Next-generation Mobile Networks Research Articles

Towards Traffic Identification and Modeling for 5G Application Use-Cases

To analyze next-generation mobile networks properly, there is a need to define key performance indicators (KPIs). Testing signaling only or just partial domains of the network have been replaced with end-to-end testing methodologies. With the appearing of machine-to-machine (M2M) applications, this question became even harder, since there is no direct user feedback. Quality of experience cannot be measured accurately in M2M applications, even if the network operates correctly and without failures. There are dozens of new—but theoretical—use-cases for 5G; however, these are not tested on a live network. The modeling methodology used throughout the paper follows the steps of observation, analysis, model creation, implementation, and verification. The first part of the paper examines the three application-types: enhanced mobile broadband (eMBB), critical Internet of Things (cIoT), and mass Internet of Things (mIoT). Afterwards, we introduce the main traffic characteristics based on current mobile networks’ traffic patterns and measurements. Considering the measurement results, we introduce a methodology and define traffic models for the simulation of different application-types. To validate these models, we compare the generated artificial traffic with real traffic patterns. In the second part of the paper, we examine what the main effects of these traffic patterns on a domestic 5G test-network are. Finally, we suggest some considerations on the possible main impacts regarding 5G network design.

Mean Field Game Guided Deep Reinforcement Learning for Task Placement in Cooperative Multiaccess Edge Computing

Cooperative multiaccess edge computing (MEC) is a promising paradigm for the next-generation mobile networks. However, when the number of users explodes, the computational complexity of the existing optimization or learning-based task placement approaches in the cooperative MEC can increase significantly, which leads to intolerable MEC decision-making delay. In this article, we propose a mean field game (MFG) guided deep reinforcement learning (DRL) approach for the task placement in the cooperative MEC, which can help servers make timely task placement decisions, and significantly reduce average service delay. Instead of applying MFG or DRL separately, we jointly leverage MFG and DRL for task placement, and let the equilibrium of MFG guide the learning directions of DRL. We also ensure that the MFG and DRL approaches are consistent with the same goal. Specifically, we novelly define a mean field guided $Q$ -value (MFG-Q), which is an estimation of the $Q$ -value with the Nash equilibrium gained by MFG. We evaluate the proposed method’s performance using real-world user distribution. Through extensive simulations, we show that the proposed scheme is effective in making timely decisions and reducing the average service delay. Besides, the convergence rates of our proposed method outperform the pure DR-based approaches.

On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks.

Addressing high capacity at low power as a key design goal envisages achieving high spectral efficiency (SE) and energy efficiency (EE) for the next-generation mobile networks. Because most data are generated in indoor environments, an ultra-dense deployment of small cells (SCs), particularly within multistory buildings in urban areas, is revealed as an effective technique to improve SE and EE by numerous studies. In this paper, we present a framework exploiting the four most interconnected-domain, including, power, time, frequency, and space, in the perspectives of SE and EE. Unlike existing literature, the framework takes advantage of higher degrees of freedom to maximize SE and EE using in-building SCs for 5G and beyond mobile networks. We derive average capacity, SE, and EE metrics, along with defining the condition for optimality of SE and EE and developing an algorithm for the framework. An extensive system-level evaluation is performed to show the impact of each domain on SE and EE. It is shown that employing multiband enabled SC base stations (SBSs) to increase operating spectrum in frequency-domain, reusing spectrum to SBSs more than once per building in spatial-domain, switching on and off each in-building SBS based on traffic availability to reduce SBS power consumption in power-domain, and using eICIC to avoid co-channel interference due to sharing spectrum with SBSs in time-domain can achieve massive SE and EE. Finally, we show that the proposed framework can satisfy SE, EE, as well as user experience data rate requirements for 5G and beyond mobile networks.

Analytical and Experimental Evaluation of a Novel Mechanism to Improve the Control Plane in Next-Generation Mobile Networks

With the continuous development of mobile communications and the Internet of Things technology, the enhanced network performance can be seen as one of the major challenges in the fourth industrial revolution context, where new services and applications with strict performance requirements have emerged, such as driverless vehicles, smart cities, factories, and manufacturing, among others. These new services and applications also drive growth of the data traffic, which is increasing exponentially. Thus, in mobile network environments, industry and academia are proposing new mechanisms to overcome the traffic bottlenecks and reduce the signaling overhead that affects current networks. Centralized Mobility Management solutions are prone to several problems such as the aforementioned signaling overhead or scalability issues. To overcome these limitations, Distributed Mobility Management approaches are being considered. In this paper, an analytical cost model and experimental evaluation will be developed for evaluating the performance of the Distributed Mobility Management implementations. Furthermore, a new approach will be proposed to improve network performance.

Performance Analysis of Resource Scheduling Techniques in Homogeneous and Heterogeneous Small Cell LTE-A Networks

The purpose of LTE-A technology is to provide high spectral efficiency, lower delay and stronger intercell interference control for a multi- user environment. The architecture of LTE basically contains E-UTRAN and evolved packet core. E-UTRAN is the combination of UE and ENodeB (enhanced node B) used to control the radio network. EPC provide end to end connection and backward capability with previous networks. Major entities included in EPC are P-GW, S-GW, HSS and MME. This new architecture fulfil the requirement of next-generation mobile networks for high connectivity and multiple type of networks. Heterogeneous network is one of them in which different power base stations like femtocell, picocell, and radio remote head deployed in a geographical area with in macrocell cell. It is one of the cost effective solution to provide high throughput and spectral efficiency and fairness for cell edge user. Deployment of femtocell improved the data rate and coverage in small regions like office, home, shopping mall and dense areas. Along with this an efficient fair sharing of resource allocation plays an important role in improving the performance of networks. This paper analysed the performance of round robin, resource fair, Max-throughput, best CQI and proportional fair LTE-A resource scheduling techniques on homogeneous networks. For heterogeneous networks a new cluster based proportional fair resource scheduling technique is proposed, which provides an efficient resource to the most sufferer user and improved fairness and cell edge user throughputs. In heterogeneous networks multiple femto cell were deployed in macro cell area. Access policies are applied on open and closed group. The performance is measured in terms of cell edge throughputs, peak throughputs, average throughputs, wideband SINR, spectral efficiency, and fairness index for homogeneous, heterogeneous networks and for user mobility. The performance result showed that Best CQI and MaxTP scheduler outperforms in throughput level and attains highest average cell throughput of 83 Mbps and UE average throughput of 20 mbps in homogeneous networks and approximately 43 Mbps in heterogeneous networks. Comparative result in heterogeneous network indicates that the proposed scheduler increases 2% gain in fairness and 1–1.89% gains in edge throughputs with respect to proportional fair scheduler and allocates 79% fair share resources among UE. Round robin scheduler delivered very poor throughput and fairness index in both homogeneous and heterogeneousnetworks.

Technologies Assisting the Paradigm Shift from 4G to 5G

Due to exponential increase in the capacity demands raised by the smart devices and multimedia applications of new generations, existing cellular networks are facing significant burden. To provide a solution to the challenges faced by 4th generation (4G) networks, there is utmost need of improving existing technologies as well as developing new technologies to meet the key requirements of 5th generation (5G) networks as well as the Next Generation Mobile Networks (NGMNs). Networks having high capacity, low latency, faster data rates and better quality of service is the vision of 5G mobile networks. In order to achieve this, we will need wider bandwidths as offered by millimeter wave bands, more spatial diversity as offered by Massive multiple input multiple output technology, denser networks as designed in dense small cell deployment technology, new waveforms using efficient coding techniques such as filtered orthogonal frequency division multiplexing and filter bank multiple carrier, a new architecture supporting virtualization and advance computing techniques as in network function virtualization, software defined networks, cloud radio access networks. Many more technologies are yet to be introduced. This tutorial gives an insight to the candidate technologies proposed by researchers till date and the progress made, for defining the standards to meet the requirements of 5G and NGMNs. It explains the basic concept of the technology and the latest development in the industry to support this technology.

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

An Efficiency-Improved Clustering Algorithm Based on KNN Under Ultra-Dense Network

Ultra-Dense Network (UDN) is one of the key techniques for the next generation of mobile network due to providing high system throughput. However, severe interference often occurs in UDN, which greatly impact the data rates of cell-edge users. User-centric wireless access virtualization has been widely adopted in UDN to mitigate the interference of cell-edge users by sharing resources and eliminating cell boundary. However, it's only effective for moderate scale networks. Moreover, the efficiency needs further improvement. In this paper, we study effective cooperative clustering method for large scale UDN with less computations in order to improve the throughput of cell-edge users. We formulate a convex optimization problem in which the objective is to maximize the system throughput with overlapping virtual cells. We propose a clustering method to solve this optimization problem. We design a fast-convergent iterative algorithm called K-Nearest Neighbor (KNN) algorithm to perform users clustering. Simulation results show that our proposed algorithm has better throughput performance for both average and cell-edge users. Especially, the per-carrier throughput is improved, which leads to more serviceable users with limited resources.

On the Opportunities and Challenges of NOMA-Based Fog Radio Access Networks: An Overview

Future generations of wireless networks are expected to provide new services with an unprecedented level of diverse and stringent requirements. Fog Radio Access Network (FRAN) and Non-Orthogonal Multiple Access (NOMA) have emerged as complimentary enablers to meet such requirements. On the one hand, FRAN architecture is designed to reduce the delay caused by the fronthaul link by pushing control and storage to the network edge. On the other hand, in addition to increasing the spectral and energy efficiency and the number of connected devices, NOMA has the potential to improve network latency. This paper overviews the joint benefits of enabling NOMA schemes in an FRAN architecture, by means of examining the applicability and adequateness of the NOMA-based FRAN features in achieving specific objectives of next generation of mobile networks, mainly those related to enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communication (URLLC). The paper further depicts the challenges and future research directions that must be addressed in order to meet such opportunities.

A Session-Based Cross-Layer Approach for Seamless Connectivity in Next-Generation Mobile Networks

The Fifth Generation (5G) network provides a platform for emerging technologies such as vehicular communication, massive Internet of Things (IoT), and tactile internet. It is paving the way to a modern autonomous industry with mobility as a key requirement to achieve seamless end-to-end connectivity. But the current transport layer is not well equipped with the evolving lower layers to deal with the dynamics of the network conditions during mobility. The variation in network conditions and frequent disturbance in lower layers due to handovers causes performance degradation in the transport layer. Hence, we propose a novel solution called ID oriented Socket Layer (IoSL), an end-to-end solution for futuristic applications. It is an ID-based software solution that maintains a unique identifier among the transport layer connections to provide seamless connectivity to the end devices. During user mobility and frequent handover scenarios, it communicates with the lower layers and effectively controls the transport layer for achieving improved Quality-of-Service (QoS) with enhanced user experience. We prototyped IoSL in mobile devices and evaluated the performance using the ns-3 simulator as well as live-air experiments. The experiments are conducted using Wi-Fi and 5G networks, and the results show a significant reduction in reconnection latencies around 33% and improved end-user throughput by up to 16%.

Ubiquitous Distributed Deep Reinforcement Learning at the Edge: Analyzing Byzantine Agents in Discrete Action Spaces

The integration of edge computing in next-generation mobile networks is bringing low-latency and high-bandwidth ubiquitous connectivity to a myriad of cyber-physical systems. This will further boost the increasing intelligence that is being embedded at the edge in various types of autonomous systems, where collaborative machine learning has the potential to play a significant role. This paper discusses some of the challenges in multi-agent distributed deep reinforcement learning that can occur in the presence of byzantine or malfunctioning agents. As the simulation-to-reality gap gets bridged, the probability of malfunctions or errors must be taken into account. We show how wrong discrete actions can significantly affect the collaborative learning effort. In particular, we analyze the effect of having a fraction of agents that might perform the wrong action with a given probability. We study the ability of the system to converge towards a common working policy through the collaborative learning process based on the number of experiences from each of the agents to be aggregated for each policy update, together with the fraction of wrong actions from agents experiencing malfunctions. Our experiments are carried out in a simulation environment using the Atari testbed for the discrete action spaces, and advantage actor-critic (A2C) for the distributed multi-agent training.

A Robust User Association, Backhaul Routing, and Switching Off Model for a 5G Network With Variable Traffic Demands

With the expected increase in data traffic (e.g., video) and in the user devices generating new traffic (e.g., device-to-device communication, Internet of Things, etc.), the evolution of next-generation mobile networks (e.g., 5G networks) has gone towards heterogeneous deployments where multiple small cells coexist in the same area covered by a macro base station. To reduce the capital expenses in the network, a wireless mesh can be used, which is made of millimeter-wave links that route the data traffic of the mobile users inside the backhaul network. Such an increase in the number of deployed base stations inevitably increases the power consumption; hence, the operating expenses and the CO2 consumption also increase. To achieve greener mobile communications, sleep-mode strategies have been considered in order to switch off the unused network components. However, the switching on/off should be made according to the traffic demanded by the users and with the aim of guaranteeing the demanded service at any time. Given that the traffic demand and networking traffic fluctuate over time at each location, we propose a robust mixed integer linear problem that jointly solves the user association, the backhaul routing paths in the wireless mesh and the switching off of the unused links with the aim of minimizing the power consumption. The robust strategy is based on the $\Gamma $ -robust approach and is able to guarantee the user demand while taking into account its intrinsic variability. A thorough evaluation has been performed in order to analyze the impact of the robust strategy on the network performance.

Communication and Computation Offloading for Multi-RAT Mobile Edge Computing

The next generation of mobile networks, 5G, aims at supporting lower end-to-end latency, higher reliability and higher throughput, which can be improved by MEC and multi-RAT offloading, respectively. With MEC, a base station in 5G can be equipped with computing power, which can be called as an edge in MEC. With the assistance of the edges, traffic with computational tasks can be directly executed in local servers, without forwarding the tasks to the cloud or core network. Edge computing hence reduces the latency and the traffic load significantly. Conventional multi- RAT offloading decides based only on either communication resources or computing resources. In this work, we argue that, to better utilize the communication and computing resources, neighboring edges should share their resources and cooperatively offload the requests from their clients. To this end, we introduced a double offloading mechanism, called LCCOP, to offload incoming traffic to the best pair of radio and edge subject to the endto- end latency of the requesting connections. We conduct simulations to compare LCCOP with the conventional offloading schemes. The results show that LCCOP's double offloading can significantly improve the satisfaction ratio by up to 83 percent and 143 percent, respectively, as compared to pure computation offloading and pure communication offloading.

An NFV-Based Service Framework for IoT Applications in Edge Computing Environments

Emerging Internet of Things (IoT) applications share the same characteristics of involving multiple processing components ( i.e. , function modules) and requiring a massive amount of data to be processed with low latency. To meet these needs, edge/fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. Thanks to the development of Network Functions Virtualization (NFV), with which edge computing platform can virtualize function modules and deploy them on any edge devices to provide flexible services on the edge networks. However, such platform would need to deal with complicated function module calling relationship ( i.e. , call graph) of applications and user mobility, and both are not thoroughly considered by existing works of NFV and edge computing. In this paper, based on our previous idea of virtual local-hub (VLH), we propose a complete design of edge computing framework, which applies NFV technology on edge computing environment for IoT applications. To handle the complicated call graphs of IoT applications with better resource utilization, the VLH framework adapts the technologies of container-based virtualization and microservice architecture, which enables remote function module sharing on the edge computing environment. The framework includes the heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We also present a design of protocols for system operations and mobility handling in the framework. Then we implement the framework on commodity hardware as a testbed. Via simulations under a large-scale environment with practical settings and experiments on the testbed under real-world scenarios, we demonstrate and verify the effectiveness and practicability of the proposed VLH framework for IoT application service provision.

A Deep Learning Based Transmission Algorithm for Mobile Device-to-Device Networks

Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the spectral efficiency. More specifically, we study a problem of radio resource management for D2D networks, which is one of the most challenging problems in D2D networks, and we also propose a new transmission algorithm for D2D networks based on deep learning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector indicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the trained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%∼95% in spite of its simple structure due to the limitation in computing power.

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks.

Self-optimization of Handover Control Parameters for Mobility Management in 4G/5G Heterogeneous Networks

A large number of small cells in the next-generation mobile networks is expected to be deployed to satisfy 5G requirements. Mobility management is one of the important issues that require considerable attention in heterogeneous networks, where 5G ultra-dense small cells coexist with the current 4G networks. An efficient handover (HO) mechanism is introduced to address this issue and improve mobility management by adjusting HO control parameters (HCPs), namely, time-to-trigger and HO margin. Dynamic HCPs (D-HCPs), which explores user experiences to adjust HCPs and make an HO decision in a self-optimizing manner, is proposed in this paper. D-HCPs classify HO failure (HOF) into three categories, namely, too late, too early and wrong cell HO, and simultaneously adjust HCPs according to the dominant HOF. The algorithm is evaluated using different performance metrics, such as HO ping-pong, radio link failure and interruption time, with different mobile speed scenarios. Simulation results show that the proposed D-HCPs algorithm adaptively optimizes the HCPs and outperforms other algorithms from the literature.

Intelligent Resource Scheduling for 5G Radio Access Network Slicing

It is widely acknowledged that network slicing can tackle the diverse use cases and connectivity services of the forthcoming next-generation mobile networks (5G). Resource scheduling is of vital importance for improving resource-multiplexing gain among slices while meeting specific service requirements for radio access network (RAN) slicing. Unfortunately, due to the performance isolation, diversified service requirements, and network dynamics (including user mobility and channel states), resource scheduling in RAN slicing is very challenging. In this paper, we propose an intelligent resource scheduling strategy (iRSS) for 5G RAN slicing. The main idea of an iRSS is to exploit a collaborative learning framework that consists of deep learning (DL) in conjunction with reinforcement learning (RL). Specifically, DL is used to perform large time-scale resource allocation, whereas RL is used to perform online resource scheduling for tackling small time-scale network dynamics, including inaccurate prediction and unexpected network states. Depending on the amount of available historical traffic data, an iRSS can flexibly adjust the significance between the prediction and online decision modules for assisting RAN in making resource scheduling decisions. Numerical results show that the convergence of an iRSS satisfies online resource scheduling requirements and can significantly improve resource utilization while guaranteeing performance isolation between slices, compared with other benchmark algorithms.

Critical Communication Infrastructures and Huawei

Recently, there have been growing cyber-safety concerns over telecom equipment made by the Chinese vendor Huawei. This has led many countries to ban Huawei from supplying equipment for building the next generation of mobile networks, 5G. Responses from mobile operators and the telecom community in general have been mixed. For instance, many European mobile operators have stated that these concerns are overblown and that such a ban would delay 5G rollout by two to three years in the best case. Moreover, some operators have directly questioned the ability of the other vendors to timely deliver a complete 5G network. However, these claims have mostly not been grounded in empirical data. This paper takes a multi-perspective approach to investigating this problem empirically. We start by categorizing responses from different countries to using Huawei equipment in 5G. We then analyze the importance and readiness of Huawei for supplying 5G equipment. This analysis is based on contributions to standards and patents. We also present a conceptual risk analysis framework to qualitatively evaluate the ability of a single vendor to cause considerable damage to critical communication infrastructures. This model aims at exploring a set of relevant axis. More specifically, we look at potential for harm in different political climates that is peace, crisis and war. Another axis is whether banning a particular vendor from supplying equipment for the upcoming mobile networks generation is useful without having a backward compatible ban. A third axis is the ability of a vendor to cause harm as a function of the type of supplied equipment, for example radio towers vs network management systems. Combining the analysis of readiness for supplying 5G and potential for causing harm allows us to roughly estimate the likely impact that a complete ban would have on 5G rollout in different parts of the world. We find that such a ban can possibly delay 5G by two years or more for operators with high dependence on Huawei. Consequently, we explore potential approaches that would both reduce vendor-related risk and do not significantly delay the rollout of 5G. These include heterogeneous multi-vendor deployments, equipment verification and testing, international collaboration as well as signing non-aggression treaties. Unfortunately, there is no technological solution that fully remedy this problem. Combining technical solutions with efforts to build trust between countries, enforce existing alignments or create new ones seems a promising way forward.

A survey on authentication and access control for mobile networks: from 4G to 5G

The next generation of mobile networks, 5G, is expected to support a set of multiple requirements and use cases that will create an improved user experience. 5G will also be able to provide a high level of security by considering a variety of security aspects, such as authentication and access control mechanisms. The current protocol in 4G designed to address security is 4G AKA. It presents some weaknesses and vulnerabilities that negatively affect operators’ networks and their subscribers’ security. In designing an authentication and access control mechanism for 5G, it is crucial to evaluate both 4G AKA’s weaknesses and the new requirements of 5G. In this paper, we survey the vulnerabilities of the 4G AKA protocol, as well as the current 5G architectural answers brought by the 3GPP.