5G Communication Networks Research Articles

Evaluation of D2D Communications in 5G Networks

5G networks are being tested today. 5G networks are capable of improving existing services and delivering new quality of service through low latency, such as tactile internet. Existing technologies do not meet the requirements of 5G, which necessitates the development of new technologies. These include new connectivity methods that depend on the quality of the network's functioning. One of the most famous D2D communications. 5G networks use millimeter-band technologies. As the radio signal energy of the millimeter range is strongly absorbed by the environment, this range has a short communication range, so a large number of base stations must be installed in the network. But this is not always effective as their use decreases. D2D device-to-device technology is used to solve this problem. In order to ensure a high quality of network service, attention must be paid to the structure of the D2D organization. The basic structural parameters are the communication distance, the communication radius and the number of repeaters. This can be solved by selecting the relay node correctly. For this purpose it is necessary to take into account the change of the allowed data rate in the channel due to the allocation of its resources by the network node, used as a repeater. In order for the route to provide the necessary bandwidth, it is necessary to change the distance between the network nodes. This ensures network connectivity and the required level of quality of service. This is achieved by choosing the optimal algorithm that is considered in the article.

Handover Management for D2D Communication in 5G Networks

This study addresses the handover management issue for Device-to-Device communication in fifth-generation (5G) networks. The Third Generation Partnership Project (3GPP) drafted a standard for proximity services (ProSe), also named device-to-device (D2D) communication, which is a promising technology in offering higher throughput and lower latency services to end users. Handover is an essential issue in wireless mobile networks due to the mobility of user equipment (UE). Specifically, we need to transfer an ongoing connection from an old E-UTRAN Node B (eNB) to a new one, so that the UE can retain its connectivity. In the data plane, both parties of a D2D pair can communicate directly with each other without the involvement of the base station. However, in the control plane, devices must be connected to the eNB for tasks such as power control and resource allocation. In the current standard of handover scheme, the number of unnecessary handovers would be increased by the effect of shadowing fading on two devices. More important, the handover mechanism for D2D pairs is not standardized yet. LTE-A only considers the handover procedure of a single user. Therefore, when a D2D pair moves across cell boundaries, the control channels of the two UEs may connect to different base stations and result in increased latency due to the exchange of D2D related control messages. Hence, we propose a handover management scheme for D2D communication to let both parties of a D2D pair handover to the same destination eNB at the same time. By doing so, the number of unnecessary handovers, as well as the handover latency, can be reduced. In the proposed method, we predict the destination eNB of D2D users based on their movements and the received signal characteristics. Subsequently, we make a handover decision for each D2D pair by jointly factoring in the signal quality and connection stability. Expected improvement can be attained, as revealed in the simulation. Unnecessary handover can be avoided. Consequently, both UEs of a D2D pair reside in the same cell and, therefore, result in increased throughput and decreased delay.

DEFT: Multipath TCP for High Speed Low Latency Communications in 5G Networks

Multipath TCP (MPTCP) is a promising solution that can provide high end-to-end throughput in fifth generation (5G) mobile networks. Many next-generation applications will require high throughput and low latency simultaneously, but the current MPTCP congestion control algorithms cannot reliably satisfy this requirement. In this paper, a novel MPTCP congestion control scheme named delay-equalized FAST (DEFT) is proposed to achieve high throughput and low end-to-end (E2E) delay in 5G networks. First, in order to achieve high throughput, DEFT includes a novel window control algorithm that shows fast responsiveness when the state of the millimeter-wave (mmWave) link changes from line-of-sight (LOS) to non-LOS (NLOS) and vice versa. Second, in order to achieve low E2E delay, DEFT includes a delay-equalizing algorithm which minimizes additional reordering delay in the receive buffer. The performance of DEFT was evaluated based on ns-3 simulation and was compared with wVegas, Balia, and delay-adapted LIA. Simulation results show that DEFT can provide a significant goodput gain and application-level E2E delay reduction for the range of interest.

Towards Ensuring Reliability of Vehicular Ad Hoc Networks Using a Relay Selection Techniques and D2D Communications in 5G Networks

Benefiting from unlimited power supply, vehicular ad hoc networks (VANETs), in a 5G platform, can deal superiorly with the challenges of providing high reliability. As a prerequisite for ensuring reliability, reducing message delivery delay is pursued by improving the performance of message broadcast protocol. The amount of exchanged overhead to select a relay node, and the time consumed to forward a message are the two foremost factors in this regard. In the other hand, cognitive radio (CR) and device-to-device (D2D) communication techniques have been considered inherently in 5G to overcome bandwidth shortage, coverage limitation and excessive delay. So, in this article, we study some relay selection algorithms appropriate for 5G-VANET and classify them based on network reliability requirements. Considering the capabilities of CR and D2D communications in 5G-VANET, a method is proposed to select the farthest available node to forward a message without any need to exchange control messages with other vehicles. Unlike other existing works, we decrease the impact of control data overhead on network reliability in terms of message delivery hops and delay. Our simulation results show the proposed method can significantly increase network reliability by reducing message delivery delay, as well as the number of hops needed to deliver a message to the furthermost vehicles in a platoon.

Distributed Artificial Intelligence Solution for D2D Communication in 5G Networks

Device to Device (D2D) Communication is one of the technology components of the evolving 5G architecture, as it promises improvements in energy efficiency, spectral efficiency, overall system capacity, and higher data rates. The above noted improvements in network performance spearheaded a vast amount of research in D2D, which have identified significant challenges that need to be addressed before realizing their full potential in emerging 5G Networks. Towards this end, this paper proposes the use of a distributed intelligent approach to control the generation of D2D networks. More precisely, the proposed approach uses Belief-Desire-Intention (BDI) intelligent agents with extended capabilities (BDIx) to manage each D2D node independently and autonomously, without the help of the Base Station. The paper includes detailed algorithmic description for the decision of transmission mode, which maximizes the data rate, minimizes the power consumptions, while taking into consideration the computational load. Simulations show the applicability of BDI agents in jointly solving D2D challenges.

A Novel Access Control and Energy-Saving Resource Allocation Scheme for D2D Communication in 5G Networks

This paper investigates access link control and resource allocation for the device-to-device (D2D) communication in the fifth generation (5G) cellular networks. The optimization objective of this problem is to maximize the number of admitted D2D links and minimize the total power consumption of D2D links under the condition of meeting the minimum transmission rate requirements of D2D links and common cellular links. This problem is a two-stage nondeterministic polynomial (NP) problem, the solving process of which is very complex. So, we transform it into a one-stage optimization problem. According to the monotonicity of objective function and constraint conditions, a monotone optimization problem is established, which is solved by reverse polyblock approximation algorithm. In order to reduce the complexity of this algorithm, a solution algorithm based on iterative convex optimization is proposed. Simulation results show that both algorithms can maximize the number of admitted D2D links and minimize the total power consumption of D2D links. The proposed two algorithms are better than the energy efficiency optimization algorithm.

Challenges on the Way of Implementing TCP Over 5G Networks

5G cellular communication, especially with its hugely available bandwidth provided by millimeter-wave, is a promising technology to fulfill the coming high demand for vast data rates. These networks can support new use cases such as Vehicle to Vehicle and augmented reality due to its novel features such as network slicing along with the mmWave multi-gigabit-per-second data rate. Nevertheless, 5G cellular networks suffer from some shortcomings, especially in high frequencies because of the intermittent nature of channels when the frequency rises. Non-line of sight state, is one of the significant issues that the new generation encounters. This drawback is because of the intense susceptibility of higher frequencies to blockage caused by obstacles and misalignment. This unique characteristic can impair the performance of the reliable transport layer widely deployed protocol, TCP, in attaining high throughput and low latency throughout a fair network. As a result, the protocol needs to adjust the congestion window size based on the current situation of the network. However, TCP is not able to adjust its congestion window efficiently, and it leads to throughput degradation of the protocol. This paper presents a comprehensive analysis of reliable end-to-end communications in 5G networks. It provides the analysis of the effects of TCP in 5G mmWave networks, the discussion of TCP mechanisms and parameters involved in the performance over 5G networks, and a survey of current challenges, solutions, and proposals. Finally, a feasibility analysis proposal of machine learning-based approaches to improve reliable end-to-end communications in 5G networks is presented.

A Survey of End-to-End Solutions for Reliable Low-Latency Communications in 5G Networks

Very low latency and high reliability are two of the main requirements for new applications exploiting 5G networks. This is the case for the remote operation of robots or vehicles, the autonomous interaction of equipment in a factory, autonomous driving and tactile internet applications. Although the TCP/IP stack has been sufficient as the end-to-end solution for most of the history of the Internet, a number of surveys have appeared recently presenting many different methods for managing the end-to-end communication to meet the requirements of various technologies such as that of 5G networks. In this paper, we present a novel classification of the literature focused on new end-to-end solutions and the creation of services towards the support of low latency (1 ms) and high reliability (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9</sup> error rate) in current and future 5G networks. We specifically highlight how the proposals can be classified according to enabling technologies and the specific method used to achieve success in terms of the latency and reliability. The literature related to end-to-end solutions for reliable low-latency communications are organized according to three main topics: (i) end-to-end protocols that improve communication in terms of latency and reliability, (ii) functionality or technologies implemented on the network to support the current demands, and (iii) application programming interfaces that enhance the correct utilization of those protocols and additional technologies.

Incentive scheme for slice cooperation based on D2D communication in 5G networks

In the 5th generation (5G) wireless communication networks, network slicing emerges where network operators (NPs) form isolated logical slices by the same cellular network infrastructure and spectrum resource. In coverage regions of access points (APs) shared by slices, device to device (D2D) communication can occur among different slices, i.e., one device acts as D2D relay for another device serving by a different slice, which is defined as slice cooperation in this paper. Since selfish slices will not help other slices by cooperation voluntarily and unconditionally, this paper designs a novel resource allocation scheme to stimulate slice cooperation. The main idea is to encourage slice to perform cooperation for other slices by rewarding it with higher throughput. The proposed incentive scheme for slice cooperation is formulated by an optimal problem, where cooperative activities are introduced to the objective function. Since optimal solutions of the formulated problem are long term statistics, though can be obtained, a practical online slice scheduling algorithm is designed, which can obtain optimal solutions of the formulated maximal problem. Lastly, the throughput isolation indexes are defined to evaluate isolation performance of slice. According to simulation results, the proposed incentive scheme for slice cooperation can stimulate slice cooperation effectively, and the isolation of slice is also simulated and discussed.

CELLULAR NETWORK RESOURCE DISTRIBUTION METHODS FOR THE JOINT SERVICING OF REAL-TIME MULTISERVICE TRAFFIC AND GROUPED IOT TRAFFIC

Immense growth in the volumes and multiplicity of data to be collected in future Internet of Things (IoT) applications is one of the crucial challenges for the networking organizations as they develop from 4G+ to true 5G systems. Particularly bulk of this traffic includes complex, unstructured and varied data (Big Data) evolve from smart networking ecosystems (LTE-devices, NB-IoT devices). Although 5G offers many low power wide area technologies (Lora WAN, GSM and NB-IoT etc.), principally NB-IoT seems very promising addressing the problem because of its certain characteristics like high fault tolerance, delay tolerance, higher coverage area etc. However, due to the limited bandwidth (180 kHz) availability one of the challenges is how to efficiently use these resources to support and handle massive number of growing IoT devices, also resource management and allocation methodology between LTE and NB-IoT traffic flows. In this context, several key issues for IoT communications in 5G networks should be addressed to satisfy quality of service (QoS) provisioning. In this paper, we proposed a mathematical model for Operator Surveillance systems for sharing radio resources between LTE and NB-IoT. The model utilizes the technique of network slicing for resource management. The proposed techniques provide scenarios that aims to offer a trade-off between the two types of traffics by guaranteeing the network performance and avoiding unproductive utilization of available resources.

5G radio resource management approach for multi-traffic IoT communications

Nowadays, Internet of Things (IoT) communications have significant impact on our social life due to the exponential growth of many objects and devices creating a fully interconnected world. To bear this growth explosion of IoT communication traffics and to support the diversity of IoT applications and services, 5G network will be the suitable solution with a needed enhancements to reach emerging features such as Human-to-Human (H–H) and machine-to-machine (M–M) communications. In this context, several key issues for IoT communications in 5G networks should be addressed to satisfy quality of service (QoS) provisioning. Radio Resource Management or scheduling is an important key issue for IoT communications in 5G network. In this paper, we propose two schedulers for IoT communications based on QoS requirements. The proposed schemes give best scenarios that aim to provide a trade-off between the two types of traffics by guaranteeing the network performance and avoiding ineffective exploitation of available resources. The simulation results prove that the proposed schedulers operate efficiently in terms of maximizing the bandwidth utilisation rate which is the important challenge of the 5G radio resource management.

An efficient energy-aware routing scheme for cloud-assisted MANETs in 5G

Energy-Saving techniques for mobile communication systems has recently received a great deal of attention in Fifth Generation (5G) networks. The Device-to-Device (D2D) communication in 5G networks has increased number of users and the data transmission rate among the mobile nodes in Cloud Assisted-Mobile Ad Hoc Networks (CA-MANETs). However, the connection between these mobile nodes and peer nodes has to frequently renew due to mobility, link failure, routing overhead and low battery power. During this period, it consumes high energy in searching and linking the mobile nodes. Hence, this research work attempts to reduce the energy consumption by proposing an Energy-Efficient Cloud-Assisted Routing Mechanism (EECRM) for CA-MANETs. Energy consumption is minimized by performing fast local route recovery among mobile nodes and peer nodes. If the link failure occurs, the backup nodes are identified by neighbour nodes and these backup nodes form an overlay that provides the coverage and this overlay network consumes less energy in routing. The results obtained from this proposed routing algorithm provides better performance in terms of less energy consumption, residual energy and network lifetime when compared with other existing network models and routing protocols.

CBMoS: Combinatorial Bandit Learning for Mode Selection and Resource Allocation in D2D Systems

The complexity of the mode selection and resource allocation (MS&RA) problem has hampered the commercialization progress of Device-to-Device (D2D) communication in 5G networks. Furthermore, the combinatorial nature of MS&RA has forced the majority of existing proposals to focus on constrained scenarios or offline solutions to contain the size of the problem. Given the real-time constraints in actual deployments, a reduction in computational complexity is necessary. Adaptability is another key requirement for mobile networks that are exposed to constant changes such as channel quality fluctuations and mobility. In this article, we propose an online learning technique (i.e., CBMoS) which leverages combinatorial multi-armed bandits (CMAB) to tackle the combinatorial nature of MS&RA. Furthermore, our two-stage CMAB design results in a tight model, which eliminates the theoretically feasible but practicality invalid options from the solution space. We prototype the first SDR-based D2D testbed to verify the performance of CBMoS under real-world conditions. The simulations confirm that the fast learning speed of CBMoS leads to outperforming the benchmark schemes by up to 132%. In experiments, CBMoS exhibits even higher performance (up to 142%) than in the simulations. This stems from the adaptability/fast learning speed of CBMoS in presence of high channel dynamics which cannot be captured via statistical channel models used in the simulators.

Virtual mesh networking for achieving multi-hop D2D communications in 5G networks

In 5G networks, when D2D communications or the quality of a cellular link fails to support data transmission of a terminal, it is indispensable to exploit data transmission opportunities via multi-hop D2D communications. However, to achieve multi-hop D2D communications, two problems need to be solved: 1) A routing and packet forwarding protocol is required to enable terminals to forward data packets across multi-hop D2D or cellular links; 2) This protocol must conform to the existing specifications of D2D communications. To this end, a framework of virtual mesh networking is developed in this paper to support multi-hop D2D communications in 5G networks. More specifically, based on the 5G network architecture, routing and packet forwarding are split into two separate mechanisms in the control plane and the user plane, respectively. In the control plane, base stations collect link and topology information of terminals and then form a virtual mesh network among terminals accordingly. The routing path for end-to-end communications is then determined by a virtual mesh routing algorithm, which also takes into account terminal mobility and link failure. In the user plane, a packet forwarding mechanism is designed to deliver data packets following the routing path as determined by the routing algorithm in the control plane. This mechanism extends but also conforms to the existing specifications of D2D communications. The virtual mesh networking protocol and its underlying algorithms are evaluated via simulations. Performance results demonstrate the effectiveness of multi-hop D2D communications via virtual mesh networking.

Interference coordination for millimeter wave communications in 5G networks for performance optimization

To address the increasing data rate demands for future wireless networks, a dense deployment of base stations or access points is the most promising approach; however, doing so may cause high intercell interference (ICI). Numerous interference coordination (IC) approaches have been proposed to reduce ICI. Conducting 5G communication on millimeter wave (mmWave) bands is more complex because of its higher propagation losses and greater attenuation variance, all of which depend on environment change. Massive antenna arrays with beamforming techniques can be used to overcome high propagation loss, reduce interference, deliver performance gains of coordination without a high overhead, and deliver high network capacity with multiplex transmitters. The central challenge of a massive antenna array that uses beamforming techniques is coordinating the users and beams for each transmitter within a large network. To address this challenge, we propose a novel two-level beamforming coordination approach that partitions a large network into clusters. At the intracluster level, this approach performs intracluster coordination similar to the user selection algorithms in a multiuser multiple input and multiple output (MU-MIMO); doing this maximizes the utility function or minimizes the signal-to-interference-plus-noise ratio (SINR) function within a cluster. A dynamic time domain IC approach is employed at the intercluster level, collecting interference information for cluster-edge user equipment (UE) and allocating the UE dynamically among the clusters to reduce the intercluster interference for a switched-beam system (SBS). Simulation results show that the proposed two-level IC approach achieves a higher edge user performance or cell capacity than with the current uncoordinated/coordinated approaches.

Big Data and Machine Learning Driven Open5GMEC for Vehicular Communications

Mobile Edge Computing (MEC) is an emerging technology and an essential component of 5G networks to bring cloud services closer to users. That means data collection, storage, processing, computing, communication, and network control are implemented at network edges. MEC is expected to be able to satisfy a variety of delay-sensitive services and applications. On the other hand, the development of vehicles to everything (V2X) communication brings many requirements to future networks to guarantee full intelligence, automatic, and faster computation, management, and optimization to fulfill network QoS (quality of service) and QoE (quality of experience). To deal with those requirements, recently, software-defined networking (SDN), network functions virtualization (NFV), big data, and machine learning (ML) have been proposed as emerging technologies and the necessary tools for MEC and vehicular networks. This study aims to integrate those technologies to build a comprehensive architecture and an experimental framework for future 5G MEC called Open5GMEC. Moreover, the authors analyzed challenges and proposed relevant solutions for future vehicular communications in 5G networks. Finally, based on this framework, we successfully implemented several powerful ML-based applications for V2X such as object detection, network slicing, and migration services, which are executed at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU).

Towards an FPGA-Accelerated programmable data path for edge-to-core communications in 5G networks

The Fifth-Generation (5G) networks, as the emerging next generation mobile networks, are adopting softwarization and virtualization technologies as the cornerstones for the network operators to gain significant competitive advantages by reducing both capital and operational expenditure, enabling agile and flexible service creation and deployment, among others. Meanwhile, a virtualized and softwarized 5G network would suffer from downgraded system performance due to this unprecedented paradigm shift towards software-based networking. Addressing one of the top challenges in this context, this paper focuses on improving the performance of the data plane from the edge to the core network segment (backhaul) in a 5G multi-tenant network by leveraging and exploring the programmability introduced by software-based networking. A fully functional prototype has been designed and implemented utilizing a Field Programmable Gate Arrays (FPGAs) acceleration-based platform, and the prototyped system has been empirically tested and evaluated to demonstrate the superior performance enhancements. The proposed solution can effectively support 5G networks in delivering mission-critical or time-sensitive applications such as ultra-high definition video use cases as experimentally validated and shown in this paper, by fulfilling the strict Quality of Service (QoS) requirements imposed to the data plane.

Energy-Efficient Power Allocation and Relay Selection Schemes for Relay-Assisted D2D Communications in 5G Wireless Networks

Device-to-device (D2D) communications allows user equipment (UE) that are in close proximity to communicate with each other directly without using a base station. Relay-assisted D2D (RA-D2D) communications in 5G networks can be applied to support long-distance users and to improve energy efficiency (EE) of the networks. In this paper, we first establish a multi-relay system model where the D2D UEs can communicate with each other by reusing only one cellular uplink resource. Then, we apply an adaptive neuro-fuzzy inference system (ANFIS) architecture to select the best D2D relay to forward D2D source information to the expected D2D destination. Efficient power allocation (PA) in the D2D source and the D2D relay are critical problems for operating such networks, since the data rate of the cellular uplink and the maximum transmission power of the system need to be satisfied. As is known, 5G wireless networks also aim for low energy consumption to better implement the Internet of Things (IoT). Consequently, in this paper, we also formulate a problem to find the optimal solutions for PA of the D2D source and the D2D relay in terms of maximizing the EE of RA-D2D communications to support applications in the emerging IoT. To solve the PA problems of RA-D2D communications, a particle swarm optimization algorithm is employed to maximize the EE of the RA-D2D communications while satisfying the transmission power constraints of the D2D users, minimum data rate of cellular uplink, and minimum signal-to-interference-plus-noise-ratio requirements of the D2D users. Simulation results reveal that the proposed relay selection and PA methods significantly improve EE more than existing schemes.

Enabling Trustworthy Multicast Wireless Services through D2D Communications in 5G Networks

Device-to-device (D2D) communication is considered as one of the key enabling technologies for fifth-generation (5G) networks as it allows data offloading generated by the huge number of connected devices. In this respect, group-oriented services are among the most interesting usage scenarios. Indeed, D2D can improve the performance of the conventional multicast scheme (CMS) in cellular networks, which is known to suffer from low spectral efficiency. Security is a further key field of investigation for 5G systems, as any threat to privacy and security may lead to both deteriorated user experience and inefficient network resources’ utilization. Security issues are even more in focus for D2D connections between devices that are in mutual proximity. To improve the CMS performance and also sustain security requirements of the 5G network, this work proposes a secure D2D data transmission algorithm. Making use of mechanisms such as encryption and signature, this algorithm aims to protect the exchanged data and the privacy of the devices involved in the communication. A simulation campaign conducted using MATLAB shows the ability of the proposed solution to take advantage of the establishment of secure D2D communications and efficiently utilize network resources.

Energy-efficient DRX scheduling for D2D communication in 5G networks

In recent years, intelligent devices such as smart phones and smart tablets are grown and developed rapidly. High-bandwidth media services such as live streaming, gaming and multi-user video conferencing are more and more popular. This results in spectrum resource depletion of the cellular network. To alleviate the problem, the 3rd Generation Partnership Project (3GPP) has proposed a new technology, called device-to-device (D2D) communications. However, D2D devices and direct link cellular users all share and access the same spectrum resource, thus degrading the network performance significantly due to the interference. In addition, due to the frequent communication nature of streaming and gaming services, the power consumption of devices is increasing dramatically. To mitigate above problems, this paper investigates how to improve the spectrum efficiency by well scheduling and allocating radio resource to more concurrent D2D and cellular users on the uplink direction and reduce unnecessary power consumption of devices via Discontinuous Reception (DRX) scheduling while guaranteeing users' Quality of Service (QoS). We propose an energy-efficient resource and sleep scheduling scheme. This scheme first establishes a conflict graph to maintain the transmission and interference relationship, and then tries to maximize concurrent D2D and mobile users by resource reuse. At the same time, the method also exploits DRX technology to further decrease possible interference and conserve devices’ power consumption through optimizing their sleep operation. Simulation results show that our scheme can achieve better performance on system throughput, network capacity, and power saving compared to the existing schemes.