Networks In 5G Research Articles

Big Data Network Optimization for Mobile Cellular Networks in 5G

5G ensures the provision of intelligent network and application services by means of connectivity to remote sensors, massive amounts of Internet of Things data, and fast data transmissions. Through the utilization of distributed compute architectures and by supporting massive connectivity across diverse devices like sensors, gateways, and controllers, 5G brings about a transformative revolution in the conversion of both big data at rest and data in motion into real-time intelligence. Big Data Analytics play an important role in the evolution of 5G standards, enabling intelligence across networks, applications, and businesses. Administrators of mobile organizations have access to a plethora of opportunities to enhance service quality through big data. Network optimization serves as a crucial method to achieve this task, with network prediction forming the foundation for such optimization. Ensuring network stability and security is essential for 5G mobile communication, considering its significance as an important tool in national life. Therefore, this work focuses on presenting big data network optimization for mobile cellular networks within the context of 5G. In order to improve the Quality of Experience (QoE) for users, this work explores various methods for integrating network optimization and Big Data analytics. The performance of the presented model is evaluated in terms of QoE, Throughput, handover rate, mobility, reliability, and network slicing.

Mobility Assisted Adaptive Clustering Hierarchy for IoT Based Sensor Networks in 5G and Beyond

One of the massive machine type communication (mMTC) applications for monitoring and sensing in 5G cellular network is the Internet of Things (IoT) based wireless sensor network (WSN). Non uniform battery usage by the nodes in these networks often results in creating energy holes or voids in the network making the network disconnected. An effective solution is to deploy multiple mobile nodes throughout the network, however finding optimal path for these mobile nodes is reported to be an NP hard problem. This paper proposes MAACH (Mobility Assisted Adaptive Clustering Hierarchy), an efficient mobility assisted clustering and routing framework for IoT based sensor network in 5G and beyond. Also, an elaborate method to calculate the exact path optimally for multiple mobile nodes is presented to alleviate non uniform energy dissipation of the sensing nodes. Simulation results show that our algorithm effectively finds the optimal trajectory of multiple mobile nodes in a distribute manner and also improves network stability period by 60-70% and the network lifetime by 70-90% across multiple network deployments.

Resource Allocation Energy Efficient Algorithm for H-CRAN in 5G

In today's generation, the demand for data rates has also increased due to the rapid surge in the number of users. With this increasing growth, there is a need to develop the next fifth generation network keeping in mind the need to replace the current 4G cellular network. The fifth generation (5G) design in mobile communication technology has been developed keeping in mind all the communication needs of the users. Heterogeneous Cloud Radio Access Network (H-CRAN) has emerged as a capable architecture for the newly emerging network infrastructure for energy efficient networks and high data rate enablement. It is considered as the main technology. Better service quality has been achieved by developing small cells into macro cells through this type of network. In addition, the reuse of radio resources is much better than that of homogeneous networks. In the present paper, we propose the H-CRAN energy-efficient methods. This energy-efficient algorithm incorporates an energy efficient resource allocation management design to deal to heterogeneous cloud radio access networks in 5G. System throughput fulfillment is elevating by incorporating an efficient resource allocation design by the energy consumption model. The simulation results have been demonstrated by comparing the efficiency of the introduced design with the existing related design.

Gaussian mixture model based radio environment mapping for an underlay Device‐to‐Device network

AbstractEfficient management of the scant radio resources in densely deployed Device‐to‐Device (D2D) and IoT networks in 5G+ and 6G wireless era would require continuous and reliable information feedback regarding the actual on‐ground resource utilization. Toward that goal, radio environment maps (REM) are becoming increasingly important as they provide critical information relevant for network management. REM essentially entails the estimation of the radio environment at unsampled locations using the values measured by a few dedicated REM sensors by interpolation. Hence, the accuracy of REM is dependent upon two major factors (1) the sensor deployment and (2) the interpolation scheme. So far, the ordinary Kriging (OK) with randomly deployed sensors is the most prevalent method of REM creation. However, the underlying assumption in OK is that the radio environment conforms to a Gaussian spatial random field (GSRF). In reality, the spatial random field generated by a D2D network displays non‐Gaussian characteristics. Hence, a variational Bayesian based Gaussian mixture model enabled Kriging (VK) method has been proposed in this work which achieves better interpolation accuracy than OK with a lower run‐time computational complexity. A comparison of various sensor deployment schemes on the basis of REM accuracy in fading channel conditions has also been conducted. Additionally, a spatial‐correlation based method has been proposed to estimate the minimum number of required REM sensors to ensure reliable REM performance. The simulation results indicate a superiority of hexagonal deployment of sensors over other schemes and establish VK as a more accurate interpolation algorithm than OK.

A Tutorial on the Characterisation and Modelling of Low Layer Functional Splits for Flexible Radio Access Networks in 5G and Beyond

A Tutorial on the Characterisation and Modelling of Low Layer Functional Splits for Flexible Radio Access Networks in 5G and Beyond

Enhancing Energy Sustainability in Two-Tier Heterogeneous Cellular Networks in 5G through Idle Mode Capabilities

Enhancing Energy Sustainability in Two-Tier Heterogeneous Cellular Networks in 5G through Idle Mode Capabilities

A hybrid dynamic bandwidth allocation scheme operating with IACG and cooperative DBA for converged fronthaul networks

The time-division multiplexed passive optical network is seen as a candidate for converged fronthaul networks in 5G, which requires low latency and high capacity. In this paper, a hybrid dynamic bandwidth allocation (DBA) scheme for converged fronthaul that combines a status reporting DBA and a cooperative DBA is proposed. Different deployment scenarios and operating conditions in a 10-Gigabit-capable PON (XGS-PON) system are investigated using the OMNeT++ network simulator. The performance of the proposed hybrid DBA is evaluated in terms of average upstream delay and percentage of frame loss meeting a 140 µs queuing delay requirement for fronthaul traffic. Simulation results show a zero-frame loss for fronthaul traffic in all scenarios using the proposed hybrid DBA which is not achievable using only the status reporting DBA, confirming the advantages of the proposed hybrid DBA.

Efficient resource allocation for hybrid nonorthogonal multiple access based heterogeneous networks beyond fifth‐generation

AbstractFifth‐generation (5G) and future beyond fifth‐generation (B5G) networks will require immense capacity due to the high rise in the number of multimedia applications and mobile devices. The heterogeneous networks (HetNets) in 5G and B5G can increase the heterogeneity and network throughput because macro base station‐only (MBS‐only) networks cannot satisfy the drastic increase in capacity demands. Hybrid nonorthogonal multiple access (H‐NOMA) can accommodate the increasing number of multimedia applications and mobile devices in 5G and B5G. H‐NOMA in B5G can also improve spectral efficiency. Before this, the researchers had not considered user clustering with H‐NOMA in HetNets. This article investigates the user clustering with downlink H‐NOMA in HetNet to maximize the network throughput in 5G and B5G. The formulated mathematical problem optimizes the considered key performance indicators (KPIs), that is, users admission in clusters, users association with base stations, power allocation to users, and network throughput. At the same time, it also satisfies the minimum transmit power and data rate requirements of users. The formulated problem is a mixed‐integer nonlinear programming (MINLP) problem. We have proposed an‐optimal algorithm, that is, outer approximation algorithm (OAA) to solve the MINLP problem because the complexity of the optimal exhaustive search algorithm (ESA) increases exponentially with an increase in the number of users. H‐NOMA with user clustering in HetNet is evaluated with extensive simulations to show its effectiveness regarding network throughput in 5G and B5G. We have also considered the proposed framework in the MBS‐only network and compared its performance with HetNet. The results verify that the proposed framework in HetNet performs better than the MBS‐only network. The complexity of the‐optimal algorithm is calculated that gives‐optimal results within. We have also made a complexity analysis of the proposed OAA and ESA and concluded that the complexity of the proposed OAA algorithm is less than the ESA.

On Sustained Zero Trust Conceptualization Security for Mobile Core Networks in 5G and Beyond

The rapid increase in data traffic is forcing mobile network operators to enhance and expand their network infrastructure to meet the new requirements of customers’ Service Level Agreements (SLA). Network Function Virtualization (NFV) provides abstractions of core network functions from the vendor-specific hardware. This allows the network functions to move around the cloud, providing better performance and scaling capabilities. However, deploying virtualized mobile core network in the cloud environment opens many security concerns not only regarding communication between the Radio Access Network (RAN) and the mobile core network but also within the core network itself. In this paper, we propose a framework called virtual Evolved Packet Core - virtual Software Defined Perimeter (vEPC-vSDP) to provide secure communications within the mobile core network by using an authentication-based approach. The SDP components are virtualized and placed within the virtualized core network to provide a zero-trust environment where only authenticated and authorized core network elements can have access to one another. The analysis of the proposed vEPC-vSDP framework confirms its ability to shield the core network traffic from both external and internal attacks. The vEPC-vSDP framework was implemented and tested against Denial of Service (DoS), Distributed Denial of Service (DDoS) and port scanning attacks to demonstrate the resilience of the proposed framework. The results show the capability of vEPC-vSDP to provide secure communication path to mobile core network elements.

Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry

Device density in cellular networks is expected to increase considerably in the near future. Accordingly, the access point (AP) will be equipped with massive multiple-input multiple-output (mMIMO) antennas, using collimated millimeter-wave (mmW) and sub-THz communications, and increasing the bandwidth to accommodate the growing data rate demands. In this scenario, interference plays a critical role and, if not characterized and mitigated properly, might limit the performances of the network. In this context, this paper derives the statistical properties of the aggregated interference power for a cellular network equipping a mMIMO cylindrical array. The proposed statistical model considers the link blockage and other network parameters such as antenna configuration and device density. The findings show that the characteristic function (CF) of the aggregated interference power can be regarded as a weighted mixture of two alpha-stable distributions. Furthermore, by analyzing the service probability, it is found that there is an optimal configuration of the array depending on the AP height and device density. The proposed statistical model can be part of the design of dense networks providing valuable insights for optimal network deployment and resource management and scheduling.

Deep learning method for delay minimization in MANET

A transmission delay is a critical metric when dealing with ad hoc networks in 5G, particularly for real-time applications and multimedia. In this paper, we describe the challenge of managing mobile ad-hoc networks (MANET) based on multi-hop time-slotted time-division multiple access (TDMA) under routing delay minimization with heterogeneous traffic flows. In particular, we consider the challenge of request scheduling and power control in TDMA, for minimizing the overall weighted end-to-end packet delay when the weights are determined according to the priorities of the requests. A delay minimization network that uses deep learning is also introduced ( DMNet ). Simulations show that DMNet outperforms other state-of-art methods. Our approach is one of the first to utilize a DNN to solve end-to-end delay minimization through scheduling and power control.

Satellite-Based Non-Terrestrial Networks in 5G: Insights and Challenges

Non-terrestrial networks (NTNs) will become an indispensable part of future wireless networks. Integration with terrestrial networks will provide new opportunities for both satellite and terrestrial telecommunication industries and therefore there is a need to harmonize them in a unified technological framework. Among different NTNs, low earth orbit (LEO) satellites have gained increasing attention in recent years and several companies have filed federal communication commission (FCC) proposals to deploy their LEO constellation in space. This is mainly due to several desired features such as large capacity and low latency. In addition, recent successful LEO network deployments such as Starlink have motivated other companies. In the past satellite and terrestrial wireless networks have been evolving separately but now they are joining forces to enhance coverage and connectivity experience in the future wireless networks. The 3rd Generation Partnership Project (3GPP) is one of the dominating standardization bodies that is working on various technical aspects to provide ubiquitous access to the 5G networks with the aid of NTNs. Initial steps have been taken to adopt 5G state of the art technologies and concepts and harmonized them with the conditions met in non-terrestrial networks. In this article, we review some of the important technical considerations in 5G NTNs with emphasis on the radio access network (RAN) part and provide some simulation based results to assess the required modifications and shed light on the design considerations.

COCO: Coherent Consensus Schema For Dynamic Spectrum Allocation For 5G

Numerous wireless technologies have been integrated to provide 5th generation (5G) communication networks capable of delivering mission‐critical applications and services. Despite considerable developments in a variety of supporting technologies, next‐generation cellular deployments may still face severe bandwidth constraints as a result of inefficient radio spectrum use. To this end, a variety of appropriate frameworks have recently emerged that all aid mobile network operators (MNOs) in making effective use of the abundant frequency bands that other incumbents reserve for their own use. The proposed COCO model for Dynamic Spectrum Allocation (DSA) has 2 functionalities such as 1. Coherent PU‐SU packet acceptance algorithm for Secondary User (SU) in DSA. 2. Consensus Algorithm for PU‐SU Channel Reservation in DSA. To enable a 5G service with one‐millisecond latency, interconnection ports between operators are expected to be required at every base station, which would have a significant influence on the topological structure of the core network. Additionally, just one radio network infrastructure would need to be created, which all operators would then be able to use. We allow change of PU SU characteristics to satisfy the needs of new services. These modifications are accomplished via the use of Coherent and Consensus Algorithms that regulate PU and SU through negotiation and allocation procedures. Our primary objective was to decrease interference, handoff latency, and the chance of blocking. In this paper, we describe our idea for employing COCO Model to address the issues of spectrum mobility, sharing, and handoff for Cognitive Radio Networks in 5G.

Dynamic-Adaptive AI Solutions for Network Slicing Management in Satellite-Integrated B5G Systems

The integrated terrestrial and non-terrestrial networks in 5G and beyond 5G are envisioned to support dynamic, seamless, and differentiated services for emerging use cases with stringent requirements. Such service heterogeneity and rapid growth in network complexity pose difficulties in network management and resource orchestration. Network slicing paves the way for delivering highly customized services and enabling service-oriented resource allocation. In this context, artificial intelligence (AI) becomes a key enabler for network slicing management. However, AI-based approaches encounter critical challenges in adapting to dynamic and complex wireless environments. In this article, first, we aim to provide a comprehensive understanding of these challenges, open issues, and future research opportunities. Second, we highlight the investigations on dynamic-adaptive AI solutions for dealing with the effect of concept drift. Third, we identify typical dynamic scenarios in case studies and provide numerical results to illustrate the effectiveness of the discussed AI solutions.

A survey on sleep mode techniques for ultra-dense networks in 5G and beyond

The proliferation of mobile users with an attendant rise in energy consumption mainly at the base station has requested new ways of achieving energy efficiency in cellular networks. Many approaches have been proposed to reduce the power consumption at the base stations in response to the contribution of energy cost to the increase of OPEX of the mobile operators and the rise of the carbon footprint on global climate. As a springboard to the application of sleep mode methods in ultra-dense cellular networks, this paper provides a comprehensive survey of the base station sleep mode strategies in heterogeneous mobile networks from perspectives of modeling and algorithm design. Specifically, the sleep mode enabling strategies and sleep wake-up schemes are reviewed. The base station sleep-mode techniques in ultra-dense networks are further discussed as well as the challenges and possible solutions.

Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff

The sensitivity to blockages is a key challenge for millimeter wave and terahertz networks in 5G and beyond. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction allows the network to anticipate future blockages, especially dynamic blockages, and initiate user hand-off beforehand. This article presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by red-green-blue (RGB) cameras deployed at the base stations. In particular, the article proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. This is mainly achieved via a deep learning algorithm that learns from visual and wireless data how to predict incoming blockages. The predictions of this algorithm are used by the wireless network to proactively initiate hand-off decisions and avoid any unnecessary latency. The algorithm is developed on a vision-wireless dataset generated using the ViWi data-generation framework. Experimental results on two basestations with different cameras indicate that the algorithm is capable of accurately detecting incoming blockages more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\sim} 90\%$</tex-math></inline-formula> of the time. Such blockage prediction ability is directly reflected in the accuracy of proactive hand-off, which also approaches 87%. This highlights a promising direction for enabling high reliability and low latency in future wireless networks.

A Vision of Self-Evolving Network Management for Future Intelligent Vertical HetNet

Future integrated terrestrial-aerial-satellite networks will have to exhibit some unprecedented characteristics for the provision of both communications and computation services, and security for a tremendous number of devices with very broad and demanding requirements across multiple networks, operators, and ecosystems. Although 3GPP introduced the concept of self-organizing networks (SONs) in 4G and 5G documents to automate network management, even this progressive concept will face several challenges as it may not be sufficiently agile in coping with the immense levels of complexity, heterogeneity, and mobility in the envisioned beyond-5G integrated networks. In the presented vision, we discuss how future integrated networks can be intelligently and autonomously managed to efficiently utilize resources, reduce operational costs, and achieve the targeted Quality of Experience (QoE). We introduce the novel concept of the “self-evolving networks (SENs)” framework, which utilizes artificial intelligence, enabled by machine learning (ML) algorithms, to make future integrated networks fully automated and intelligently evolve with respect to the provision, adaptation, optimization, and management aspects of networking, communications, computation, and infrastructure nodes' mobility. To envisage the concept of SEN in future integrated networks, we use the Intelligent Vertical Heterogeneous Network (I-VHetNet) architecture as our reference. The article discusses five prominent scenarios where SEN plays the main role in providing automated network management. Numerical results provide an insight into how the SEN framework improves the performance of future integrated networks. The article presents the leading enablers and examines the challenges associated with the application of the SEN concept in future integrated networks.

An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond

The Internet of Things grew rapidly, and many services, applications, sensor-embedded electronic devices, and related protocols were created and are still being developed. The Internet of Things (IoT) allows physically existing things to see, hear, think, and perform a significant task by allowing them to interact with one another and exchange valuable knowledge when making decisions and caring out their vital tasks. The fifth-generation (5G) communications require that the Internet of Things (IoT) is aided greatly by wireless sensor networks, which serve as a permanent layer for it. A wireless sensor network comprises a collection of sensor nodes to monitor and transmit data to the destination known as the sink. The sink (or base station) is the endpoint of data transmission in every round. The major concerns of IoT-based WSNs are improving the network lifetime and energy efficiency. In the proposed system, Optimal Cluster-Based Routing (Optimal-CBR), the energy efficiency, and network lifetime are improved using a hierarchical routing approach for applications on the IoT in the 5G environment and beyond. The Optimal-CBR protocol uses the k-means algorithm for clustering the nodes and the multihop approach for chain routing. The clustering phase is invoked until two-thirds of the nodes are dead and then the chaining phase is invoked for the rest of the data transmission. The nodes are clustered using the basic k-means algorithm during the cluster phase and the highest energy of the node nearest to the centroid is selected as the cluster head (CH). The CH collects the packets from its members and forwards them to the base station (BS). During the chaining phase, since two-thirds of the nodes are dead and the residual energy is insufficient for clustering, the remaining nodes perform multihop routing to create chaining until the data are transmitted to the BS. This enriches the energy efficiency and the network lifespan, as found in both the theoretical and simulation analyses.

Blockchain-Enabled 5G Edge Networks and Beyond: An Intelligent Cross-Silo Federated Learning Approach

Edge networks (ENs) in 5G have the capability to protect traffic between edge entry points (edge-to-edge), enabling the design of various flexible and customizable applications. The advantage of edge networks is their pioneering integration of other prominent technologies such as blockchain and federated learning (FL) to produce better services on wireless networks. In this paper, we propose an intelligent system integrating blockchain technologies, 5G ENs, and FL to create an efficient and secure framework for transactions. FL enables user equipment (UE) to train the artificial intelligence model without exposing the UE’s valuable data to the public, or to the model providers. Furthermore, the blockchain is an immutable data approach that can be leveraged for FL across 5G ENs and beyond. The recorded transactions cannot be altered maliciously, and they remain unchanged by design. We further propose a dynamic authentication protocol for UE to interact with a diverse base station. We apply blockchain as a reward mechanism in FL to enable computational offloading in wireless networks. Additionally, we implement and investigate blockchain technology for FL in 5G UE.

A lightweight authentication scheme for 5G mobile communications: a dynamic key approach

The security of modern IoT Industry 4.0, 5G, 6G, Mobile ad hoc (MANET), narrowband internet of things (NB-IoT) and wireless sensory (WSN) networks and the autonomous computing capabilities of individual devices and self-organizing, greatly influence their applications in smart connected world. To achieve the sufficient security and privacy, autonomous and dynamic adaptive key management scheme and mutual authentication protocols should be provided to validate legitimacies of large number of nodes, users, devices, and servers in 5G networks. To this end, we propose a lightweight cipher approach that enhances the security level of communications and enables authentication and access control scheme that is implemented on a multi-layer communication architecture designed for NB-IoT 5G networks and resolve heterogeneity and scalability issues. It is well known that establishing an efficient cryptographic key generation and management scheme for machine-to-machine (M2M) and device-to-device (D2D) communication for IoT networks in 5G, the purposes of encryption and decryption is severely constrained by computation resources and performance. As a part of the network security, the key management plays a vital role in it. When compared to other schemes in PKI such as public key, pairwise-key and group-based key management, the dynamic-key based security is efficient in resource utilization and scalable which are the two key performance factors for Cloud based IoT/wireless sensory networks. Usually most IoT networks use a less secure communication channel that employs session keys in order to encrypt the communication. Additionally, in most IoT networks the resource consumption is constrained which then results in existing dynamic key generation algorithms being infeasible to run. It is still a challenging problem to securely interconnect and operate IoT protocols between constrained embedded devices. We propose a novel dynamic key generation scheme that takes the entropy and performs various operations to continuously generate a large set of unique keys. This type of key generation model is predominantly suitable for the conditions where the IoT devices cannot rely on constant key negotiation with dedicated servers as well as those devices which cannot reuse existing keys for encryption purposes. Performance evaluation using simulation and case study demonstrates that our dynamic key establishment scheme ensures an enhanced security level while reducing the communication overhead and the average latency for different category of IoT applications in 5G networks.