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Summary: Since 2017, 3rd Generation Partnership Program (3GPP) is working on integration of satellite communication in 3GPP. This paper discusses the results of the work on service and network architecture aspects in 3GPP Technical Specification Group (TSG) Service and System Aspects (SA) and 3GPP TSG Core Networks and Terminals (CT). This paper does not discuss radio aspects as are specified in 3GPP TSG radio access network (RAN). 3GPP first specified requirements and use cases for satellite access. Subsequently, architecture and protocol specifications were created for integration scenarios, direct access, network selection, mobility management, and satellite backhaul with quality of service (QoS). Also, impacts of regulatory aspects on satellite integration have been discussed in 3GPP. With the completion of Release 17, 3GPP specifications are now ready for implementation of integrated 5G satellite networks. 3GPP will continue with further development of satellite integration in Release 18 and Release 19. [ABSTRACT FROM AUTHOR]

Summary: With 3GPP Release‐17, global 5G standards now support non‐terrestrial mobile networks comprising radio access network, terminals, and core network. This enables multi‐vendor interoperability as well as interoperability with 3GPP‐compliant 5G systems. This paper describes the key features enabling the NG‐RAN architecture defined for 5G to support non‐terrestrial networks. Starting from a general overview of NG‐RAN and of the new paradigms of NTN, we introduce the NTN functionality in NG‐RAN specifications with respect to feeder link switchover, cell handling, terminal registration, and OAM aspects. We also discuss different scenarios combining satellite access with 3GPP‐defined core networks. We also describe some further enhancements expected to be seen in the next 3GPP release (Rel‐18). We believe current and upcoming 3GPP work for NTN represents a solid basis on which 5G satellite networks can be built in the upcoming future. [ABSTRACT FROM AUTHOR]

Abstract The evolution of mobile communications towards beyond 5th-generation (B5G) networks is envisaged to incorporate high levels of network automation. Network automation requires the development of a network architecture that accommodates multiple solutions based on artificial intelligence (AI) and machine learning (ML). Consequently, integrating AI into the 5th-generation (5G) systems such that we could leverage the advantages of ML techniques to optimize and improve the networks is one challenging topic for B5G networks. Based on a review of 5G system architecture, the state-of-the-art candidate AI/ML techniques, and the progress of the state of the art, and the on AI/ML for 5G in standards we define an AI architecture and performance evaluation framework for the deployment of the AI/ML solution in B5G networks. The suggested framework proposes three AI architectures alternatives, a centralized, a completely decentralized and an hybrid AI architecture. More specifically, the framework identifies the logical AI functions, determines their mapping to the B5G radio access network architecture and analyses the associated deployment cost factors in terms of compute, communicate and store costs. The framework is evaluated based on a use case scenario for heterogeneous networks where it is shown that the deployment cost profiling is different for the different AI architecture alternatives, and that this cost should be considered for the deployment and selection of the AI/ML solution.

High data rate coverage, security, and energy efficiency will play a key role in the continued performance scaling of next-generation mobile systems. Dense, small mobile cells based on a novel network architecture are part of the answer. Motivated by the recent mounting interest in free-space optical (FSO) technologies, this paper addresses a novel mobile fronthaul network architecture based on FSO, spread spectrum codes, and graphene modulators for the creation of dense small cells. The network uses an energy-efficient graphene modulator to send data bits to be coded with spread codes for achieving higher security before their transmission to remote units via high-speed FSO transmitters. Analytical results show the new fronthaul mobile network can accommodate up to 32 remote antennas under error-free transmissions with forward error correction. Furthermore, the modulator is optimized to provide maximum efficiency in terms of energy consumption per bit. The optimization procedure is carried out by optimizing both the amount of graphene used on the ring resonator and the modulator's design. The optimized graphene modulator is used in the new fronthaul network and requires as low as 4.6 fJ/bit while enabling high-speed performance up to 42.6 GHz and remarkably using one-quarter of graphene only. [ABSTRACT FROM AUTHOR]

This work concentrates on the problem of optimizing the cost of a passive wavelength division multiplexing (WDM) optical network used as a transport network for carrying the xHaul packet traffic between a set of remote radio sites and a central hub in a 5G radio access network (RAN). In this scope, we investigate the flexible use of optical add-drop multiplexers (OADMs) for the aggregation of traffic from a number of remote sites, where the type/capacity of optical devices—OADMs and optical multiplexers (MUXs)—is selected in accordance with the traffic demand. The approach is referred to as Flex-O. To this end, we formulate the xHaul network planning problem consisting in the joint provisioning of transmission paths (TPs) between the remote sites and the hub with optimized selection and placement of OADMs on the paths and proper selection of MUXs at the ends of the TPs. The problem formulation takes into accounts the optical power budget that limits the maximum transmission distance in a function of the amount and type of optical devices installed on the TPs. The network planning problem is modeled and solved as a mixed-integer linear programming (MILP) optimization problem. Several network scenarios are analyzed to evaluate the cost savings from the flexible (optimized) use of OADMs. The scenarios differ in terms of the availability of OADMs and the capacity of the WDM devices applied on the TPs. The numerical experiments performed in three mesh networks of different size show that the cost savings of up to between 35 and 45% can be achieved if the selection of OADMs is optimized comparing to the networks in which either single-type OADMs are used or the OADMs are not applied. [ABSTRACT FROM AUTHOR]

Network Slicing is a key technology of 5G mobile systems. This technology enables operators to create customized logical networks on the same physical substrate in order to support different services and vertical industries, such as public safety and entertainment. In this paper we present NS-ENFORCER, a dynamic slicing mechanism for 4G/5G Radio Access Networks. NS-ENFORCER receives slicing rules from an SDN Controller. Based on these rules and the network state, NS-ENFORCER dynamically assigns spectrum resources to slices, and performs slicing in both downlink and uplink communication channels. Such assignment procedure is supported by a novel allocation model proposed in this work. The performance evaluation of NS-ENFORCER was carried out using a scenario having different types of applications and mobility of devices. The results show the benefits that can be achieved with NS-ENFORCER, such as the improvement of the QoS experienced by end users. [ABSTRACT FROM AUTHOR]

Over the last few years, kernel adaptive filters have gained in importance as the kernel trick started to be used in classic linear adaptive filters in order to address various regression and time-series prediction issues in nonlinear environments. In this paper, we study a recursive method for identifying finite impulse response (FIR) nonlinear systems based on binary-value observation systems. We also apply the kernel trick to the recursive projection (RP) algorithm, yielding a novel recursive algorithm based on a positive definite kernel. For purposes, our approach is compared with the recursive projection (RP) algorithm in the process of identifying the parameters of two channels, with the first of them being a frequency-selective fading channel, called a broadband radio access network (BRAN B) channel, and the other being a a theoretical frequency-selective channel, known as the Macchi channel. Monte Carlo simulation results are presented to show the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article presents a study with feasibility and performance analysis of machine learning (ML) techniques using supervised techniques for anomaly detection problems in a 5G communication network. The proposed ML models (Multilayer Perceptron, Decision Tree, and Support Vector Machine) were used to classify data into anomaly or non-anomaly based on two 5G Open Radio Access Network (O-RAN) datasets with various key performance indicators (KPIs). Furthermore, we propose a strategy that devotes to labeling anomalous situations, leveraging the t-Distributed Stochastic Neighbor Embedding (tSNE) technique atop datasets enclosing multiple KPIs. The results were significant, with an accuracy above 90% for all use cases considered. [ABSTRACT FROM AUTHOR]

Packet-switched xHaul networks are a scalable solution enabling convergent transport of diverse types of radio data flows, such as fronthaul / midhaul / backhaul (FH / MH / BH) flows, between remote sites and a central site (hub) in 5G radio access networks (RANs). Such networks can be realized using the cost-efficient Ethernet technology, which enhanced with time-sensitive networking (TSN) features allows for prioritized transmission of latency-sensitive fronthaul flows. Provisioning of multiple types of 5G services of different service requirements in a shared network, commonly referred to as network slicing, requires adequate handling of transported data flows in order to satisfy particular service / slice requirements. In this work, we investigate two traffic prioritization policies, namely, flowaware (FA) and latency-aware (LA), in a packet-switched xHaul network supporting slices of different latency requirements. We evaluate the effectiveness of the policies in a networkplanning case study, where virtualized radio processing resources allocated at the processing pool (PP) facilities, for two slices related to enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services, are subject to optimization. Using numerical experiments, we analyze PP cost savings from applying the LA policy (vs. FA) in various network scenarios. The savings in active PPs reach up to 40% - 60% in ring scenarios and 30% in a mesh network, whereas the gains in overall PP cost are up to 20% for the cost values assumed in the analysis. [ABSTRACT FROM AUTHOR]

The 3rd generation partnership project (3GPP) standards organizations makes great efforts in order to reduce the latency of 5G mobile networks to the least possible extent. Recently, these networks are associated with big buffers to maximize the network utilization and minimize the wasted wireless resources. However, in existence of the TCP congestions, having bottlenecks are still expected on radio access networks (RANs) data paths. Consequently, this influences the network performance and reduces its quality of services (QoSs). Apparently, studying and improving the behavior of buffers deployed at 5G mobile networks devices can contribute to solving these problems (at least by reducing the queuing time at these buffers). In this paper, we study the buffer behavior of base stations in a 5G mobile network at steady state. We consider a cellular mobile network consisting of finite number of users (stations, terminals, and mobiles). At any time-slot, a station may be using the channel (busy) or not using the channel (idle). Since system analysis of cellular mobile networks in general form is rather complex, solutions are always obtained in closed forms or by numerical techniques. A two-dimensional traffic system for cellular mobile networks is presented, and the main performance evaluations are derived. Moreover, different moments of the base station buffer occupancy are calculated. The study reveals that there is a correlation between the state of the mobile stations (busy or idle) and the expected buffers occupancy of the base station. In addition, the results discussions demonstrate some important factors and parameters that affect the base station buffers and the overall network performance. These factors can be further worked on and controlled to obtain the least possible latency in next generation mobile networks. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. [ABSTRACT FROM AUTHOR]

The fog radio access network (F‐RAN) has been regarded as a promising wireless access network architecture in the fifth generation (5G) and beyond systems to satisfy the increasing requirements for low‐latency and high‐throughput services by providing fog computing. However, because the cloud computing centre and fog computing‐enabled access points (F‐APs) in the F‐RAN have different computation and communication capabilities, it is crucial to make an efficient computation offloading and resource allocation strategy that can fully exploit the potential of the F‐RAN system. In this paper, the authors investigate a decentralized low‐complexity deep reinforcement learning (DRL)‐based framework for joint computation task offloading and resource allocation in the F‐RAN, which supports assistive computing‐enabled tasks offloading between F‐APs. Considering the constraints of task latency, wireless transmission rate, transmission power, and computational resource capacity, the authors formulate the system processing efficiency maximization problem by jointly optimizing offloading mode selection, channel allocation, power control, and computation resource allocation in the F‐RAN. To solve this non‐linear and non‐convex problem, the authors propose a federated DRL‐based computation offloading and resource allocation algorithm to improve the task processing efficiency and ensure privacy in the system, which can significantly reduce the computing complexity and signalling overhead of the training process compared with the centralized learning‐based method. Specifically, each local F‐AP agent consists of dueling deep Q‐network (DDQN) and deep deterministic policy gradient (DDPG) networks to appropriately deal with discrete and continuous valuable action spaces, respectively. Finally, the simulation results show that the proposed federated DRL algorithm can achieve significant performance improvements in terms of system processing efficiency and task latency compared with other benchmarks. [ABSTRACT FROM AUTHOR]

The advent of Fifth-Generation (5G) networks has led to the enhancement of digital connectivity throughout the world. Device-to-Device (D2D) or sidelink communication is an important feature of 5G networks, allowing network resources to be utilized optimally for network load management. The employment of D2D communication strategies can however allow malicious users perpetrating attacks such as Denial-of-Service (DoS). In particular, the problem is most acute for multi-hop networks. Consequently, the present work initially outlines a DoS attack strategy for multi-hop networks trading resources via a double auction game mechanism. The proposed DoS attack implements a gradual disincentivization strategy for nodes participating in the auction. The paper then illustrates a DoS attack mitigation strategy which forces malicious nodes to ask for reasonable incentives for resource-sharing, preventing the disincentivization of non-malicious nodes and maximizing social welfare. The mathematical results presented are corroborated by the simulation results obtained in the work. Finally, experiments are performed with mobile devices regulated by a centralized Open Radio Access Network (O-RAN) controller, and the results obtained match the simulation results to an agreeable extent. [ABSTRACT FROM AUTHOR]

Several assessing efforts are approved making both communication and information technologies available. In addition to being a part of universal access and service, we aim at assessing the impact of radio access technologies on universal access indicators. The proposed work demonstrates the possibility of inter-working of all existing Radio Access Technologies (RATs), in a limited area presented in the Six Generation Radio Resources Allocation (6 G-A) network. We propose a solution for the Vertical hand Over (VHO) in 6 G-A heterogeneous system, that adopts Media Independent Handover (MIH) protocol to benefit between different used technologies in a direct communication firstly and facilitate, in addition, coordination between congestion control mechanisms with the mobility management entity. The mathematical model has been developed to deal with locality coverage and broadband needs, based on a methodological approach consisting in integrating parameters linked to access index through radio technologies. This approach has been applied. In practice, it has contributed to highlighting its relative simplicity of implementation, but it is not enough for six-generation system radio access networks. The congestion control mechanism is integrated within the vertical handover process, this proposition is used to prevent each presented congestion state. The media independent handover IS equipment, as well as six G-A equipment which are both considered as the whole architecture key equipment, perform and guaranty this related process. Efficient communication is established besides information exchange between various technologies. Achieved evaluation results are performant and prove that proposed mechanisms are efficient. Both simulation and tests results are accomplished. [ABSTRACT FROM AUTHOR]

We experimentally demonstrate a quantum-measurement-based receiver for a range of modulation schemes and alphabet lengths in a telecom C-band. We attain symbol error rates below the shot noise limit for all the studied modulation schemes and the alphabet lengths 4 ≤ M ≤ 16. In doing so, we achieve the record energy sensitivity for telecom receivers. We investigate the trade-off between energy and bandwidth use and its dependence on the alphabet length. We identify the combined (energy and bandwidth) resource efficiency as a figure of merit and experimentally confirm that the quantum-inspired hybrid frequency/phase encoding has the highest combined resource efficiency. [ABSTRACT FROM AUTHOR]

The advent of the 5th generation of mobile networks brought a large number of new use case and applications to be supported by the physical layer (PHY), which must be more flexible than all previous radio access networks (RAN). The concept of the centralized RAN (C-RAN) allows all the baseband processing to be performed in the central office, simplifying the network deployment and also allowing the operators to dynamically control the PHY according with the applications requirements. The radiofrequency (RF) signal generated by the C-RAN can be transported to the remote radio unit (RRU) by using a radio over fiber (RoF) system. In this paper, we propose two RoF approaches for composing the transport and access networks of the next-generation systems. The first investigation relies on the implementation of a machine learning-based digital pre-distortion (DPD), designed for RoF systems. In the second approach, we implement an RoF system and characterize the optical and electrical power levels aiming to reduce the RoF non-linear distortions. The overall link performance is evaluated by measuring the error vector magnitude (EVMRMS) and 590 Mbit/s is achieved with EVMRMS as low as 4.4% in a 10 m reach cell. [ABSTRACT FROM AUTHOR]

Multicast/broadcast delivery is a critical challenge of future 6G mobile networks where massive Internet of Things (IoT) deployment and extended reality multimedia such as teleportation are target application scenarios. Non-terrestrial networks (NTNs) are considered essential for the success of 6G, which aims to provide true “global” services by extending mobile access worldwide, thus overcoming the coverage limit of current terrestrial networks (TNs). This article discusses how the main distinguishing features of NTNs can be effectively exploited for 6G multicasting. Furthermore, in line with the evolution of future 6G networks toward softwarized systems, we evaluate the potential of using the softwarization paradigm in the heterogeneous TN–NTN architecture to deliver multicast services. [ABSTRACT FROM AUTHOR]

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Fifth-generation (5G) New Radio (NR) systems are expected to support multiple traffic classes including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) at the same air interface. This functionality is assumed to be implemented by utilizing the network slicing concept. According to the 3rd Generation Partnership Project (3GPP), the efficient support of this feature requires statistical multiplexing and, at the same time, traffic isolation between slices. In this paper, we formulate and solve a mathematical model for a class of Radio Access Network (RAN) slicing algorithms that simultaneously include resource reservation and a priority-based service discipline allowing us to incur fine granularity in the service processes of different traffic aggregates. The system is based on a queueing model and allows parametrization by accounting for the specifics of wireless channel impairments. As metrics of interest, we utilize K-class session drop probability, K-class session pre-emption probability, and system resource utilization. To showcase the capabilities of the model, we also compare performance guarantees provided for URLLC, eMBB, and mMTC traffic when multiplexed over the same NR radio interface. Our results demonstrate that the performance trade-off is dictated by the offered traffic load of the highest priority sessions: (i) when it is small, mixed reservation/priority scheme outperforms the full reservation mechanism; (ii) for overloaded conditions, full reservations provides better traffic isolation. The mixed strategy is beneficial to traffic aggregates with short-lived lightweight sessions, such as URLLC and mMTC, while the reservation only scheme works better for elastic eMBB traffic. The most important feature is that the mixed strategy allows resource utilization to be improved up to 95%, which is 10–15% higher compared to the reservation-only scheme while still providing isolation between traffic types. [ABSTRACT FROM AUTHOR]

Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate the inherent inter-cell interference (ICI) of cellular networks. In such networks, several distributed access points (APs) together with precoding/detection processing can serve many users while sharing the same time-frequency resources. Each AP can be equipped with single or multiple antennas, and hence, can provide a consistently adequate service to all users regardless of their locations in the network. This paper presents a detailed overview of the current state-of-the-art on CF systems. First, it performs a literature review of the conventional CF and scalable user-centric (UC) CF mMIMO systems in terms of the limited capacity of the fronthaul links and the connection between APs and user equipments (UEs). As beyond networks will rely on higher frequency bands, it is of paramount importance to discuss the impact of beamforming techniques that are being investigated. Finally, some of the CF promising enabling technologies are presented to emphasize the main applications in these networks. [ABSTRACT FROM AUTHOR]

A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in the real network, as well as for predicting the performance of such algorithms under different conditions. While an NDT can be defined for the different subsystems of the network, this paper proposes an NDT architecture focusing on the Radio Access Network (RAN), describing the components to represent and model the operation of the different RAN elements, and to perform emulations. Different application use cases are identified, and among them, the paper puts the focus on the training of Reinforcement Learning (RL) solutions for the RAN. For this use case, the paper introduces a framework aligned with O-RAN specifications and discusses the functionalities needed to integrate the NDT. This use case is illustrated with the description of a RAN NDT implementation used for training an RL-based capacity-sharing solution for network slicing. Presented results demonstrate that the implemented RAN NDT is a suitable platform to successfully train the RL solution, achieving service-level agreement satisfaction values above 85%. [ABSTRACT FROM AUTHOR]

This study presents the architectural design and implementation of a multi-RAT gateway (MRGW) supporting dual satellite and terrestrial connectivity that enables moving maritime vessels, such as autonomous surface ships, to be connected to multiple radio access networks in the maritime communication environment. We developed an MRGW combining LTE and very-small-aperture terminal (VSAT) access networks to realize access traffic steering, switching, and splitting functionalities between them. In addition, we developed communication interfaces between the MRGW and end-devices connecting to their corresponding radio access networks, as well as between the MRGW and the digital bridge system of an autonomous surface ship, enabling the MRGW to collect wireless channel information from each RAT end-device and provide the collected data to the digital bridge system to determine the optimal navigation route for the autonomous surface ship. Experiments on the MRGW with LTE and VSAT end-devices are conducted at sea near Ulsan city and the Kumsan satellite service center in Korea. Through validation experiments on a real maritime communication testbed, we demonstrate the feasibility of future maritime communication technologies capable of providing the minimum performance necessary for autonomous surface ships or digitized aids to navigation (A to N) systems. [ABSTRACT FROM AUTHOR]

Fixed wireless access (FWA) provides a solution to compete with fiber deployment while offering reduced costs by using the mmWave bands, including the unlicensed 60 GHz one. This paper evaluates the deployment of FWA networks in the 60 GHz band in realistic urban and rural environment in Belgium. We developed a network planning tool that includes novel backhaul based on the IEEE 802.11ay standard with multi-objective capabilities to maximise the user coverage, providing at least 1 Gbps of bit rate while minimising the required network infrastructure. We evaluate diverse serving node locations, called edge nodes (EN), and the impact of environmental factors such as rain and vegetation on the network design. Extensive simulation results show that defining a proper EN's location is essential to achieve viable user coverage higher than 95%, particularly in urban scenarios where street canyons affect propagation. Rural scenarios require nearly 75 ENs per km 2 while urban scenarios require four times (300 ENs per km 2 ) this infrastructure. Finally, vegetation can reduce the coverage by 3% or increment infrastructure up to 7%, while heavy rain can reduce coverage by 5% or increment infrastructure by 15%, depending on the node deployment strategy implemented. [ABSTRACT FROM AUTHOR]

In this study, two dynamic algorithms are verified for transmit antenna selection aimed at achieving the energy efficiency in massive MIMO, namely: Random User Selection (RUS) and Maximum Channel Gain (MCG). Massive MIMO setups are investigated over N radio cables via centralized massive base station (BS), which uses RUS and MCG to provide management to U clients. Massive MIMO networks are shown for 8×8, 16×16, 25×25, and 50×50 setups using RUS and MCG. The proficiency of the proposed scheme on setup network has been investigated with RUS and MCG, and then the effect on performance characteristics, such as sumrate, user throughput and energy efficiency has been analyzed. The power efficiency has also been improved by increasing the number of Massive MIMO antennas. Compared to RUS, algorithm MCG provides a better service to users located far from the base station antenna. [ABSTRACT FROM AUTHOR]

The roll-outs of fifth-generation (5G) New Radio (NR) systems operating in the millimeter-wave (mmWave) frequency band are essential for satisfying IMT-2020 requirements set forth by ITU-R in terms of the data rate at the access interface. To overcome mmWave-specific propagation phenomena, a number of radio access network densification options have been proposed, including a conventional base station (BS) as well as integrated access and backhaul (IAB) with terrestrial and aerial IAB nodes. The aim of this paper is to qualitatively and quantitatively compare the proposed deployments using coverage, spectral efficiency and BS density as the main metrics of interest. To this end, we develop a model capturing the specifics of various deployment options. Our numerical results demonstrate that, while the implementation of terrestrial relaying nodes potentially improves coverage and spectral efficiency, aerial relays provide the highest coverage, three times that of a direct link connection, and also significantly reduce the required BS density. The main benefit is provided by the link between the BS and the aerial relay. However, gains are highly dependent on a number of elements in antenna arrays and targeted outage probability. The use of terrestrial relays can be considered a natural trade-off between coverage and the aggregate rate. [ABSTRACT FROM AUTHOR]

With the widespread expansion of telecommunication networks, the increase in the number and complexity of base stations has led to an exponential growth in the volume of alarms. Traditional alarm prediction based on expert experience or rules has posed significant challenges due to the demand for engineers' expertise and workload. It has become imperative to enhance efficiency by employing data-driven approaches for network alarm prognosis. In this paper, a data-driven alarm prediction model is proposed to support the alarm prognosis in base stations. To improve model performance, the proposed approach utilises ensemble deep learning methods to address the heterogeneity and highly imbalanced alarm dataset. The model is trained and validated using a dataset provided by British Telecom (BT) group. The validation results demonstrate that the proposed method achieves a top-5 accuracy of up to 90% in predicting alarms across 170 categories on the validation set. [ABSTRACT FROM AUTHOR]

Low-latency requirements of 5G networks are one of the fundamental needs to support delay-sensitive applications. Uplink scheduling mechanism at wired midhaul of Centralized Radio Access Network (C-RAN) architecture significantly affects the transmission speed and delay performance of the network. Inadequate performance of conventional scheduling algorithms necessitates improvements to attain these requirements. In this paper, load adaptive scheduling with single threshold (LAS-ST) and load adaptive scheduling with multi-threshold (LAS-MT) algorithms on C-RAN architecture are proposed to reduce transmission time and wait time parameters concurrently. The proposed algorithms have been compared with two conventional scheduling algorithms, multi-scheduling domain (MSD) & single scheduling domain (SSD) and applied at fronthaul and midhaul of C-RAN for its comprehensive analysis. MSD and SSD are widely used in Ethernet Passive Optical Network (EPON) architectures. The results confirm that the proposed multi-threshold scheduling algorithm performs best in meeting the objectives of the study compared to the existing scheduling algorithms in terms of transmission time and wait time by adapting to conditions. Further, resource utilization and resource availability are observed for several transmission cycles (TCs). In addition, data load variation and variation in number of available resources is analysed and its effect on transmission time and wait time is studied. [ABSTRACT FROM AUTHOR]

In the realm of the 5 G environment, Radio Access Networks (RANs) are integral components, comprising radio base stations communicating through wireless radio links. However, this communication is susceptible to environmental variations, particularly weather conditions, leading to potential radio link failures that disrupt services. Addressing this, proactive failure prediction and resource allocation adjustments become crucial. Existing approaches neglect the relationship between weather changes and radio communication, lacking a holistic view despite their effectiveness in predicting radio link failures for one day. Therefore, the Dynamic Arithmetic Residual Multiscale attention-based Modulated Convolutional Neural Network (DARMMCNN) is proposed. This innovative model considers radio link data and weather changes as key metrics for predicting link failures. Notably, the proposed approach extends the prediction span to 5 days, surpassing the limitations of existing one-day prediction methods. In this, input data is collected from the Radio Link Failure (RLF) prediction dataset. Then, the distance correlation and noise elimination are used to improve the quality and relevance of the data. Following that, the sooty tern optimization algorithm is used for feature selection, which contributes to link failures. Next, a multiscale residual attention modulated convolutional neural network is applied for RLF prediction, and a dynamic arithmetic optimization algorithm is accomplished to tune the weight parameter of the network. The proposed work obtains 79.03 %, 65.93 %, and 67.51 % of precision, recall, and F1-score, which are better than existing techniques. The analysis shows that the proposed scheme is appropriate for RLF prediction. [ABSTRACT FROM AUTHOR]

• The multicast services, transmitted over multiple paths, will result in more energy consumption overhead than unicast service. When multicast services are deployed for baseband functionality in 5G/B5G radio access network (RAN), this energy consumption overhead is particularly evident. • Multicast service oriented DU-CU deployment, multicast routing, modulation, and spectrum allocation (RMSA) is discussed over elastic optical network (EON) enabled RAN. • A Mixed Integer Linear Programming (MILP) model is presented to minimize total energy consumption for multicast service in EON enabled 5G/B5G RAN. • A Graph Convolutional Network (GCN) enhanced Deep Reinforcement Learning (DRL) algorithm is proposed to solve the MILP model. • Simulation results show that our proposed algorithm reduces energy consumption by about 30% compared with traditional Deep Q-learning Network (DQN) based on Convolutional Neural Network (CNN). The proposed intelligence algorithm reduces the transmission delay by about 20% compared with traditional CNN-DQN. Home-working has become a significant trend since COVID-19, enabling the rapid prosper of multicast services supported by 5th/Beyond 5th Generation Mobile Communication (5G/B5G) technologies, e.g., online conference and live streaming. However, these multicast services will transmit over multiple paths, resulting in more energy consumption overhead than unicast service. When multicast services are deployed for baseband functionality in 5G/B5G radio access network (RAN), this energy consumption overhead is particularly evident. To address this issue, multicast service oriented DU-CU deployment, multicast routing, modulation, and spectrum allocation (RMSA) is discussed over elastic optical network (EON) enabled RAN. Notably, although EON provides a fine-grained and on-demand spectrum allocation, it also brings more complex spectral constraints. To this end, a Mixed Integer Linear Programming (MILP) model is presented to minimize total energy consumption for multicast service in EON enabled 5G/B5G RAN. Moreover, a Graph Convolutional Network (GCN) enhanced Deep Reinforcement Learning (DRL) algorithm is proposed to solve the MILP model. Specifically, GCN improves the efficiency of DRL decision-making by fully extracting network features. Simulation results show that our proposed algorithm reduces energy consumption by about 30 % compared with traditional Deep Q-learning Network (DQN) based on Convolutional Neural Network (CNN). The proposed intelligence algorithm reduces the transmission delay by about 20 % compared with traditional CNN-DQN. [ABSTRACT FROM AUTHOR]

This work presents a Radio-over-Fiber architecture for Cloud Radio Access Networks (CRAN) based on polarization division multiplexing for delivering up-converted high-speed data signals using a single laser-photodetector pair and free of polarization tracking devices. This system enables the allocation of data signals in two different bands defined as low-frequency (5–25 GHz) and high-frequency (55–65 GHz) millimeter waves (mmWave). Polarization multiplexing ensures minimal crosstalk when data signals are photonically up-converted while providing all-optical inter-band switching capabilities. Proof-of-concept experiments have been performed by simultaneously transmitting NRZ and multi-carrier modulated (Discrete Multitone - DMT) data signals over a radio-over-fiber link consisting of 1 km of standard optical fiber and radio transmission in the 7.5 GHz (wired) and 60 GHz (wireless) bands, respectively, with an estimated potential total throughput of at least 7.5 Gbps when considering the transmission of NRZ signals in these mmWave carriers. Results exhibit a BER below the FEC limit of 3.8 × 10−3 in all cases analyzed when the received optical power at the remote radio head (RRH) is above −4 dBm. The reduced number of components and versatility of this implementation provide a cost-effective solution for delivering and switching high-speed data signals in RF fronthaul for future 5G/6G communications. [Display omitted] • A RoF architecture for CRAN based on two cascaded PDM structures is presented • Compact design: polarization-tracking free and single photodetector-laser pair • Alternation between two mm-Wave bands is done via optical polarization switching • Simultaneous multi-data dual-band (7.5 GHz and 60 GHz) operation is demonstrated [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) technology marks a significant milestone in next-generation networks, offering higher data rates and new services. Achieving optimal performance in 5G and beyond 5G (B5G) systems requires addressing key requirements like increased capacity, high efficiency, improved performance, low latency, support for many connections, and quality of service. It is well-known that suboptimal network configuration, hardware impairments, or malfunctioning components can degrade system performance. The physical layer of the radio access network, particularly channel estimation and synchronization, plays a crucial role. Hence, this paper offers an in-depth evaluation of the 5G Physical Downlink Shared Channel (PDSCH), along with its related channel models such as the Clustered Delay Line (CDL) and the Tapped Delay Line (TDL). This work assesses 5G network performance through practical and IA-based channel estimation and synchronization techniques, and anticipates numerologies for B5G networks. Extensive simulations leveraging the Matlab 5G New Radio (NR) toolbox assess standardized channel scenarios in both macro-urban and indoor environments, following configurations set by the 3rd Generation Partnership Project (3GPP). The numerical results offer valuable insights into achieving the maximum achievable throughput across various channel environments, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The throughput comparisons are performed under assumptions of ideal, realistic, and convolutional neural networks (CNN)-based channel estimation with both perfect and realistic synchronization conditions. Importantly, the study pinpoints certain physical layer elements that have a pronounced impact on system performance, providing essential insights for devising effective strategies or refining CNN-based methods for forthcoming mobile B5G networks. [ABSTRACT FROM AUTHOR]

The rapid advancement in the Internet of Things (IoT) and its integration with Artificial Intelligence (AI) techniques are expected to play a crucial role in future Intelligent Transportation Systems (ITS). Additionally, the continuous progress in the industry of autonomous vehicles will accelerate and increase their short adoption in smart cities to allow safe, sustainable and accessible trips for passengers in different public and private means of transportation. In this article, we investigate the adoption of 5G different technologies, mainly, the Software-Defined Networks (SDN) to support the communication requirements of delegation of control of level-2 autonomous vehicles to the Remote-Control Center (RCC) in terms of ultra-low delay and reliability. This delegation occurs upon the detection of a drowsy driver using our proposed deep-learning-based technique deployed at the edge to reduce the level of accidents and road congestion. The deep learning-based model was evaluated and produced higher accuracy, precision and recall when compared to other methods. The role of SDN is to implement network slicing to achieve the Quality of Service (QoS) level required in this emergency case. Decreasing the end-to-end delay required to provide feedback control signals back to the autonomous vehicle is the aim of deploying QoS support available in an SDN-based network. Feedback control signals are sent to remotely activate the stopping system or to switch the vehicle to direct teleoperation mode. The mininet-WiFi emulator is deployed to evaluate the performance of the proposed adaptive SDN framework, which is tailored to emulate radio access networks. Our simulation experiments conducted on realistic vehicular scenarios revealed significant improvement in terms of throughput and average Round-Trip Time (RTT). [ABSTRACT FROM AUTHOR]

A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and information-based network connectivity. This paper proposes a modified Resource Allocation (RA) scheme for effectively handling the RA problem using a learning-based Resource Segmentation (RS) technique. It uses a modified Random Forest Algorithm (RFA) with Signal Interference and Noise Ratio (SINR) and position coordinates to obtain the position coordinates of end-users. Further, it predicts Modulation and Coding Schemes (MCS) for establishing a connection between the end-user device and the Remote Radio Head (RRH). The proposed algorithm depends on the accuracy of positional coordinates for the correctness of the input parameters, such as SINR, based on the position and orientation of the antenna. The simulation analysis renders the efficiency of the proposed technique in terms of throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) emerged as a promising network paradigm that provides computation, storage and networking features within the edge of the pervasive mobile radio access network. This paper jointly considers computation offloading and resource allocation problem in device-to-device (D2D)-assisted and non-orthogonal multiple access (NOMA)-empowered MEC systems, where each mobile device (MD) is allowed to execute its task in one of the three ways, i.e., local computing, MEC offloading or D2D offloading. We invoke orthogonal multiple access (OMA) and NOMA schemes for MDs that select D2D offloading mode, allowing them to assign tasks to their peers using OMA or NOMA. The original problem is formulated as an overall energy consumption minimization problem, which proves to be NP-hard, making it intractable to solve optimally. We start from a simple case, OMA case and transform the original problem into two sub-problems, i.e., resource allocation sub-problem and computation offloading sub-problem and propose two heuristic algorithms to obtain the sub-optimal solutions of both sub-problems. Then, for the MDs selecting D2D offloading mode, we conduct user pairing and apply the NOMA scheme. Finally, simulation results demonstrate the efficiency of the proposed scheme when compared with the related schemes. [ABSTRACT FROM AUTHOR]

5G, 6G, and beyond (xG) technologies aim at delivering emerging services with new requirements and challenges, enabling full and hyper mobile connectivity over the world. These beyond 5G networks are expected to ensure better quality of service, very high data rate, improved network security, high capacity, low latency, and low cost. In order to meet these objectives, a number of key enabler technologies have been proposed including massive multiple input multiple output, small cells, mobile edge computing, software defined network, network function virtualization, heterogeneous networks, network slicing, cloud radio access network, ultra‐dense network, energy efficiency, and spectrum sharing. Although, the potential interest of these technologies in the network, they opened the door to many security concerns and challenges making the network security one of the primary concerns of the future wireless communication networks. In this article, we investigated the recent advancements on the xG security issues resulted by each key enabler technologies. We analyzed how to secure the network while meeting the emerging promising services, users' demands, and service requirements. We also discussed how the security issues raised by these emerging technologies can be mitigated for efficient and secure communication. [ABSTRACT FROM AUTHOR]

The Cloud Radio Access Network (C-RAN) with centralized processing features achieves efficient and unified resource management to meet the quality of service (QoS) requirements, while results in an increment of energy consumption. To reach a tradeoff between energy efficiency and QoS, jointly considering baseband unit (BBU) computing resource, remote radio head (RRH) power, and fronthaul (FH) link capacity optimization for delay-aware traffic is an NP-hard problem. In this paper, we propose a system energy efficiency optimization model jointing multiple resources allocation for C-RAN downlink transmission. The end-to-end delay (De) in the proposed model is formulated by the established user data queue model, which satisfies the strict Lyapunov stability. Then, based on defining an improved Drift-Plus-Penalty function F DPP to transform the proposed original problem into two sub-problems which are BBU service rate allocation and RRH power control problems. The optimal BBU service rate and RRH transmission power of a single slot are obtained through solving a linear equation and applying a convolution neural network (CNN), respectively. Further, we propose an iterative-based optimization algorithm to achieve the optimal resource allocation for each slot. The simulation results show that the proposed optimization algorithm effectively reaches the balance between energy efficiency and QoS, and achieves better energy efficiency compared with the decomposition allocation method based on heuristic algorithm and BBU scheduling based on first-fit-decreasing (FFD) algorithm with lower computational complexity. [ABSTRACT FROM AUTHOR]