5G Radio Access Research Articles

Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond

Leaving the current 4th generation of mobile communications behind, 5G will represent a disruptive paradigm shift integrating 5G Radio Access Networks (RANs), ultra-high-capacity access/metro/core optical networks, and intra-datacentre (DC) network and computational resources into a single converged 5G network infrastructure. The present paper overviews the main achievements obtained in the ALLIANCE project. This project ambitiously aims at architecting a converged 5G-enabled network infrastructure satisfying those needs to effectively realise the envisioned upcoming Digital Society. In particular, we present two networking solutions for 5G and beyond 5G (B5G), such as Software Defined Networking/Network Function Virtualisation (SDN/NFV) on top of an ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure, and the clean-slate Recursive Inter-Network Architecture (RINA) over packet networks, including access, metro, core and DC segments. The common umbrella of all these solutions is the Knowledge-Defined Networking (KDN)-based orchestration layer which, by implementing Artificial Intelligence (AI) techniques, enables an optimal end-to-end service provisioning. Finally, the cross-layer manager of the ALLIANCE architecture includes two novel elements, namely the monitoring element providing network and user data in real time to the KDN, and the blockchain-based trust element in charge of exchanging reliable and confident information with external domains.

Why do we need 6G?

The study and design of 5G seems to have reached its end and 5G communication systems are currently under deployment. In parallel, 5G standardization is as Release 16, which is going to complete the definition and the design guidelines of the 5G radio access network. Because of that, the interest of the scientific and industrial communities has already started focusing on the future 6G communication networks. The preliminary definition of future technology trends towards 2030, given by major standardization bodies, and the flagship 6G projects worldwide have started proposing various visions about what 6G will be. Side by side, various scientific articles, addressing the initial characterisation of 6G, have also been published. However, considering the promises of 5G, can 6G represent a significant technological advancement to justify a so-called new generation? In fact, now, 5G softwarized networks may just imply continuous network software upgrades (as it happens for the Internet) instead of new generations every ten years. This article starts describing the main characteristics that made 5G a breakthrough in telecommunications, also briefly introducing the network virtualisation and computing paradigms that have reformed telecommunications. Next, by providing rigorous definition of the terminology and a survey of the principal 6G visions proposed, the paper tries to establish important motivations and characteristics that can really justify the need for and the novelty of future 6G communication networks.

Toward securing the control plane of 5G mobile networks against DoS threats: Attack scenarios and promising solutions

With the advent of the fifth generation (5G) technology, a plethora of revolutionary applications can now be supported. This tremendous growth will be certainly accompanied by a wider and fast-evolving security threat landscape, especially when the potential of massive devices connectivity will be fully unleashed. In this paper, we outline the security challenges faced by the 5G radio access network (5G-RAN) control plane as a result of the functional and architectural enhancements made at the radio resource control (RRC) protocol layer. We correspondingly analyze the dynamics of the new 5G RRC three-states model for a machine-type traffic pattern under an attack-free situation. Afterwards, we introduce and describe two RRC-based denial of service (DoS) attacks threatening the 5G-RAN resources availability. In the first threat scenario, the attacker maliciously manipulates the timing of state transitions in an attempt to overload the control plane. The second attack can be carried out through faking huge on-demand system information requests to prevent legitimate consumers from access to the cell and cause failed and interrupted 5G mobile services. Through numerical simulations, we measure the potential impact of these attacks on both 5G devices using the collision probability along with the access delay metrics, and on the critical gNB-centralized unit using the signaling overhead and the resource occupancy time metrics. Our observations reveal that both attacks can have disastrous effects on network stability and resiliency. Finally, some promising solutions are listed, with a special emphasis on injecting randomness into system parameters to complicate the task of designing such DoS attacks.

Intelligent multi-branch allocation of spectrum slices for inter-numerology interference minimization

Network slicing and mixed-numerology access schemes cover a central role to enable the flexible multi-service connectivity that characterizes 5G radio access networks (RAN). However, the interference generated by the simultaneous multiplexing of radio slices having heterogeneous subcarrier spacing can hinder the isolation of the different slices sharing the RAN and their effectiveness in meeting the application requirements. To overcome these issues, we design a radio resource allocation scheme that accounts for the inter-numerology interference and maximizes the aggregate network throughput. To overcome the computationally complexity of the optimal formulation, we leverage deep reinforcement learning (DRL) to design an agent capable of approximating the optimal solution exploiting a model-free environment formulation. We propose a multi-branch agent architecture, based on Branching Dueling Q-networks (BDQ), which ensures the agent scalability as the number of spectrum resources and network slices increases. In addition, we augment the agent learning performance by including an action mapping procedure designed to enforce the selection of feasible actions. We compare the agent performance to several benchmarks schemes. Results show that the proposed solution provides a good approximation of the optimal allocation in most scenarios.

Design of hybrid topologies using higher order sectorisation for performance enhancement of 5G radio access network

Design of hybrid topologies using higher order sectorisation for performance enhancement of 5G radio access network

Evaluation of adjacent channel interference from land-earth station in motion to 5G radio access network in the Ka-frequency band

As the fifth-generation (5G) mobile communication service is expected to operate in one of the 11 candidate frequency bands of 24.25–27.5 GHz, compatibility and coexistence with other adjacent wireless applications must be evaluated. In this paper, the adjacent channel interference (ACI) between Land-Earth Station in Motion (L-ESIM), 5G base station (BS), and user equipment (UE) operating in the adjacent frequency bands is assessed. The minimum coupling loss (MCL) method is used by considering the worst-case scenario to evaluate the effect of geostationary orbit-fixed satellite service's (GSO-FSS) frequency band of 27.5–29.5 GHz on the 5G radio access network (BS and UE) from L-ESIM. From the numerical simulations, minimum separation distances of 35 km and 12 km were recorded for the BS and UE to meet the maximum acceptable interference of -147 dBW/MHz. The obtained results will protect the 5G RAN from harmful interference by ensuring adjacent channel compatibility and coexistence with L-ESIM in their future deployment.

Worst-Case Detection Performance for Distributed SIMO Physical Layer Authentication

Feature-based physical layer authentication (PLA) schemes, using position-specific channel characteristics as identifying features, can provide lightweight protection against impersonation attacks in overhead-limited applications like e.g., mission-critical and low-latency scenarios. However, with PLA-aware attack strategies, an attacker can maximize the probability of successfully impersonating the legitimate devices. In this paper, we provide worst-case detection performance bounds under such strategies for a distributed PLA scheme that is based on the channel-state information (CSI) observed at multiple distributed remote radio-heads. This distributed setup exploits the multiple-channel diversity for enhanced detection performance and mimics distributed antenna architectures considered for 4G and 5G radio access networks. We consider (i) a power manipulation attack, in which a single-antenna attacker adopts optimal transmit power and phase; and (ii) an optimal spatial position attack. Interestingly, our results show that the attacker can achieve close-to-optimal success probability with only statistical CSI, which significantly strengthens the relevance of our results for practical scenarios. Furthermore, our results show that, by distributing antennas to multiple radio-heads, the worst-case missed detection probability can be reduced by 4 orders of magnitude without increasing the total number of antennas, illustrating the superiority of distributed PLA over a co-located antenna setup.

Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation

High-frequency signal oscillators are devices needed for a variety of scientific disciplines. One of their fundamental requirements is low phase noise in the micro- and millimeter wave ranges. The opto-electronic oscillator (OEO) is a good candidate for this, as it is capable of generating a signal with very low phase noise in the micro- and millimeter wave ranges. The OEO consists of an optical resonator with electrical feedback components. The optical components form a delay line, which has the advantage that the phase noise is independent of the oscillator’s frequency. Furthermore, by using a long delay line, the phase noise characteristics of the oscillator are improved. This makes it possible to widen the range of possible OEO applications. In this paper we have reviewed the state of the art for OEOs and micro- and millimeter wave signal generation as well as new developments for OEOs and the use of OEOs in a variety of applications. In addition, a possible implementation of a centralized OEO signal distribution as a local oscillator for a 5G radio access network (RAN) is demonstrated.

High Rate CV-QKD Secured Mobile WDM Fronthaul for Dense 5G Radio Networks

A coherent transmission methodology for a continuous-variable quantum key distribution (CV-QKD) system based on quantum-heterodyne measurement through a coherent intradyne receiver is experimentally demonstrated in the framework of 5G mobile fronthaul links. Continuous optical carrier synchronization is obtained through training information, which is multiplexing to the quantum signal as pilot tone in both, frequency and polarization. Spectral tailoring by means of optical carrier suppression and single-sideband modulation is adopted to simultaneously mitigate crosstalk into the quantum channel and self-interference for the pilot tone, thus allowing for a high signal-to-noise ratio for this training signal. Frequency offset correction and optical phase estimation for the free-running local oscillator of the receiver is accurately performed and guarantees low-noise quantum signal reception at high symbol rates of 250 MHz and 500 MHz with additional Nyquist pulse shaping. A low excess noise in the order of 0.1% to 0.5% of shot-noise units is obtained for fiber-based transmission over a fronthaul link reach of 13.2 km. Moreover, co-existence with 11 carrier-grade classical signals is experimentally investigated. Joint signal transmission in the C-band of both, quantum signal and classical signals, is successfully demonstrated. Secure-key rates of 18 and 10 Mb/s are obtained under strict security assumptions, where Eve has control of the receiver noise, for a dark and a lit fiber link, respectively. Moreover, rates of 85 and 72 Mb/s are resulting for a trusted receiver scenario. These secure-key rates are well addressing the requirements for time-shared CV-QKD system in densified 5G radio access networks with cloud-based processing.

Use cases and testbed solutions for 5G cellular backhauling via satellite

SummaryThis paper describes the use cases and testbed solutions for 5G cellular backhauling via satellite developed within the H2020 5G‐Public Private Partnership (PPP) Phase III project “5G‐VINNI” (5G Verticals Innovation Infrastructure). It presents the end‐to‐end design of the 5G‐VINNI Norway and Luxembourg Facility Sites and aims to showcase the satellite integration into 5G with focus on satellite backhauling solutions. It elaborates on the satellite transport network between the 5G Radio Access Network (RAN) and the 5G Core Network (5GC), where design aspects on the satellite network integration into the standard 3GPP 5GC architecture are detailed. It also addresses the split of the 5GC between the Central Node and the Edge Node, where the Edge Node can be fixed and nomadic. The paper describes also the Management and Orchestration (MANO) and Network Functions Virtualisation Infrastructure (NFVI) features of the 5G‐VINNI Norway and Luxembourg Facility Sites.

Overlaying 5G radio access networks on wavelength division multiplexed optical access networks with carrier distribution.

As 5G communication matures, the requirement for advanced radio access networks (RAN) drives the evolution of optical access networks to support these needs. Basic RAN functions, mobile front-haul to the backbone and interconnected front-end remote radio units, must support and enable data rate surges, low-latency applications, RF coordination, etc. Wavelength division multiplexed optical access networks (WDM-OANs) provide sufficient network capacity to support the addition of RAN services, especially in unused portions of WDM. We propose and demonstrate a method for RAN overlay in WDM-OANs that employ distributed carriers. In such systems, the carrier is modulated at the central office for direct-detected downstream digital data services; later the same carrier is remodulated for the uplink. We propose the use of silicon photonics to intercept the downstream and add 5G signals. We examine the distributed-carrier power budget issues in this overlay scenario. The carrier power must be harvested for direct detection of both digital and RoF services, and yet hold in reserve sufficient power for the uplink remodulation of all services. We concentrate on the silicon photonics subsystem at the remote node to add RoF signals. We demonstrate the overlay with a fabricated chip and study strategic allocations of carrier power at the optical network units housing the radio units to support the overlay. After the successful drop and reception of both conventional WDM-OAN and the newly overlaid RoF signals, we demonstrate sufficient carrier power margin for the upstream remodulation.

Multi-Task Learning for Efficient Management of Beyond 5G Radio Access Network Architectures

Automation of Radio Access Network (RAN) operation is a fundamental feature to manage sustainable and efficient Beyond Fifth-generation wireless (5G) networks, in the context of the Next Generation Self-Organizing Network (NG-SON) vision. Machine Learning (ML) is already identified as the key ingredient of this vision, with new standardized and open architectures, like Open-RAN (O-RAN), taking momentum. In this paper, we propose models based on single-task and Multi-Task Learning (MTL) paradigms to address two RAN use cases, handover management and initial Modulation and Coding Scheme (MCS) selection. Traditional handover schemes have the drawback of taking into account the quality of the signals from the serving, and the target cell, before the handover. Also, initial MCS at the start of the session and after a handover usually is handled conservatively. The proposed ML solutions allow to address these drawbacks by 1) considering the expected Quality of Experience (QoE) resulting from the decision of a target cell to handover, as the driving principle of the handover decision and 2) using the experience extracted from network data to make smarter initial MCS allocations. In this line, we implement a realistic cellular simulation scenario by incorporating coverage holes to build an extensive database to train and test the proposed models. The results show that the ML-based models outperform the 3rd Generation Partnership Project (3GPP) standardized handover and initial MCS selection approaches by improving the QoE of users resulting from a handover and the throughput obtained upon establishing a new connection with a network. Besides that, using the obtained results, this paper extensively discusses the merits of leveraging the MTL model to address different, but related multiple RAN functions because it allows reusing a common learning architecture for multiple RAN use cases, which provides significant implementation advantages.

A Centralized Win‐Win Cooperative Framework for Wi‐Fi and 5G Radio Access Networks

Cooperation to access wireless networks is a key approach towards optimizing the use of finite radio spectrum resources in overcrowded unlicensed bands and to help satisfy the expectations of wireless users in terms of high data rates and low latency. Although solutions that advocate this approach have been widely proposed in the literature, they still do not consider a number of aspects that can improve the performance of the users’ connections, such as the inclusion of (1) cooperation among network operators and (2) users’ quality requirements based on their applications. To fill this gap, in this paper we propose a centralized framework that is aimed at providing a “win‐win” cooperation among Wi‐Fi and cellular networks, which takes into account 5G technologies and users’ requirements in terms of Quality of Service (QoS). Moreover, the framework is supported by smart Radio Access Technology (RAT) selection mechanisms that orchestrate the connection of the clients to the networks. In particular, we discuss details on the design of the proposed framework, the motivation behind its implementation, the main novelties, its feasibility, and the main components. In order to demonstrate the benefits of our solution, we illustrate efficiency results achieved through the simulation of a smart RAT selection algorithm in a realistic scenario, which mimics the proposed “win‐win” cooperation between Wi‐Fi and cellular 5G networks, and we also discuss potential benefits for wireless and mobile network operators.

МОДЕЛИРОВАНИЕ СВЕРХПЛОТНЫХ СЕТЕЙ РАДИОДОСТУПА 5G С ДИАГРАММООБРАЗОВАНИЕМ

In this paper, we investigate the connectivity and probability of successful radio reception in the Poisson model of a 5G ultra-dense radio access network formed by devices with the possibility of location aware beamforming. The difference be-tween the proposed model and the existing prototypes is the complex account for the gains of devices, as well as the influence of radio wave propagation losses, slow and fast fading in the radio channel on the performance of the radio access network. The study is carried out for sessions of directional and omni-directional radio communication, and the beamforming mode is based on the preliminary positioning of neighboring devices. Mathematical modeling of a set of simultaneously operating radio lines in the Poisson model of an ultra-dense 5G radio access network makes it possible to estimate the gain in connectivity and the probability of successful radio reception when forming a beam based on preliminary positioning of devices in comparison with the case of omnidirectional antennas. The results of mathematical modeling of a ultra-dense radio access network with devices equipped with six-element circular antenna arrays showed a significant increase in connectivity indicators and the probability of successful radio reception in comparison with the case of omnidirectional antennas and confirmed the possibility of spatial multiplexing of simultaneous transmissions with a territorial separation of devices on 20% of the range of radio coverage based on the signal-to-noise ratio criterion. The result of the study is the establishment of the dependences of the connectivity and the probability of successful radio reception for an ultra-dense 5G radio access network, built on the basis of devices operating in the adaptive diagramming mode, as well as confirmation of the possibilities of spatial multiplexing of simultaneously operating transceiver devices with their territorial separation by 20% of the range of radio coverage based on the signal-to-interference ratio criterion. The obtained dependences of connectivity and the probability of successful radio reception allows to scientifically substantiate construction of an initial approximation of ultra-dense 5G radio access network from devices with the possibility of beamforming based on positioning, taking into account practical recommendations for their territorial diversity with an ultra-dense distribution.

Evolutionary Algorithms for 5G Multi-Tier Radio Access Network Planning

With the ever-increasing traffic demand of wireless users, resulting from the huge deployment of Internet-of-Things (IoT) devices and the emergence of smart city applications requiring ultra-low latency networks, the Fifth Generation (5G) of cellular networks have been introduced as a revolutionary broadband technology to boost the quality of service of mobile users. In this paper, we investigate the planning process for a 5G radio access network having mmWave Micro Remote Radio Units (mRRUs) on top of sub-6 GHz Macro Remote Radio Units (MRRUs). We rely on proper channel models and link budgets as well as Urban Macro-cells (UMa) and Urban Micro-cells (UMi) characteristics to carefully formulate a 5G network planning optimization problem. We aim to jointly determine the minimum number of MRRUs and mRRUs to install and find their locations in a given geographical area while fulfilling coverage and user traffic demand constraints. In order to solve this planning process, we propose a two-step process where we first employ a low complexity meta-heuristic algorithm to optimize the locations of RRUs followed by an iterative elimination method to remove redundant cells. To evaluate the performances of this proposed approach, we conduct a comparative study using Accelerated Particle Swarm Optimization and Simulated Annealing. Simulations results using sub-6 GHz UMa and 28 GHz UMi demonstrate the ability of the proposed planning approach to achieve more than 98% coverage with minimum cell capacity outage rate, not exceeding the 2%, for different scenarios and illustrate the efficiency of the evolutionary algorithms in solving this NP-hard problem in reasonable running time.

ML-Assisted UE Positioning: Performance Analysis and 5G Architecture Enhancements

Artificial intelligence and data-driven networks will be integral part of 6G systems. In this article, we comprehensively discuss implementation challenges and need for architectural changes in 5G radio access networks for integrating machine learning (ML) solutions. As an example use case, we investigate user equipment (UE) positioning assisted by deep learning (DL) in 5G and beyond networks. As compared to state of the art positioning algorithms used in today's networks, radio signal fingerprinting and machine learning (ML) assisted positioning requires smaller additional feedback overhead; and the positioning estimates are made directly inside the radio access network (RAN), thereby assisting in radio resource management. In this regard, we study ML-assisted positioning methods and evaluate their performance using system level simulations for an outdoor scenario. The study is based on the use of raytracing tool, a 3GPP 5G NR compliant system level simulator and DL framework to estimate positioning accuracy of the UE. We evaluate and compare performance of various DL models and show mean positioning error in the range of 1-1.5 m for a 2-hidden layer DL architecture with appropriate feature-modeling. Building on our performance analysis, we discuss pros and cons of various architectures to implement ML solutions for future networks and draw conclusions on the most suitable architecture.

Slice Merging/Spliting Operations and Tenant Profit Optimization Across 5G Base Stations

Network slicing is a promising technology that can tailor the network to support diverse types of services over a common physical network infrastructure, and tenants can lease the customized slices from network providers to provide customized services for their users. This paper focuses on tenant profit optimization in 5G Radio Access Network (RAN) slicing and analyzes slice resource reservation and reconfiguration operation. A slice cost model is proposed, in which a base cost is included to compensate reconfiguration management overhead for the network providers. Increasing the accuracy of tenant resource requirement prediction can decrease tenant cost while guaranteeing QoS of its users. Further, we analyze the price strategy based on user traffic, and propose several alternative price scheme. In slicing across multiple Base Stations (BSs) scenario, we study two slice management operations, called slice mergence/split, similar but not identical to the breathing behavior of conventional cellular BS management. The tenant profit optimization problem across adjacent BSs is formulated, which can be used in slice mergence/splilt decision making. Taking VR (Virtual Reality) application as an example, simulation in allusion to the typical user arrival process is performed. The simulation indicates how these two operations can increase tenant profits.

Evaluation of Interference in Ultra-Dense 5G Radio Access Networks with Beamforming

In this paper, we investigate the dependence of the level of intersystem interference
 on the beam width of the adaptively formed antenna radiation pattern and the territorial separation of neighboring devices in ultra-dense 5G radio access networks. The results of simulation modeling of a radio access network based on 19 base stations with the parameterization of the antenna array gain by the width of the radiation pattern in the horizontal plane show that when the base station beam is di-rected to the user device and narrowed from 360° to 5°, the level of intrasystem interference decreases by 15 dB compared with the case of omnidirectional antennas. The results of simulation
 of a radio access network based on 19 three-sector base stations with planar antenna arrays
 of 64 elements illustrate a significant reduction in the level of interference in comparison with the case of omnidirectional antennas and, in order to obtain zones of a positive signal-to-noise ratio, confirm the need for a territorial separation of neighboring devices by 10–20 % of the range of radio coverage.

Service Orchestration Over Wireless Network Slices: Testbed Setup and Integration

Network Functions Virtualization Management and Orchestration (NFV-MANO) provides a standardized approach for the management and effortless deployment of (virtual) services. Although NFV-MANO initially focused on the deployment of services over datacenters, the introduction of fully softwarized network architectures even for the wireless network creates fertile ground for the re-conception of the manner through which the underlying hardware is managed. In this article, we consider the case of an open experimentation testbed, with focus on wireless networking, and adopt the Open Source MANO framework for provisioning virtual services on top of the experimentation equipment. We extend the Virtual Infrastructure Managers (VIMs) of the NFV-MANO architecture in two well-known frameworks (Openstack and OpenVIM) for the testbed in order to create and handle virtualized wireless network interfaces, hosted on the generic networking nodes of the testbed. Through our contributions, existing VNFs can be deployed over the testbed interconnected over wireless links, specified during the on-boarding phase. The extensions are introduced transparently to the existing operation of the platform, in order to allow the portability of network services and network functions to other instances as well. We focus on providing virtual functions inter-networked over wireless links, which traditionally are not handled by the framework, and allow easier interaction of the end-users with the testbed. We benchmark the framework in terms of performance and analyze the deployment case of a softwarized 5G Radio Access Network in a real testbed deployment.

Simultaneous Nonlinear Self-Interference Cancellation and Signal of Interest Recovery Using Dual Input Deep Neural Network in New Radio Access Networks

An efficient method for simultaneous nonlinear self-interference (SI) cancellation and signal-of-interest (SOI) recovery is proposed and experimentally verified in a mmWave over fiber testbed, based on a specially designed dual-input deep neural network (DI-DNN). The mmWave band has been adopted in the U.S. to provide 5G wireless access services due to its wide-bandwidth, light-of-sight propagation, and inherent compatibility with small cell architecture. To further meet the uprising bandwidth-demanding services, the in-band full-duplex (IBFD) scheme is introduced to magnify the mmWave channel capacity for the 5G and beyond wireless communications. Fortunately, the mmWave band is more realistic to implement IBFD compared with the conventional sub-6GHz band, as it operates as a highly directional beam and shorter transmission distance resulting in a higher SOI to SI ratio. In our proposed solution, a DI-DNN is, for the first time, proposed and implemented to simultaneously realize the SI cancellation and the SOI recovery. Moreover, we firstly mitigate the nonlinearity arising from the transmitters of both the SI and SOI, as well as the nonlinear crosstalk from the SI to the SOI after detection by a nonlinear receiver. The demonstration of the DI-DNN joint with the experimental implementation in a mmWave over fiber system, promise spectral efficient, high-capacity signal transport in the 5G and beyond wireless communications.