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Mobile communication is one of the most widely used technologies in today's world, which is on the verge of its fifth-generation (5G). Mobile broadband services are frequently availed by users (e.g., vehicular infotainment) on the go. Providing a consistent service quality to the user despite his mobility is a key challenge for network operators. The cellular service area is often associated with coverage holes, where the received signal quality is too poor to maintain a desired service quality. Further, deep shadowed holes would lead to signal loss and service interruption. Acquiring beforehand knowledge of a user encountering a coverage hole in his near future will be beneficial in designing smart resource allocation schemes. In this paper, a proactive allocation of radio resources is presented based on the forecast of a coverage hole in the user's immediate route. A Markov model enhanced with the start and terminus information of a user is used for the prediction. The user anticipated to experience a coverage hole imminently is allocated more resources appropriately and data buffering is facilitated. The simulation results show a significant improvement in user throughput and a major reduction in service interruption (for streaming service) while traversing routes associated with coverage holes. Further, this paper also highlights recent advancements in the 5G system (5GS) architecture that are vital for the deployment of the presented scheme and associated data analytics.

The fifth generation (5G) system was marketed as a true enabler of latency-aware ultra-reliable low latency communications (uRLLC), bandwidth-devouring enhanced mobile broadband (eMBB), and unlimited-things-centric massive machine type communication (mMTC) services for vertical industries and internet-of-everything (IoE). Among other promises, the eMBB services have gained significant momentum in various 5G deployments, such as using millimeter wave (mmWave) frequency band in the next-generation radio access network (NG-RAN). The early rollouts of the uRLLC and mMTC services show that the 5G system supports their basic features, however, supporting advanced and complicated use-cases of such services remains uncertain. These drawbacks are due to, but not limited to, the existing limitations in the adaptation of current technologies, such as multi-access cloud computing, network function virtualization, software-defined networking (SDN), network slicing, etc. in NG-RAN. In this regard, we aim to (i) provide a comprehensive overview on key concepts towards the RAN of sixth generation (6G) system; (ii) study various legacy RAN implementations, reported in literature, from different perspectives, and the motivation of their reconstruction and redesign with respect to the requirements of their forth-coming generations; (iii) present an exclusive review of the NG-RAN with special emphasize on the cloudification and virtualization of resources and services, and on the management and orchestration of RAN slices; (iv) identity the key drivers that leverage advanced mMTC, uRLLC, and eMBB services and applications; and (v) address key challenges and future research arising from the deployment of NG-RAN.

Mobile communication is an ubiquitously used technology that has evolved through various generations and is currently on the verge of its fifth generation (5G). In the recent years, Intelligent Transportation Systems (ITS) and supplementary vehicular use cases (e.g., autonomous driving) are considered widely within the scope of cellular networks. These use cases generally demand reliable and low latency services from the cellular network. Mobile Edge Clouds (MEC) in the 5G networks are often applicable to satiate such service demands of a vehicular user. However, the cellular handovers (HO) of vehicular users prompt frequent service migration among the MECs. The handovers and service migration increase the service interruption of a user. In this paper, we consider machine learning (ML) based mobility awareness to obtain future service migration and HO sites of a user. This enables smooth service migration by allowing non-state data transfer earlier to user handover. Further, it provides sufficient time for establishment of successful Coordinated Multipoint (CoMP) transmissions, which will reduce service interruption due to HO. Simulation results show that the proposed framework provides timely assistance for service migration and significantly reduces the service interruption time.

[EN] One of the main objectives of the METIS-II project was to enable 5G concepts to reach and convince a wide audience from technology experts to decision makers from non-ICT industries. To achieve this objective, it was necessary to provide easy-to-understand and insightful visualization of 5G. This paper presents the visualization platform developed in the METIS-II project as a joint work of researchers and artists, which is a 3D visualization tool that allows viewers to interact with 5G-enabled scenarios, while permitting simulation driven data to be intuitively evaluated. The platform is a game-based customizable tool that allows a rapid integration of new concepts, allows real-time interaction with remote 5G simulators, and provides a virtual reality-based immersive user experience. As a result, the METIS-II visualization platform has successfully contributed to the dissemination of 5G in different fora and its use will be continued after METIS-II., This work has been performed in the framework of the H2020/5G-PPP project METIS-II cofunded by the EU. The authors wish to thank the rest of METIS-II colleagues who contributed to the development of the METIS-II visualization platform.

This paper presents a novel user plane framework, tailored for different 5G services with diverse and conflicting key performance indicators. Initially, this paper identifies the major challenges in the legacy user-plane approaches and highlights the up-to-date 5G standardization activities in this area. It further analyzes new functional requirements related to service-oriented design and the introduction of new mechanisms to address them. Subsequently, this paper discusses how various user plane design decisions related to the control/user plane split options, network slicing, and radio access network (RAN)-core network (CN) interfacing can potentially impact the overall 5G architecture. For the latter, some key RAN/CN interface considerations and the interactions with CN given different protocols and quality of service models are investigated.

Mobile communication has evolved through various generations and is currently on the verge of its fifth generation (5G). The continually increasing number of subscribers and data traffic demand reflect the ubiquitous usage of this technology. Device-to-Device communication (D2D) is considered as a key technological component in 5G, which aims to offload base station from traffic routing by enabling direct link between communicating devices in proximity. In a D2D underlay, D2D pairs reuse resources from cellular links which results in better spectrum utilization. However, the mutual interferences (among D2D pairs and cellular users sharing resource) sabotage the performance. Smart resource allocation for D2D pairs can reduce the interference caused by cellular users to D2D pair. Nevertheless, D2D transmissions still cause significant interference to cellular links leading to poor cellular performance. This paper proposes interference aware power allocation (IAPA) for cellular links following D2D resource allocation, to make cellular links robust against interfering D2D transmissions. This solution avoids restricting D2D transmissions in-order to reduce their interference with cellular links and allows maximum exploitation of D2D communication opportunities. Further, considering the power consumption at the UE, power allocation at cellular links are administered leading to regulated IAPA (R-IAPA). Simulation results show decrease in number of dropped cellular users due to high interference from D2D transmissions, and an improved throughput at base station without hindering D2D performance while taking into account power consumption of cellular users.

Mobile communication has become one of the key technologies in contemporary world which is on the verge of its fifth generation (5G). In parallel, Intelligent Transportation System (ITS) is an emerging sector whose focus is safe, smart and efficient transportation. In recent years, various vehicular use cases are studied within the scope of cellular networks, to provide reliable and low latency services as required. With contemporary advancements such as Mobile Edge Clouds (MEC), ability of cellular networks to support vehicular use cases with low latency requirements (e.g., platooning of vehicles) has also increased. In this work, mobility context awareness is built in a cellular network and is subsequently exploited to assist migration of service or application that is being used by vehicular use case (e.g., vehicular platoon). Further, solutions based on Cooperative Multi-Point transmission (CoMP) and sidelink transmission are proposed to be used in tandem with service migration, to reduce data/service interruption that would arise due to high mobility of vehicles (e.g., handovers (HO)). Message sequence charts are also presented for aforementioned solutions and functional framework is presented for inspected vehicular scenario. Simulation and analysis show the potential of proposed mobility context awareness schemes in assisting service migration among MECs in timely fashion and reducing amount of data transfer and data interruption times, for considered vehicular use case.

5G is expected to operate in a wide frequency range to support new challenging use-cases. Multi- RATs (Radio Access Technologies): NR (New Radio) and evolved LTE (Long Term Evolution) will together constitute 5G. Utilizing NR at high frequencies will have a significant impact on radio propagation conditions with e.g. unfavorable higher path loss and increased outdoor-to-indoor penetration losses. In order to provide a reliable communication from the outset of 5G deployment and to minimize the standardization and implementation complexity, 5G UP (User Plane) instances of 5G AIs (Air Interface) related to evolved LTE and NR need to be aggregated on a certain layer of the protocol stack. This paper sheds light on how to integrate 5G AIs into a single 5G AI framework and explores which protocol stack layer could be used as aggregation layer. Inter-RAT hard handover is the state of the art technique to integrate multiple RATs in order to support mobility and reliability across different RATs. However, the hard handover incurs a transmission interruption which stands as an obstacle along the way of accomplishing 5G design. According to simulation results, a common PDCP (Packet Data Convergence Protocol) layer improves the hard handover functionality and stands out as a basis for tight interworking between evolved LTE and NR. By means of simulation, it is shown that the multi-RAT UP aggregation can achieve three times higher user throughput, when NR is using 28 GHz and LTE 2 GHz, compared to stand-alone NR.

In today’s world, mobile communication has become a ubiquitously used technology, currently on the verge of its fifth generation (5G). 5G anticipates higher traffic volume and connected device density than present. It further envisions variety of service types and use cases arising from them. Various 5G concepts are being developed in order to satiate the aforementioned challenges. One of the key tasks for 5G consortium however, is to present the developed technology and concepts to wider audience (academia, industry) and even convince decision makers from non-information and communications technology (ICT) industries. Thus, it is desired to have easy to understand illustrations of envisioned 5G use cases and proposed technical solutions, targeting the non-experts. The contemporary simulation framework has limited or no provision to dynamically visualize and interact with the simulation scenario, rendering it difficult to be used for marketing as well as academic purposes. In this paper, we propose a serious game engineering based 3D visualization and evaluation framework, which allows dynamic and interactive visualization of simulated scenario. The proposed framework is simulator agnostic which enables viewer to immerse oneself into the simulated scene, visualize key performance indicators (KPI) in almost real time and even interact with simulator by changing key simulation parameters. These features equip the proposed interactive visualization/evaluation platform (IVEP) to demonstrate the 5G use cases and developed concepts better, paving way for easier understanding and evaluation of the concepts even by non-experts., 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Mobile communication is arguably the most ubiquitously used technology in contemporary world, evolving towards its fifth generation (5G). The key challenges being faced by present day mobile communication are growing number of mobile users and subsequent high traffic volume posed by them. Providing uniform service quality and best quality of experience (QoE) in such dense scenarios is a major motive of 5G. Context awareness is a concept of extracting information from the user and his environment, and utilizing it to optimize user performance. Context awareness is recognized as one of the key pillars in enabling uniform quality of experience for mobile users. For instance, predicting the next cell for user transition, predicting the crowd formation in a cell etc., will assist the basestation to reserve or manage resources and prepare the cell well in advance for a future event, targeting to provide uninterrupted and uniform QoE. This paper investigates context aware procedures with a focus on user mobility, finds commonalities among different procedures and proposes a general framework to support mobility context awareness. The new information and interfaces which are required from various entities (e.g., vehicular infrastructure) are discussed. Further, a context aware resource allocation algorithm is designed, exploiting new interfaces and information arising from vehicular infrastructure. Simulation results demonstrate improvements in user throughput, thus validating the requirement of new interfaces.

The standardization of 5G, referred to as new radio (NR), has started. The 5G air interface (AI) is envisioned to comprise a number of AIs including the evolution of Long Term Evolution (LTE) and new NR AIs operating at frequencies from below 1GHz and up to 100GHz. One of the key challenges is the design of an overall 5G AI that efficiently combines these AIs into one 5G AI framework. This paper examines how the AIs can be harmonized into a single 5G AI framework, while minimizing the complexity of standardization and implementation. The harmonization among the 5G AIs could take place at any layer: at the physical layer (PHY), at the Medium Access Control layer (MAC) or at the Packet Data Convergence Protocol (PDCP) layer. Among various detailed harmonization options that are being investigated in this context, for the integration of the new AIs at the physical layer, cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) waveform with various numerologies is considered as a basis and the MAC layer harmonization can be built on top of the PHY layer, where the CP-OFDM-related AIs of different numerologies are catered for different future services, bands, and cell types. Also, harmonization at the PDCP layer appears to be the most viable harmonization option between the evolved LTE and the new AIs. The results of the first conducted 5G AI analysis will constitute discussion topics for other 5G-PPP projects as well as 3GPP.

Mobile communication is one of the most ubiquitously used technologies in today's world, evolving towards its fifth generation (5G). Amidst increasing number of devices and traffic volume, one of the key focuses of 5G is to provide uniform service quality despite high mobility. In real world scenarios, user mobility is not random but rather direction oriented, based on its origin and destination. Further, several users exhibit repeated mobility patterns on daily basis (e.g., office goers, commuters in public transport etc.). Such mobility is termed as Diurnal mobility. Information of such diurnal mobility can assist in improving prediction accuracy of future user location (e.g., cells, routes). Knowledge of future user location will enable the designing of efficient resource management algorithms, aiming to make great service quality follow the user. In the presented work, information of diurnal mobility is used to enhance the accuracy of mobility prediction (next cell prediction as well as route prediction) in a realistic urban scenario. Further, using this context information about future routes and possible coverage holes in them, efficient resource allocation is done to sustain streaming/full buffer services, even in coverage holes. The simulation results show substantial improvements in user throughput as a result of context aware resource allocation, enabled by diurnal user mobility prediction.

Device-to-Device (D2D) communication underlaying cellular communications takes advantage of physical proximity of devices to improve coverage, resource utilization, data rates, QoS,and offers network operators the possibility to offload normally network-routed traffic to direct P2P links. This paper presents a novel Resource Allocation (RA) concept for D2D User Equipment(D-UEs) reusing the Physical Resource Blocks (PRBs) of the Cellular UEs (C-UEs). The RA algorithm is based on a virtual sectoring concept that relies on network assisted positioning technologies. By means of system level simulations, we show that this novel RA scheme yields significant performance and efficiency gains. To this end, we show the gains when such a RA scheme is employed along with some potential future directions.

Device-to-Device (D2D) communications is considered as one of the key technologies for 5G wireless communications system, since it offers several advantages compared with traditional cellular network, i.e. low end-to-end latency and high spectral efficiency. By allowing underlaying direct D2D communications, a D2D link which reuses the spectral resources of a cellular user causes increased interference to this cellular link. Further, the D2D link suffers from interference caused by the cellular user. In this paper, we propose a network controlled algorithm with low computation complexity to efficiently maximize the reuse of cellular network spectrum with the help of using channel information at base station. This algorithm can be used to select the transmission mode of potential D2D pairs. Besides, we also propose to use an auction algorithm for maximizing system capacity. The low complexity of these two algorithms reduces the setup delay of D2D links. Simulation results demonstrate improved system performance in terms of overall system capacity and number of established D2D links.

As one of the key technologies for next generation wireless communication systems, Device-to-Device (D2D) communication is able to offer several benefits, e.g. low end-to-end latency and traffic offload. In particular network controlled D2D operation underlaying the primary cellular system, e.g. LTE, and reusing its radio resources, is seen as an appealing option for increasing system capacity. However, a D2D link that reuses radio resources of another link introduces mutual interference in between these two links. In this paper, we firstly propose a network controlled radio resource management (RRM) algorithm that maximizes overall user satisfaction with the assistance of context information. The task of the algorithm is to decide on appropriate operation mode and allocate radio resources to both cellular and D2D links with respect to their channel state information (CSI) and service requirements. Furthermore, the RRM algorithm is extended to account for link priority information. Simulation results demonstrate the substantial performance improvement of our smart algorithm compared with a partial smart algorithm in terms of links satisfaction ratio.

The high popularity of smartphones and mobile PCs is expected to increase wireless data traffic in the order of 1000 times by 2020 [1]. However, the current situation of Mobile Network Operator (MNO)s is characterized by increasing margin pressure due to declining revenues and an increasing cost base. Self-optimization functionalities, e.g. for Mobility Robustness Optimization (MRO), are essential means for reducing Operational Expenditure (OPEX). In particular, mobile user groups or moving networks at high speeds impose challenges and may severely degrade network performance as well as user experience. The Fuzzy Q-Learning-based approach presented in this paper aims at providing a generic basis for enabling self-optimizing and self-healing network operations. The designed concept consists of the following key components: Fuzzy Inference System (FIS), heuristic Exploration/Exploitation Policy (EEP), and Q-Learning (QL). Its performance in a reference scenario is compared with a trend-based handover (HO) optimization scheme presented in [2] and a scheme that assigns time-to-trigger (TTT) values based on velocity estimates.

Mobility of commuters is not purely random but rather direction oriented and may be learned after monitoring user movements for a couple of business days. Exploiting movement data and context information of diurnal user movements (public transportation, vehicular users, etc.) allows for predicting cell transitions and lays the basis e.g. for designing efficient resource reservation schemes or smart resource mapping approaches. In real life scenarios, several mobile users co-travel in public transport forming data intensive moving user clusters or moving networks [12]. Various load balancing solutions exist to manage congestion situations that could arise [4][5]. However, the crucial trigger for these solutions is timely prediction of arrival of moving user clusters or moving networks into a cell. This paper presents prediction and detection schemes that exploit context information for predicting user cell transitions and resulting congestion. These schemes are utilized to anticipate the arrival of data intensive moving user groups/moving networks, which are also referred to as "hotspots", into a cell. Simulation results demonstrate robust and timely prediction of these events and their applicability for handover optimization and smart resource management even at high velocities.