Access Network Technologies Research Articles

TENSAI - Practical and Responsible Observability for Data Quality-aware Large-scale Analytics

Given a large-scale mobile network with a variety of equipment and radio access network technologies for an approximate 20 million subscribers, there are many types of data that can be used for big data analytics and machine learning (ML) tasks for network operations, monitoring, and optimization. However, a variety of data is measured, collected, and propagated through numerous complex data and software systems. Thus, people, software components, and data-driven operations for big data and ML pipelines face great challenges in dealing with data quality impacts. Data quality related problems occur and are propagated through complex operations involving different types of data, people, software components, and analytics that cannot be solved purely through data quality engineering. This paper discusses our TENSAI framework, as a practical and responsible observability for ensuring data quality in such a mobile network. TENSAI focuses on methods of communication, strategy specifications, and data quality engineering for diverse types of data and analytics among different types of operations. TENSAI presents techniques for capturing and communicating causes/effects of data quality problems clearly to all relevant stakeholders, developing data quality-aware adaptation strategies for actions on data that can be integrated into analytics processes, and engineering data quality awareness in software and data pipelines. Thus, TENSAI supports full visibility of data quality problems and impacts among related systems to empower the utilization and adaptation of data analytics for different types of operations. We will illustrate our TENSAI with several real-world data types, pipelines, and cases based on a real-world mobile network.

Research on future 6G green wireless networks

The 6G technology is expected to revolutionize wireless networks by enabling intelligent connectivity of all devices. The concept of a 6G green network aims for ubiquity, intelligence, simplicity, environmental friendliness, and carbon reduction. This paper delves into the essential energy-saving technologies for 6G radio access networks within an energy-efficient framework and proposes a multi-tiered cloud-enabled endogenous intelligent architecture for 6G wireless networks. It provides a design case for an intelligent endogenous wireless network architecture in the context of 6G. Building upon this intelligent network architecture, the paper introduces novel ideas for protocol stack design and multi-rate signaling transmission semantic transmission model. Furthermore, it analyzes the relationship between AI model deployment and energy efficiency while presenting an example of hierarchical deployment of federated learning models. The paper suggests deploying a hierarchical federated learning model within the network architecture to enhance energy efficiency.

Simplified coherent optical network units for very-high-speed passive optical networks

Future passive optical networks (PONs) for 200Gb/s/λ, and beyond, pose a significant technological challenge. The use of coherent technology in access networks provides a great solution based on concepts from mature technologies to achieve the speeds needed for very-high-speed PON (VHSP). In this paper, we provide an overview of the currently demonstrated technologies and propose a possible simplified optical network unit (ONU) for time division multiplex PON (TDM-PON). The proposed ONU uses a single polarization heterodyne receiver, using either a balanced photodiode or a single-ended one and an electro-absorption modulated laser (EML)-based transmitter. The experimental demonstration using the proposed ONU in a bidirectional, symmetrical transmission over fiber distances of 20 km and 40 km shows the viability of the technology. The downstream direction achieves a power budget of 34.3/29.3 dB for 20 km and 33.9/29 dB for 40 km, for a balanced/single-ended receiver, whereas the upstream transmission achieves 29.3 dB for both scenarios.

Comparative analysis of passive optical networks using multiple parameters: a review

Abstract The desideratum for high-end communication networks and high-speed data transfer has surged exorbitantly in the contemporary era. To meet this demand, various optical access network technologies have been invented and assembled, including gigabit passive optical networks (GPON), Ethernet passive optical networks (EPON), and wavelength multiplexed passive optical networks (WDM-PON). Due to their huge bandwidth capacity, excellent compatibility, dynamic bandwidth allocation, and dispersion tolerance, these technologies constitute potential substitutes for imparting local loop broadband services. This paper presents a thorough review and comparative analysis of the contemporary evolution in optical access network technologies with an accentuate on their functionality, mechanism, manoeuvre, and applications. We are also looking at the upcoming development of these innovations, including the incorporation of 100 G-EPON connectivity to meet the enhanced bandwidth demands precipitated by IP video, mobile broadband, and the Internet of things (IoT).

Implementation of Ukraine's State Policy in Innovative Practices of Educational Institutions in the Context of Digital Transformation

In the article, the directions and strategic tasks of the state policy in the implementation of innovative practices of educational institutions, the direction of the ways of modernization of modern education in view of the integration of Ukraine into the European educational space are important; the views of scientists regarding the reform of the educational sphere were analyzed, especially the issues of digital transformation in education; new directions of program actions of the state, an effective management system for the innovative development of education in Ukraine in modern conditions, taking into account the European experience.
 The requirements for scientific and theoretical substantiation and the development of a practical model for the modernization of comprehensive general secondary education and the improvement of the mechanism of regulation of modern innovative state practices, digitalization of new, new processes in educational institutions, and the regulatory tools of the state influence on this process have been determined.
 Outlined specific tasks for the development of the educational process in educational institutions in the context of education reform in Ukraine, ensuring the functioning of the education system in the face of societal changes to implement state educational policies, innovative practices, scientific research, and methodological work.
 Digitization based on the progressive development of network access technologies; semiconductor technologies; software and its cross-use, becomes an important condition in the field of education, and service models of interaction with users become the leading methods of communication.
 Innovative educational projects are described, which highlight the general characteristics of innovations in education, the problems of their accounting and support; based on the analysis of the current state of implementation and dissemination of innovations in educational institutions within the limits of new means and directions of work, which cover all educational and methodological, educational activities of the school and lead to positive changes; modern approaches to the introduction of innovations into the system of general secondary education in Ukraine are formulated.

Cost-effective live video streaming for internet of connected vehicles using heterogeneous networks

Smart vehicles of Internet of Connected Vehicles (IoCV) create Ad-Hoc networks and are persistently connected to the Internet through different network access technologies like Wifi, cellular, and dedicated short range communication (DSRC). A vehicle may prefer one network access technology over another based on availability, coverage area, and network access cost. The choice also depends on application requirements, like delay-tolerant or delay-sensitive, and application objectives, like maximizing data rate, maximizing cellular traffic offloading, etc. Cellular traffic offloading using Wifi access points and vehicle-to-vehicle (V2V) collaborations are very challenging due to the intermittent short coverage areas of Wifi access points and highly dynamic connectivity of V2V Ad-Hoc network. Most existing works on traffic offloading have considered delay-tolerant or on-demand services, whereas delay-constrained live video streaming services are mostly ignored. This work is focused on live video streaming services over IoCVs, where the non-triviality of the network selection task is demonstrated by formulating the task as an integer linear programming problem (ILPP). Accordingly, a distributed algorithm is proposed, which greedily maximizes cellular traffic offloading while significantly reducing the control packet overheads. Compared to existing works, the proposed work provides the best service effectiveness with a balanced performance on traffic offloading, service efficiency, and user satisfaction.

Low-latency and Energy Efficient Technologies for New Optical Access Networks

Low-latency and Energy Efficient Technologies for New Optical Access Networks

FastTraffic: A lightweight method for encrypted traffic fast classification

Nowadays, most Internet communications have adopted encrypted network access technology for privacy protection, so encrypted traffic classification (ETC) has become a crucial research point to support network management and ensure cyberspace security. Meanwhile, off-the-shelf deep learning (DL)-based approaches suffer from long preprocessing time, large input size, and a trade-off between model complexity and accuracy. There is a tough challenge to deploy them on mainstream network devices and achieve fast and accurate traffic classification. In this paper, we design FastTraffic, a lightweight DL-based method for ETC on low-configuration network devices. To speed up processing, we set an IP packet as the granularity of FastTraffic, truncate the informative parts in packets as inputs, and utilize a text-like packet tokenization method. For a lightweight and effective model, we propose an N-gram feature embedding method to represent structured and sequential features of packets and design a three-layer MLP to complete fast classification. We compare FastTraffic with eight state-of-the-art ETC methods on three public benchmark datasets. The experimental results show that FastTraffic obtains better classification performance than the other seven methods with only 0.43M model parameters. Besides, it can also achieve high throughput on low-configuration devices and consume a small amount of computing and storage resources. Therefore, FastTraffic is a lightweight ETC method capable of large-scale deployment on Internet devices.

A Probabilistic Copula-Based Fault Detection Method With TrAdaBoost Strategy for Industrial IoT

With the development of information and technology, Industrial Internet of Things (IIoT) are widely-spread. In the face of heterogeneous network access technologies, IIoT data presents characteristics such as massiveness, heterogeneity and dynamics. In this work, an inductive transfer modeling fault detection method based on the vine copula-based dependence description (VCDD), named TrAdaBoost Vine Copula-Based Dependence Description (TAVCDD), is proposed for fusing the target and source data. Especially, there is too little data in target domain operation condition to establish a good monitoring model. In the modeling process, a generalized local probability (GLP) index is used to set the selection probabilities of samples in the target and source domains. In addition, two adaptive indexes are used to determine the number of training samples in one iteration and the number of iterations. To avoid unbalanced samples between the target and source datasets, an adaptive sample selection strategy called active VCDD (AVCDD) is introduced to explore the underlying distribution of the target sample space and to select the most “informative” samples from the source dataset. The proposed TAVCDD method is compared with six state-of-the-art methods via a numerical example, the Tennessee Eastman process, and the ethylene cracking furnace process.

Application Research of Optical Fiber Sensing Technology in Power Optical Fiber Communication Systems

The distributed optical fiber sensing system uses optical fiber as the sensing element and signal transmission medium at the same time, and adopts advanced Optical Time-domain Reflectometry (OTDR) technology to detect the changes of temperature and strain at different positions along the optical fiber, thus achieving a truly distributed measurement. Using the Micro-structured Optical Time-domain Reflectometry (M-OTDR) optical fiber sensing access network technology, combined with optical link components such as fault locators, can judge the operating status of optical fiber Composite phase cable (IOPPC) lines, and detect faults (such as breakpoints) in optical fiber communication location and diagnosis. The position of the lightning strike is determined by detecting the change of polarization state caused by the transmitted light in the optical fiber inside the IOPPC before and after a lightning strike, and the positioning error is less than 1%. By observing the Brillouin frequency shift chart, the ice state can be accurately identified and the positioning function can be realized. By monitoring the vibration frequency of the IOPPC cable, the line dancing phenomenon can be detected in time to prevent line faults caused by dancing. The online safety and health monitoring of the operating state of transmission lines are realized.

A review of machine learning techniques for enhanced energy efficient 5G and 6G communications

Cellular technologies have evolved continuously from the 1st to the 5th generation (5G) to meet the exponentially growing needs of bandwidth, throughput and latency. However, the energy consumption experienced a proportional rise generation-wise when new hardware to support additional applications were incorporated. 5G, which already consumes four times more energy than 4G, is expected to bring about a significant spike in the conventional trajectory of energy consumption. 5G is therefore triggering a major concern for energy efficiency and with an even higher technical and network complexity, 6G will pause an unprecedented challenge to energy efficiency and sustainability. This paper focuses on the energy consumption at the base station and access network levels, which amount to around 80% of energy consumption in mobile networks. Machine learning techniques can be employed in several ways to improve the energy efficiency in these components. In this paper, efficient base station deployment strategies and adaptive operational modes as well as access network technologies such as massive MIMO and millimeter waves, which employ machine learning to enhance the energy efficiency, have been reviewed in depth. Since existing research works have focused mostly on a single optimization strategy at either the base station or access network level, this paper proposes a framework, which combines efficient base station deployment methods and machine learning-based switching between different operation modes based on traffic load. Moreover, an adaptive beamforming methodology through identification of hotspots and user association, sub-channel and power allocation in heterogeneous networks is discussed in detail.

A Rotated-Constellation Based Method for BER Analysis in Uplink NOMA Systems

Non-orthogonal multiple access (NOMA) is one of candidate technologies in the next generation radio access networks, in which the joint maximum-likelihood (JML) detector has the optimal estimation performance. In this paper, we propose a rotated-constellation based method to regularize the decision boundaries of received constellation. Through regular decision boundaries, we further derive the approximate closed-form BER expressions of uplink NOMA systems in Rayleigh fading channels. Numerical results verify the effectiveness of rotated-constellation based method and conform to our derivation. It is also shown through simulations that the proposed analysis method outperforms conventional method in terms of accuracy.

Design of Semipersistent Resource Allocation in LTE-V Network

Radio network access technology currently used in 4G/5G is Long Term Evolution-Advanced (LTE-A), which was developed by 3rd Generation Partnership Project (3GPP). Device-to-device (D2D) communication is a technology enabling direct communications among wireless devices without forwarding through an evolved Node B (eNB). Moreover, D2D transmission can support vehicles as a vehicle-to-vehicle (V2V) environment. It is possible to avoid accidents via exchanging movement-related information among vehicles and effectively increase driving safety (and efficiency). However, radio resources are limited in radio networks. A vehicle transmits through D2D in Long Term Evolution-Vehicle (LTE-V) mode-3 standard, and an eNB can allocate the same spectrum radio resources for cellular and V2V links simultaneously. When using the same radio resources, the probability of interference may increase. This study designed a semipersistent resource allocation algorithm based on different cycles in an LTE-V network. Moreover, resource allocation under different cycles was analyzed, and a scheme for resource selection is proposed based on cycle size. The proposed Semi Persistent Gain Aware Resource Allocation (SP-GARA) scheme selects resources based on the expected sum rate of the cycle size and analyzes and discusses the results of the total sum rate at different cycles and speeds for an improved performance.

A Survey of Application and Key Techniques for Mobile Crowdsensing

Mobile crowdsensing (MCS) uses ubiquitous smart devices and network access technology to migrate sensing tasks from a centralized platform to a distributed computing terminal across time and space and provides new approaches for solving large-scale, diversified, and complex sensing problems. MCS research is of great importance and has attracted wide attention from researchers. Accordingly, this article summarizes the current research status of MCS. First, the typical applications of MCS are summarized. Then, the research status of key technologies, such as task allocation, incentivizing, data transmission, quality assurance, and evaluation methods, is considered. Finally, future research directions of MCS are given to provide a reference for in-depth MCS research.

An Analysis of Influence of Network Topology on Power Network Reliability Based on OPLC

Optical fiber to the home (PFTTH) is an access network technology, which uses optical fiber composite low voltage cable (OPLC) to realize the deep integration of the power grid and communication network infrastructure. Key technologies include network structure design and high-speed access technology, PON technology, and the OPLC technology. This paper introduces the key technologies of power fiber-to-home networking and the influence of network topology on the reliability of PFTTH.

A Novel Cooperative Relaying-Based Vertical Handover Technique for Unmanned Aerial Vehicles

The real-time monitoring and autonomous decision making through unmanned aerial vehicles (UAVs) are the potential applications of future networks. Vertical handover in future networks is a mechanism to switch communication between different network access technologies like Wireless Local Area Network (WLAN), Worldwide Interoperability for Wireless Microwave Access (WiMAX), Third-Generation (3G), Fourth-Generation (4G), and Fifth-Generation (5G) mobile technologies. These technologies have significant importance in providing fast, reliable, and timely communication. However, during a vertical handover, an inadequate delay and packet loss can cause considerable disruption in maintaining communication sessions and results in intolerable end-to-end delay, disconnectivity, and poor packet delivery ratio. The proposed work addresses the vertical handover method in UAVs communication by designing a relay-based vertical handover technique. The relay UAVs is an assistant node, requiring an organized and intelligent deployment that assists in vertical handover and communication by minimizing the average packet loss and average delay from source to destination. Moreover, a multicriteria handover parameter triggering is used for seamless and more extended network coverage. Extensive simulations using S-shaped and U-shaped trajectories are designed and simulated for relay-based vertical handover performance evaluation. The results obtained show that our proposed relay-based handover method offers seamless connectivity and high-performance experienced during the vertical handover process. The extensive comparison with state-of-the-art techniques proves that the proposed method is better in terms of 18% handover success rate, 21% end-to-end delay, and 29% packet loss.

Inference of Wi-Fi busy time fraction based on Markov chains

IEEE 802.11 has emerged as a vital wireless network access technology for mobile devices. By providing the potential for high connectivity speeds, this technology has led to a huge rise in the number of access points (APs). In such environments, mobile devices have the choice to join several Wi-Fi networks. Despite its importance to user Quality of Experience (QoE), the AP selection is still trivial since it focuses at best on the received signal strength if not only the user’s history. Crucial metrics that capture the overall dynamics of the AP load condition, such as the network load, are not taken into account. In this paper, we propose to use the Busy Time Fraction (BTF) as a metric to choose the best AP to attach to. The BTF level of a given channel is inferred based on the frame aggregation scheme proposed since the 802.11n standard. In this regard, we build a proof of concept system, FAM (Frame Aggregation based method) that leverages the theoretical frame aggregation levels of a probe traffic returned by two analytical Markovian models and the measured ones in order to estimate not only the BTF but also the nature of the traffic. We validate the accuracy of our proposed approach against ns-3 simulations under several scenarios.

Achieving QoS for bursty uRLLC applications over passive optical networks

Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict quality of service (QoS) requirements. The passive optical network (PON) is a popular access network technology, which is envisioned to handle such applications at the access segment of the network. However, the existing standards cannot handle strict QoS constraints for such applications. The available solutions rely on instantaneous heuristic decisions and maintain QoS constraints (mostly bandwidth) in an average sense. Existing proposals in generic networks with optimal strategies are computationally complex and are, therefore, not suitable for uRLLC applications. This paper presents a novel computationally efficient, far-sighted bandwidth allocation policy design for facilitating bursty uRLLC traffic in a PON framework while satisfying strict QoS (age of information/delay and bandwidth) requirements. To this purpose, first we design a delay-tracking mechanism, which allows us to model the resource allocation problem from a control-theoretic viewpoint as a model predictive control (MPC) problem. MPC helps in making far-sighted decisions regarding resource allocations and captures the time-varying dynamics of the network. We provide computationally efficient polynomial time solutions and show their implementation in the PON framework. Compared to existing approaches, MPC can improve delay violations by 15% and 45% at loads of 0.8 and 0.9, respectively, for delay-constrained applications of 1 ms and 4 ms. Our approach is also robust to varying traffic arrivals.

Optical access evolutions towards SDN and disaggregated hardware: an operator perspective [Invited

In this paper, we review recent advances of the optical access network in the path toward Cloud-computing-related technologies, software defined networking, and network function virtualization. We present the new network management architectures proposed by standardization bodies such as the Broadband Forum and non-profit organizations such as the Open Networking Foundation and propose to use today’s Orange-France Operation Support System as a baseline to discuss different evolution paths. We also provide a review of recent advances in the optical line terminal (OLT) hardware evolution, from white-box OLTs to general purpose hardware, and identify the remaining related challenges, such as energy consumption and interoperability. We also show how Cloud-computing technologies in optical access networks could remove the frontier between network segments (home local area network, optical transport network, and mobile network) by deeply improving the synergy among them.

BCSM: Blockchain‐based cooperative spectrum management system for 5G NR‐U and WiFi coexistence in the unlicensed band

The licensed band is crowded and suffers from immense mobile data traffic growth, which exceeded 58 exabytes per month in 5 years. Meanwhile, a significant portion of the unlicensed band is underutilized and not coordinated efficiently. Experiments in some urban areas of the world have shown that only 5% of the unlicensed 5 GHz band is being used. 5G NR-U technology supports 5G networks in the unlicensed band to alleviate the traffic congestion and boosts 5G networks capacity. Different heterogeneous network access technologies already use the unlicensed band. Consequently, 5G NR-U networks will operate in the proximity of the other coexisting networks, such as WiFi networks in the 5 GHz and 6 GHz bands. In such environments, assessing the shared spectrum becomes challenging and necessitates adequate protocols to identify idle slots for successful transmissions. Cooperative Spectrum Sensing (CSS) improves the spectrum assessment process, as the decision about the spectrum state is rendered based on the local decisions of multiple sensing nodes. CSS is exploited by integrating it with Blockchain technology to design a decentralized cooperative spectrum management system called: Blockchain-Based Cooperative Spectrum Management (BCSM). The system is attributed to ameliorating 5G NR-U awareness about the neighboring WiFi networks traffic in the unlicensed band. An algorithm is designed for performing distributed cooperative spectrum assessment between the 5G NR-U base stations to profile the WiFi networks traffic in their proximity. To ensure fairness based on the effort expended in assessing the spectrum, a priority-based algorithm is designed for spectrum access scheduling. A proof-of-concept is implemented using private Ethereum Blockchain and NS3 simulator. Finally, the system's accuracy is evaluated empirically along with theoretical security analysis.