work is exhaustively compared based on the proposed taxonomy and some added attributes and criteria. Finally, this survey identifies and discusses some major challenges and open issues, such as scalability and reliability, optimal resource scheduling and allocation, management and orchestration, and network sharing and slicing that raise from the taxonomy and comparison tables that need to be further investigated and explored.
5G network architecture and its functions are yet to be defined. However, it is generally agreed that cloud computing, network function virtualization (NFV), and software defined networking (SDN) will be key enabling technologies for 5G. Indeed, putting all these technologies together ensures several advantages in terms of network configuration flexibility, scalability, and elasticity, which are highly needed to fulfill the numerous requirements of 5G. Furthermore, 5G network management procedures should be as simple as possible, allowing network operators to orchestrate and manage the lifecycle of their virtual network infrastructures (VNIs) and the corresponding virtual network functions (VNFs), in a cognitive and programmable fashion. To this end, we introduce the concept of "Anything as a Service" (ANYaaS), which allows a network operator to create and orchestrate 5G services on demand and in a dynamic way. ANYaaS relies on the reference ETSI NFV architecture to orchestrate and manage important services such as mobile Content Delivery Network as a Service (CDNaaS), Traffic Offload as a Service (TOFaaS), and Machine Type Communications as a Service (MTCaaS). Ultimately, ANYaaS aims for enabling dynamic creation and management of mobile services through agile approaches that handle 5G network resources and services.
The upcoming 5G technologies promise to enable ultra low latency services such as remote robotics, augmented reality or vehicle to vehicle communications. Fog and MEC computing can enable low latency services for many different scenarios by moving the cloud and some network functions closer to the user. In the case of mobile networks this improvement is traditionally introduced in the reference point that defines the limit of the operator domain. In this paper we explore the introduction of an intermediate component in the LTE standard architecture, describing its functionality and providing experimental results on its use. This component, called Fog Gateway, can process the data plane for specific services to prevent all the traffic reaching the core network. The gateway analyzes the GTP traffic inner destination IP in order to determine whether to route the packet to the fog network or forward it to the destination SGW. The solution is compliant with standard LTE equipment all along the path (UE, eNodeB, EPC), so it can be implemented in current networks, and the preliminary figures, obtained combining emulated and COTS equipment, show an improvement of up to the 78% in terms of latency reduction.
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.
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- R Viola
- A Martin
- M Zorrilla
- J Montalbn
Anything as a service for 5G mobile systems
- T Taleb
- A Ksentini
- R Jntti
ETSI white paper 11 mobile edge computing—A key technology towards 5G—ETSI wp11 MEC a key technology towards 5G
- Y C Hu
- M Patel
- D Sabella
- N Sprecher
- V Young
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- D Klein