Next-generation Network Architecture Research Articles

AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems.

Traditional heterogeneous networks (HetNets) are constrained by their hardware design and configuration. These HetNets have a limited ability to adapt to variations in network dynamics. Software-defined radio technology has the potential to address this adaptability issue. In this paper, we introduce a software-defined radio (SDR)-based long-term evolution licensed assisted access (LTE-LAA) architecture for next-generation communication networks. We show that with proper design and tuning of the proposed architecture, high-level adaptability in HetNets becomes feasible with a higher throughput and lower power consumption. Firstly, maximizing the throughput and minimizing power consumption are formulated as a constrained optimization problem. Then, the obtained solution, alongside a heuristic solution, is compared against the solutions to existing approaches, showing our proposed strategy is drastically superior in terms of both power efficiency and system throughput. This study is then concluded by employing artificial intelligence techniques in multi-objective optimization, namely random forest regression, particle swarm, and genetic algorithms, to balance out the trade-offs between maximizing the throughput and power efficiency and minimizing energy consumption. These investigations demonstrate the potential of employing the proposed LTE-LAA architecture in addressing the requirements of next-generation HetNets in terms of power, throughput, and green scalability.

Dynamic Telemetry and Deep Neural Networks for Anomaly Detection in 6G Software-Defined Networks

With the increasing availability of computational power, contemporary machine learning has undergone a paradigm shift, placing a heightened emphasis on deep learning methodologies. The pervasive automation of various processes necessitates a critical re-evaluation of contemporary network implementations, specifically concerning security protocols and the imperative need for swift, precise responses to system failures. This article introduces a meticulously crafted solution designed explicitly for 6G software-defined networks (SDNs). The approach employs deep neural networks for anomaly detection within network traffic, contributing to a more robust security framework. Furthermore, the paper delves into the realm of network monitoring automation by harnessing dynamic telemetry, providing a specialized and forward-looking strategy to tackle the distinctive challenges inherent in SDN environments. In essence, our proposed solution aims to elevate the security and responsiveness of 6G mobile networks. By addressing the intricate challenges posed by next-generation network architectures, it seeks to fortify these networks against emerging threats and dynamically adapt to the evolving landscape of next-generation technology.

A Group Handover Scheme for Supporting Drone Services in IoT-Based 5G Network Architectures

Next generation mobile networks are expected to integrate multiple drones organized in Flying Ad Hoc Networks (FANETs) to support demanding and diverse services. The highly mobile drones should always be connected to the network in order to satisfy the strict requirements of upcoming applications. As the number of drones increases, they burden the network with the management of signaling and continuous monitoring of the drones during data transmission. Therefore, designing transmission mechanisms for fifth-generation (5G) drone-aided networks and using clustering algorithms for their grouping is of paramount importance. In this paper, a clustering and selection algorithm of the cluster head is proposed together with an efficient Group Handover (GHO) scheme that details how the respective Point of Access (PoA) groups will be clustered. Subsequently, for each cluster, the PoA elects a Cluster Head (CH), which is responsible for manipulating the mobility of the cluster by orchestrating the handover initiation (HO initiation), the network selection, and the handover execution (HO execution) processes. Moreover, the members of the cluster are informed about the impending HO from the CH. As a result, they establish new uplink and downlink communication channels to exchange data packets. In order to evaluate the proposed HO scheme, extensive simulations are carried out for a next-generation drone network architecture that supports Internet of Things (IoT) and multimedia services. This architecture relies on IEEE 802.11p Wireless Access for Vehicular Environment (WAVE) Road Side Units (RSUs) as well as Long-Term Evolution Advanced (LTE-A) and IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX). Furthermore, the proposed scheme is also evaluated in a real-world scenario using a testbed deployed in a controlled laboratory environment. Both simulation and real-world experimental results verify that the proposed scheme outperforms existing HO algorithms.

A Cybertwin Based Multimodal Network for ECG Patterns Monitoring Using Deep Learning

In next-generation network architecture, the Cybertwin drove the sixth generation of cellular networks sixth-generation (6G) to play an active role in many applications, such as healthcare and computer vision. Although the previous sixth-generation (5G) network provides the concept of edge cloud and core cloud, the internal communication mechanism has not been explained with a specific application. This article introduces a possible Cybertwin based multimodal network (beyond 5G) for electrocardiogram (ECG) patterns monitoring during daily activity. This network paradigm consists of a cloud-centric network and several Cybertwin communication ends. The Cybertwin nodes combine support locator/identifier identification, data caching, behavior logger, and communications assistant in the edge cloud. The application focuses on monitoring the ECG patterns during daily activity because few studies analyze them under different motions. We present a novel deep convolutional neural network based human activity recognition classifier to enhance identification accuracy. The healthcare monitoring values and potential clinical medicine are provided by the Cybertwin based network for ECG patterns observing.

A robust tuned classifier-based distributed denial of service attacks detection for quality of service enhancement in software-defined network

In today’s world, Software-Defined Networking (SDN) plays a significant role in the advancement of next-generation network architecture that offers vast control to the network operators. However, the control layer is vulnerable to Distributed Denial of Service (DDoS) attacks where DDoS is one of the most powerful and devastating cyber-attacks. Thus, the development of a DDoS attack detection mechanism is very essential since these kinds of attacks have a direct impact on the overall performance of the SDN. In this paper, a new robust Tuned support vector machine-based DDoS attack detection methodology has been proposed to categorize the benign traffic from DDoS attack traffic on the SDN. Primarily, the network is created with controller and OpenFlow switch and the communication can be carried out through secure channels among different benign users and also attackers. Afterward, the multi-characteristic values are extracted by the effective extraction strategy which consists of the six-tuple characteristic values matrix. Finally, the tuned classifier has been implemented with the aid of optimization algorithm for differentiating the abnormal traffic and the normal traffic. The performance results manifest that the proposed detection framework achieves a higher accuracy of 98% and precision of 99% when compared with existing classifiers.

Network Architecture for Machine Learning: A Network Operator's Perspective

Data-driven network design suggests that substantial technology advances of 5G and 6G networks will be enabled with enhanced automation, intelligence, and user-experience-focused capabilities. Network operators need to upgrade the standard network models by applying machine learning (ML) to address the complexities of next-generation network deployments. This article explores the role of ML and its interplay with wireless communications networks to develop the next-generation network architecture. A use case scenario for self-configuration of radio-access-network-based notification areas (RNAs) for effective resource management is analyzed to exemplify the proposed architecture where a paging load reduction of 64 percent is observed in the resulting RNA clusters. A conceptual framework for RNA configuration and management enabling a broader perspective toward an ML-driven hybrid self-organizing network is discussed to improve the signaling load to attain reduced latency and improved network capacity.

Detection of malicious consumer interest packet with dynamic threshold values.

As a promising next-generation network architecture, named data networking (NDN) supports name-based routing and in-network caching to retrieve content in an efficient, fast, and reliable manner. Most of the studies on NDN have proposed innovative and efficient caching mechanisms and retrieval of content via efficient routing. However, very few studies have targeted addressing the vulnerabilities in NDN architecture, which a malicious node can exploit to perform a content poisoning attack (CPA). This potentially results in polluting the in-network caches, the routing of content, and consequently isolates the legitimate content in the network. In the past, several efforts have been made to propose the mitigation strategies for the content poisoning attack, but to the best of our knowledge, no specific work has been done to address an emerging attack-surface in NDN, which we call an interest flooding attack. Handling this attack-surface can potentially make content poisoning attack mitigation schemes more effective, secure, and robust. Hence, in this article, we propose the addition of a security mechanism in the CPA mitigation scheme that is, Name-Key Based Forwarding and Multipath Forwarding Based Inband Probe, in which we block the malicious face of compromised consumers by monitoring the Cache-Miss Ratio values and the Queue Capacity at the Edge Routers. The malicious face is blocked when the cache-miss ratio hits the threshold value, which is adjusted dynamically through monitoring the cache-miss ratio and queue capacity values. The experimental results show that we are successful in mitigating the vulnerability of the CPA mitigation scheme by detecting and blocking the flooding interface, at the cost of very little verification overhead at the NDN Routers.

Interest flooding attack mitigation in a vehicular named data network

Vehicular named data network (VNDN) is the next-generation network architecture for intelligent transportation system. Contrary to the conventional transmission control protocol/internet protocol (TCP/IP) communication model, VNDN follows a data-centric approach where the user is interested in ‘WHAT’ instead of ‘WHERE’. Interest flooding attack (IFA) is one of the prominent security concerns in VNDNs. In IFA, attackers request for non-existent content to exhaust network resources and cause Interest packet flooding across the network. A novel attack mitigation scheme to counter IFA in VNDN has been proposed in this study. The proposed priority-based per-flow Interest rate monitoring (PP-FIRM) scheme determines the suspicious flow of malicious incoming Interest packets in attacked vehicles. A priority flag is assigned to the incoming flow of Interest packets that detects the occurrence of an attack. The priority of incoming Interest packet flow is calculated using a collaborative or neighbour-assisted approach. A comparison with another attack mitigation scheme validates that the proposed scheme performs better in terms of an improved cache hit ratio and Interest satisfaction ratio during the attack window. Besides this, pending Interest table utilisation, packet collisions rate, Interest packets retransmission count, end-to-end delay, and the ratio of timed out Interest packets have also been reduced. Furthermore, the scalability of the proposed research strategy is also evaluated by changing the density of attackers in real time. Moreover, in the proposed attack mitigation model, the rate of incoming legitimate Interest packets increases by reducing the drop rate of valid Interest packets.

Cognitive Popularity Based AI Service Sharing for Software-Defined Information-Centric Networks

As an important architecture of next-generation network, Software-Defined Information-Centric Networking (SD-ICN) enables flexible and fast content sharing in beyond the fifth-generation (B5G). The clear advantages of SD-ICN in fast and efficient content distribution and flexible control make it a perfect platform for solving the rapid sharing and cognitive caching of AI services, including data samples sharing and pre-trained models transferring. With the explosive growth of decentralized artificial intelligence (AI) services, the training and sharing efficiency of edge AI is affected. Various applications usually request the same AI samples and training models, but the efficient and cognitive sharing of AI services remain unsolved. To address these issues, we propose a cognitive popularity-based AI service distribution architecture based on SD-ICN. First, an SD-ICN enabled edge training scheme is proposed to generate accurate AI service models over decentralized big data samples. Second, Pure Birth Process (PBP) and error correction-based AI service caching and distribution schemes are proposed, which provides user request-oriented cognitive popularity model for caching and distribution optimization. Simulation results indicate the superiority of the proposed architecture, and the proposed cognitive SD-ICN scheme has 62.11% improved to the conventional methods.

Non-Orthogonal Unicast and Broadcast Transmission via Joint Beamforming and LDM in Cellular Networks

Limited bandwidth resources and higher energy efficiency requirements motivate incorporating multicast and broadcast transmission into the next-generation cellular network architectures, particularly for multimedia streaming applications. Layered division multiplexing (LDM), a form of non-orthogonal multiple access (NOMA), can potentially improve unicast throughput and broadcast coverage with respect to traditional orthogonal frequency division multiplexing (FDM) or time division multiplexing (TDM), by simultaneously using the same frequency and time resources for multiple unicast or broadcast transmissions. In this paper, the performance of LDM-based unicast and broadcast transmission in a cellular network is studied by assuming a single frequency network (SFN) operation for the broadcast layer, while allowing arbitrarily clustered cooperation among the base stations (BSs) for the transmission of unicast data streams. Beamforming and power allocation between unicast and broadcast layers, the so-called injection level in the LDM literature, are optimized with the aim of minimizing the sum-power under constraints on the user-specific unicast rates and on the common broadcast rate. The effects of imperfect channel coding and imperfect channel state information (CSI) are also studied to gain insights into robust implementation in practical systems. The non-convex optimization problem is tackled by means of successive convex approximation (SCA) techniques. Performance upper bounds are also presented by means of the S-procedure followed by semidefinite relaxation (SDR). Finally, a dual decomposition-based solution is proposed to facilitate an efficient distributed implementation of LDM in each of the SCA subproblems, where the unicast beamforming vectors can be obtained locally by the cooperating BSs. Numerical results are presented, which show the tightness of the proposed bounds and hence the near-optimality of the proposed solutions.

Large-scale cloud radio access networks: Fundamental asymptotic analysis

Large-scale cloud radio access network (LS-CRAN) is a highly promising next-generation cellular network architecture whereby many base stations (BSs) equipped with a massive antenna array are connected to a cloud-computing based central processor unit via digital front/backhaul links. This paper studies the asymptotic behavior of downlink (DL) performance of a LS-CRAN. As an asymptotic performance measure, the scaling exponent of the signal-to-interference-plus-noise-ratio (SINR) is derived for interference-free (IF), maximum-ratio transmission (MRT), and zero-forcing (ZF) operations. Our asymptotic analysis reveals four fundamental operating regimes and the performances of both MRT and ZF operations are fundamentally limited by the UL transmit power for estimating user's channel state information, not the DL transmit power. We obtain the conditions that MRT or ZF operation becomes interference-free, i.e., order-optimal. As higher UL transmit power is provided, more users can be associated and the data rate per user can be increased simultaneously while keeping the order-optimality.

Profitable and Energy-Efficient Resource Optimization for Heterogeneous Cloud-Based Radio Access Networks

As network operators invest more and more in infrastructure to keep up with the ever-increasing traffic demand, it has become important for them to operate networks in a profitable manner. The resulting expansion of network infrastructure also increases power consumption, which has a negative impact on both the environment and revenue. In this regard, the cloud-based radio access network (C-RAN), which is a promising next-generation network architecture, has gained much attention as a solution. In addition, by utilizing the macro base stations for coverage, the resulting heterogeneous C-RAN (H-CRAN) can further help optimize the network while increasing the complexity of resource optimization. In this paper, we study an optimal resource allocation for C-RANs to maximize profit while minimizing power consumption considering the inherent network uncertainties. Moreover, by allowing network operators to share networking resources among themselves, we show that the service outage can be minimized without installing additional bandwidth or base stations. The proposed multi-stage stochastic programming model makes a robust optimal decision that effectively responds to uncertainties from users’ mobility and service demand while maximizing both profit and energy efficiency. The extensive evaluation and comparison results show that the proposed solution can maximize profit and energy efficiency while minimizing the service outage under network uncertainties.

Resilient virtual communication networks using multi-commodity flow based local optimal mapping

Network virtualization has been considered a promising approach for promoting heterogeneous services and mechanisms towards the next-generation network architectures, which can greatly improve the global utilization efficiency of network resources, and hence the capacity of service delivery. However, virtual network invulnerability has long been ignored and little research effort has been made. In this paper, we presented a novel virtual network restoration approach, MFP-VNMH, which could enhance the VN mapping and service restoration subject to the physical link failure in the physical substrate whilst avoiding the remapping of the overall VNs. The performance of proposed MFP-VNMH is assessed through extensive numerical experiments for a range of network scenarios in comparison with the overall VN remapping approach and the end-to-end link remapping approach. The result is encouraging and demonstrates its effectiveness and efficiency for VN mapping update and service restoration upon physical substrate failures.

Software-Defined Next-Generation Satellite Networks: Architecture, Challenges, and Solutions

Traditional satellite networks depend on the closed and planned architecture. Thus, there are many challenges such as configuration update, new communication and networking technologies introduction, truly-differentiated services provision, satellite network device interoperability, and the integration of satellite and terrestrial networks. Software-defined networking (SDN) has the features of flexibility, programmability, and logical centralization, which increases network resource utilization, simplifies network management, reduces operating cost, and promotes the evolution and innovation. In this paper, a new software-defined architecture for next-generation satellite networks, called SoftSpace, is presented. The concepts of network function virtualization, network virtualization, and software-defined radio are exploited in the SoftSpace to facilitate the incorporation of new applications, services, and satellite communication technologies. This can not only reduce the capital expenditures and operational expenditures but also integrate satellite networks with terrestrial networks seamlessly, as well as can improve the interoperability of satellite network devices. In addition, we discuss the challenges and solutions for network management. The necessary network management instruments including multi-layer controller architecture, cooperative traffic classification, and utility-optimal network virtualization are presented. Finally, we discuss the challenges and solutions for space networking. The software-defined space networking solutions including quality of experience-aware space routing, SDN-enabled hybrid fault recovery mechanism, and software-defined space mobility management are developed.

Cache Placement in Fog-RANs: From Centralized to Distributed Algorithms

To deal with the rapid growth of high-speed and/or ultra-low latency data traffic for massive mobile users, fog radio access networks (Fog-RANs) have emerged as a promising architecture for next-generation wireless networks. In Fog-RANs, the edge nodes and user terminals possess storage, computation and communication functionalities to various degrees, which provides high flexibility for network operation, i.e., from fully centralized to fully distributed operation. In this paper, we study the cache placement problem in Fog-RANs, by taking into account flexible physical-layer transmission schemes and diverse content preferences of different users. We develop both centralized and distributed transmission aware cache placement strategies to minimize users' average download delay subject to the storage capacity constraints. In the centralized mode, the cache placement problem is transformed into a matroid constrained submodular maximization problem, and an approximation algorithm is proposed to find a solution within a constant factor to the optimum. In the distributed mode, a belief propagation based distributed algorithm is proposed to provide a suboptimal solution, with iterative updates at each BS based on locally collected information. Simulation results show that by exploiting caching and cooperation gains, the proposed transmission aware caching algorithms can greatly reduce the users' average download delay.

Big Data Enabled Mobile Network Design for 5G and Beyond

Mobile communication networks are more and more characterized by the integration of distributed and centralized computing and storage resources. Big data capability thus available throughout such networks will not only deliver enhanced system performance, but also profoundly impact the design and standardization of the next-generation network architecture, protocol stack, signaling procedure, and physical- layer processing. In this article, a mobile network architecture enabled by big data analytics is proposed, which is capable of efficient resource orchestration, content distribution, and radio access network optimization. The protocol stack configuration at each access point and the processing optimization of each layer are presented. Key physical layer designs including reference signals and frame structure are discussed. Moreover, utilizing signals in the transform domains, such as delay, Doppler, and angle, may bring enlarged coherence time of the effective channels. It enables much simpler physical layer design, and effectively bridges the latency gap between big data cloud computing and real-time network optimization.

State-Dependent Bandwidth Sharing Policies for Wireless Multirate Loss Networks

We consider a reference cell of fixed capacity in a wireless cellular network while concentrating on next-generation network architectures. The cell accommodates new and handover calls from different service-classes. Arriving calls follow a random or quasi-random process and compete for service in the cell under two bandwidth sharing policies: 1) a probabilistic threshold (PrTH) policy or 2) the multiple fractional channel reservation (MFCR) policy. In the PrTH policy, if the number of in-service calls (new or handover) of a service-class exceeds a threshold (difference between new and handover calls), then an arriving call of the same service-class is accepted in the cell with a predefined state-dependent probability. In the MFCR policy, a real number of channels is reserved to benefit calls of certain service-classes; thus, a service priority is introduced. The cell is modeled as a multirate loss system. Under the PrTH policy, call-level performance measures are determined via accurate convolution algorithms, while under the MFCR policy, via approximate but efficient models. Furthermore, we discuss the applicability of the proposed models in 4G/5G networks. The accuracy of the proposed models is verified through simulation. Comparison against other models reveals the necessity of the new models and policies.

Named Data Networking over WDM-Based Optical Networks

Named data networking (NDN) is a typical implementation of information-centric networking (ICN), which has been widely investigated in recent years for its potential as the next-generation network architecture. Optical networks are important as the demand for Internet bandwidth is enormous and they can be used to attain extremely high bandwidth. In this article, we study optical named data networking (ONDN), which is a complete solution for NDN deployment over optical networks based on wavelength-division multiplexing (WDM). To the best of our knowledge, this is the first attempt to deploy NDN over optical networks. We first introduce the basic network architecture of ONDN, based on which we discuss the intuitive packet routing and forwarding approach in ONDN, and propose an innovative alternative to improve network performance. In our proposed approach, a novel packet type, called a response packet, is proposed to reserve wavelength along the routing path for data transmission. Packet aggregation in ONDN is discussed to improve the efficiency of wavelength utilization. In addition, the optical node structure of ONDN is developed and discussed to support all functionalities proposed in this article.

Designing a ‘concept of operations’ architecture for next-generation multi-organisational service networks

Networked service organisations are increasingly adopting a ‘smarter networking’ philosophy in their design of more agile and customer-focused supply models. Changing consumer behaviours and the emergence of transformative technologies—industry 4.0, artificial intelligence, big data analytics, the Internet of Things—are driving a series of innovations, in terms of ‘products’ and business models, with major implications for the industrial enterprise, in their design of more ‘digitalised’ supply chains. For B2B systems, emerging ‘product-service’ offerings are requiring greater visibility, alignment and integration across an increasingly complex network of multiple partners and collaborators, in order to deliver a better service and customer ‘experience’. To support the design and operation of these multi-organisational service networks, we outline a concept of operations architecture here, underpinned by the literature and network theory, and demonstrate application using a series of exemplar case studies. Focusing on relational elements and the processes key to network integration within service supply networks, the cases inform a set of operating principles and protocols—applicable to all stakeholders ‘cooperating’, within a ‘shared’ environment. Equally critical is to understand how digital technologies may influence future operating philosophies. This article extends our theoretical understanding of network organisations, from a traditional ‘product’ perspective to that of ‘services’, and presents the case for developing a common, unified approach to designing diverse forms of multi-partner service networks.

Management architecture for location-aware self-organizing LTE/LTE-a small cell networks

This article proposes a novel architecture for next-generation cellular networks in indoor scenarios. The objective of this model is to support SON mechanisms based on the knowledge of the user equipment location in a small cell network, the use of such information being a key enabler for advanced SON methods. The defined design is the basis for systems providing innovative location-aware SON techniques that make use of user localization in medium/large indoor areas (e.g., malls or corporate buildings). The functional and physical characteristics of this architecture and their technical implications are analyzed. Proposed innovations to generic mobile architecture are described as well as specific implementation for LTE/LTE-A standards. Interoperability with standard management systems and localization services, congestion avoidance, and data offloading are the key drivers of the design. Finally, the capabilities of the proposed architecture are demonstrated through the performance analysis of a simple key use case for location-aware self-optimization.