Future 5G Networks Research Articles

Iron-Oxide Magnetic Materials for Millimeter-Wave Absorption

High-performance electromagnetic-wave absorbers are required for the control of millimeter-wave spectra, which will play a big role in future 5G and 6G wireless networks. Traditional absorbing materials comprised of metals or soft ferrites have been developed but their lack of ability to absorb at extremely high frequencies continues to hinder their practical applications. Thus, this article briefly introduces several iron-oxide magnetic materials with millimeter-wave absorbing capability.

Hygienic assessment of the impact of 5G/IMT-2020 communication networks on public health (literature review)

The program “Digital Economy of the Russian Federation” approved the Concept for the creation and development of 5G / IMT-2020 networks. The development of 5G communications will significantly impact the implementation of many innovative projects and initiatives: the Smart City project, Unmanned Transport, etc. Along with significant technical advantages compared to previous generations of communication (2G, 3G, 4G), 5G technology has completely different emitting characteristics: more emitting elements, signal modulation, three-dimensional beam, the ability to control the beam, SHF (ultra-high) and EHF (extremely high) radio frequency ranges and centimetre and millimetre wavelengths of electromagnetic radiation. Therefore, it is becoming an especially urgent problem to ensure exposure to the human body of non-ionizing electromagnetic fields of the radio frequency range (30 kHz-300 GHz). The authors searched the literature on the biological effects of 5G cellular communications and electromagnetic radiation in the centimetre and millimetre ranges using the appropriate keywords in PubMed search engines, Scopus, Web of Science, Medline, The Cochrane Library, EMBASE, Global Health, CyberLeninka, RSCI and others. There is currently tentative and conflicting evidence on the impact of 5G. The rapidly growing density of wireless devices and antennas (considering future 5G networks) increases the public health risk from exposure to RF EMFs as the penetration depth for 5G EHF radiation is only a few millimetres. At these wavelengths, resonance phenomena are possible at the cellular and molecular levels, particularly concerning stimulating SHF and EHF oxidative processes and damaging DNA. The influence of the millimetre range of RF-EMF is poorly understood; oncological and non-oncological (impact on the reproductive, immune systems, etc.) effects are possible. Using numerical simulation methods of EMF radiation resonances on insects, Thielens A et al., 2018, found a significant overall increase in the absorbed RF power at a frequency of 6 GHz and higher than a frequency below 6 GHz.

Energy Efficiency Concerns and Trends in Future 5G Network Infrastructures

Energy efficiency is a huge opportunity for both the developed and the developing world, and ICT will be the key enabler towards realising this challenge, in a huge variety of ways across the full range of industries. In the telecommunications space in particular, power consumption and the resulting energy-related pollution are becoming major operational and economical concerns. The exponential increases in network traffic and the number of connected devices both make energy efficiency an increasingly important concern for the mobile networks of the (near) future. More specifically, as 5G is being deployed at a time when energy efficiency appears as a significant matter for the network ability to take into account and to serve societal and environmental issues, this can play a major role in helping industries to achieve sustainability goals. Within this scope, energy efficiency has recently gained its own role as a performance measure and design constraint for 5G communication networks and this has identified new challenges for the future. In particular, the inclusion of AI/ML techniques will further enhance 5G’s capabilities to achieve lower power consumption and, most importantly, dynamic adaption of the network elements to any sort of energy requirements, to ensure effective functioning.

Wireless Power Transfer for 6G Network Using Monolithic Components on GaN

A novel architecture for Wireless Power Transfer (WPT) module usingmonolithic components on GaN is presented in this paper. The design ofsuch a WPT module receives DC power from solar panels, consists ofphotonic power converter (PPC), beamforming antenna, low pass filter,input matching network, rectifier, output matching network and logic circuit(off-chip) which are all integrated on a GaN chip. Our WPT componentsshow excellent simulated performance, for example, our novel beamforming antenna and multiple port wideband antenna have a gain of 8.7 dBand 7.3 dB respectively. We have added a band pass filter to the rectifieroutput which gives two benefits to the circuit. The first one is filteringcircuit will remove unwanted harmonics before collecting DC power andsecond is filter will boost the efficiency of rectifier by optimizing the loadimpedance. Our proposed rectifier has RF-DC conversion efficiency of74% and 67% with beam-forming antenna and multiple port wide bandantenna respectively. Our WPT module is designed to charge a rechargeablebattery (3 V and 1 mA) of a radio module which will be used between twoantennas in future 5G networks. We believe our proposed WPT modulearchitecture is unique and it is applicable to both microwave and millimeterwave systems such as 6G.

Evaluating the impact of delay constraints in network services for intelligent network slicing based on SKM model

Solving the problem of network resource allocation with delay constraint is a significant challenge for realizing future Internet and 5G networks services such as advanced mobile broadband services and Internet of things (IoT), especially under the network slicing scenario. The impact of delay constraints may lead to rejection of demands, resulting in low resource utilization of network resources. This is especially severe when dynamic traffic is considered. Therefore, intelligent resource allocation algorithms are required to use the network resources in delay constrained scenario efficiently. Moreover, these algorithms should guarantee quality of service (QoS) between different priority slices during congestion case. Therefore, in this paper, we analyze the impact of delay constraint on the performance of an online resource allocation algorithm based on an intelligent efficient squatting and kicking model (SKM), proved in other works to be the most effective up to the present time yet. SKM incorporates kicking and squatting of resources as innovative techniques enabling it to achieve 100% resource utilization and acceptance ratio for higher priority slices in scenarios where the other state of art algorithms not able to reach by far in some scenarios. Simulation results showed that incorporating delay constraints has a significant impact on the performance, resulting in up to 10% and 4% reduction in terms of average resource utilization and acceptance ratios respectively.

Multi-criteria handover mobility management in 5G cellular network

To fulfill the future demand and expansion of the coverage of the network, ultra-dense deployment of small cell (SC) is an optimal solution for future 5G networks, which will ensure the UEs (User Equipment) continuous connectivity. However, these small cells (SCs) lead to the issue of interference, additional unnecessary handover (HO), signaling overhead, and which in turn decreases the overall quality of service (QoS) of the users. In this paper, an intelligent mobility management system based on Enhanced Multi-Objective Optimization Method by Ratio Analysis (E-MOORA) and Q-learning approach is introduced for handover optimization. E-MOORA method is the combination of modified entropy weighting technique and Multi-Objective Optimization Method by Ratio Analysis (MOORA) which introduces vector normalization. The proposed E-MOORA method judicially exploits the performance parameters and thus reduces ranking abnormality when it selects a HO target cell. Q-learning approach is applied to select the optimal triggering points to minimize the effect of frequent unnecessary handovers for satisfying user QoS requirements. The performance analysis results depict significant performance improvement in terms of minimizing the unnecessary HO, radio link failure, and user throughput compared to other existing Multi-Criteria Decision Making (MCDM) methods.

Novel Wake-up Scheme for Energy-Efficient Low-Latency Mobile Devices in 5G Networks

Improved mobile device battery lifetime and latency minimization are critical requirements for enhancing the mobile broadband services and user experience. Long-term evolution (LTE) networks have adopted discontinuous reception (DRX) as the baseline solution for prolonged battery lifetime. However, in every DRX cycle, the mobile device baseband processing unit monitors and decodes the control signaling, and thus, all instances without any actual data allocation leads to unnecessary energy consumption. This fact together with the long start-up and power-down times can prevent adopting frequent wake-up instants, which, in turn, leads to considerable latency. In this work, a novel wake-up scheme is described and studied, to tackle the trade-off between latency and battery lifetime in future 5G networks, seeking thus to facilitate an always-available experience, rather than always-on. Analytical and simulation-based results show that the proposed scheme is a promising approach to control the user plane latency and energy consumption, when the device is operating in the power saving mode. The aim of this article is to describe the overall wake-up system operating principle and the associated signaling methods, receiver processing solutions and essential implementation aspects. Additionally, the advantages compared to DRX-based systems are shown and demonstrated, through the analysis of the system energy-efficiency and latency characteristics, with special emphasis on future 5G-grade mobile devices.

End-to-end experimentation of a 5G vertical within the scope of blended learning

The next generation of mobile networks, widely known as 5G, was designed to respond to the next 10 years’ communication challenges. 5G will be an essential structure on which all types of electronic communications will rely. Built on the emerging 5G technology, we developed a vertical solution for blended learning environments within a Portuguese national project scope. The project aims to propose an integrated demonstration of a set of products capable of being part and providing services in the future 5G networks framework. To this end, we adopted a bottom-up approach to design and develop a product named 5GOpenclasses, where we leveraged FIWARE middleware to manage all entities. This paper presents the architecture, technological platform, associated data structures, and end-user applications of 5GOpenclasses. We also present the design of innovative location-based service for blended learning environments. This paper is the first step of the proposed product towards its quantitative evaluation when running Over-the-Top on both 4G and 5G networks. Although successful unit tests were carried out in what concerns the functional outcome, the integration tests for quantitative results depend on the availability of other project components.

Stochastic Dosimetry Assessment of the Human RF-EMF Exposure to 3D Beamforming Antennas in indoor 5G Networks

The deployment of near future 5G networks will introduce modifications in the population’s exposure levels to radio-frequency electromagnetic fields (RF-EMFs). The present work aimed to face the challenge of studying the exposure variability in the presence of an access point (AP) at 3.7 GHz with 64 patch elements uniform planar array antenna and 3D beamforming capability. The novelty introduced in the methodology of the exposure’s evaluation was the combining of traditional computational methods with a new approach based on stochastic dosimetry, called polynomial chaos kriging method, in order to estimate the exposure levels for 1000 different antenna beamforming patterns with low computational efforts. The simulations were evaluated considering a child model and computing the specific absorption rate (SAR) in different tissues. The analysis of the results highlighted a high exposure variability scenario depending on the beamforming patterns of the array antenna and identified the ranges of elevation and azimuth angles of the main antenna beam that may cause the highest levels of exposure.

Data Authentication and Anonymization in IoT Scenarios and Future 5G Networks Using Chaotic Digital Watermarking

Future Internet-of-Things (IoT) scenarios and applications are envisioned to be supported by emerging 5G networks. In this context, complex routing schemes for pervasive infrastructures are highly simplified, as every hardware element may stablish its own communication link with a 5G base station. However, this situation also introduces new risks, especially in the security field where innovative cyber-physical attacks and distributed denial of service attacks are becoming more popular and dangerous each day. Thus, data authentication, protection and anonymization in those new applications and schemes is a key challenge to be addressed. Besides, most devices in future IoT systems will be resource constrained, so traditional solutions based on private keys stored in devices' memory and computationally heavy cryptographic algorithms will turn unsecure, inefficient or, directly, impossible to run. Therefore, in this paper we propose a new mechanism to protect, authenticate and anonymize data in IoT systems supported by future 5G networks. The proposed solution employs both digital watermarking techniques and lightweight cryptographic technologies. To generate keys in a secure and simple manner, physical unclonable functions are employed. Besides, to reduce as much as possible the computational cost of algorithms, chaotic dynamics will be considered. In order to evaluate the performance of the proposed solution an experimental validation based on simulation techniques is also carried out.

Wideband Reconfigurable Millimeter-Wave Linear Array Antenna Using Liquid Crystal for 5G Networks

The advanced design of a 10 × 1 linear antenna array system with the capa-bility of frequency tunability using GT3-23001 liquid crystal (LC) is pro-posed. The design for this reconfigurable wideband antenna array for 5G ap-plications at Ka-band millimeter-wave (mmw) consists of a double layer of stacked patch antenna with aperture coupled feeding. The bias voltage over LC varies from 0 V to 10.6 V to achieve a frequency tunability of 1.18 GHz. The array operates from 25.3 GHz to 33.8 GHz with a peak gain of 19.2 dB and a beamwidth of 5.2&#176 at 30 GHz. The proposed reconfigurable antenna ar-ray represents a real and efficient solution for the recent and future mmw 5G networks. The proposed antenna is suitable for 5G base stations in stadiums, malls and convention centers. It is proper for satellite communications and radars at mmw.

5G Multi-Band Numerology-Based TDD RAN Slicing for Throughput and Latency Sensitive Services

This paper extensively examines the impact of the numerology schemes on a sliced TDD radio access network. The slices are multiplexed over both the sub-6 GHz and mmWave bands. The incorporation of numerology schemes into network slicing, the impact of which has yet to be explored, will be highly instrumental in realizing the future 5G and 6G networks. Thus, this work demonstrates the enhanced capabilities of sliced networks utilizing numerology. To that end, two optimization problems are formulated, one to maximize the downlink average spectral efficiency of a throughput-sensitive slice, and the other to minimize the uplink transmission power of the users of the latency-sensitive slice. This work optimizes key parameters such as duplex ratio, numerology scheme and optimally allocates power and bandwidth under numerology-enabled slicing. The comparative analyses highlight that for high-throughput applications, lower numerology schemes are significantly more spectrally efficient and can often achieve throughputs characteristic of higher schemes. On the other hand, the higher schemes are the best choice for the minimization of user transmission power and latency, which are crucial in energy-efficient communications and edge computing applications, respectively.

Throughput Prediction using Machine Learning in LTE and 5G Networks

The emergence of novel cellular network technologies, within 5G, are envisioned as key enablers of a new set of use-cases, including industrial automation, intelligent transportation, and tactile internet. The critical nature of the traffic requirements ranges from ultra-reliable communications, massive connectivity, and enhanced mobile broadband. Thus, the growing research on cellular network monitoring and prediction aims for ensuring a satisfied user-base and fulfillment of service level agreements. The scope of this study is to develop an approach for predicting the cellular link throughput of end-users, with a goal to benchmark the performance of network slices. First, we report and analyze a measurement study involving real-life cases, such as driving in urban, sub-urban, and rural areas, as well as tests in large crowded areas. Second, we develop machine learning models using lower-layer metrics, describing the radio environment, to predict the available throughput. The models are initially validated on the LTE network and then applied to a non-standalone 5G network. Finally, we suggest scaling the proposed model into the future standalone 5G network. We have achieved 93% and 84% R^2 accuracy, with 0.06 and 0.17 mean squared error, in predicting the end-user's throughput in LTE and non-standalone 5G network, respectively.

Applications of Deep Learning for Millimeter Wave

Millimeter Wave (mm-wave) has been considered as significant importance in various communication systems. It has achieved a greater attention to meet the capacity requirement of the future 5G network. Since mm-wave has a high frequency (30 to 300 GHz) using orthodox technologies for mm wave is more challenging. Thus advanced technology i.e. Deep Learning (DL) is a pragmatic approach to analyze a massive amount of data. Firstly, to find out how DL has beaten traditional approaches, this review briefly explores, the different methods of DL for mm wave are. Secondly, the review of the multiple applications in mm wave such as beam and blockages prediction, beam spacing, beamforming for mm wave OFDM system, precoding for mm-wave, channel estimation for mm-wave, sparse channel estimation, and hybrid precoding and fingerprinting-based indoor localization with mm wave is concisely explained. Last but not least, several studies have proved that DL has superior efficiency for mm wave than conventional approaches.

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This article gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

Performance evaluation of wireless data traffic in Mm wave massive MIMO communication

<span>Due to the evaluation of mobile devices and applications in the current decade, a new direction for wireless networks has emerged. The general consensus about the future 5G network is that the following should be taken into account; the purpose of thousand-fold system capacity, hundredfold energy efficiency, lower latency, and smooth connectivity. The massive multiple-input multiple-output (MIMO), as well as the Millimeter wave (mm Wave) have been considered in the ultra-dense cellular network (UDN), because they are viewed as the emergent solution for the next generations of communication. This article focuses on evaluating and discussing the performance of mm Wave massive MIMO for ultra-dense network, which is one of the major technologies for the 5G wireless network. More so, the energy efficiencies of two kinds of architectures for wireless backhaul networks were investigated and compared in this article. The results of the simulation revealed some points that should be considered during the deployment of small cells in the two architectures UDN with backhaul network capacity and backhaul energy efficiency, that the changing the frequency bands in Distribution approach gives the same energy efficiency reached to 600 Mb/s at 15 nodes while the Conventional approach results reached less than 100 Mb/s at the same number of nodes.</span>

QoEMultiSDN: Management of Multimedia Services using MPTCP/SR in Softwarized and Virtualized Networks

5G networks are set to empower new user experiences for multimedia streaming services by offering high data rates and low latency. The rapid growth of multimedia services, such as video streaming services over future softwarized 5G networks, poses great challenges for both service providers and network operators regarding service provisioning with high Quality of Experience (QoE). Network evolution towards Software Defined Networking (SDN) and Network Function Virtualization (NFV) provides new opportunities to develop innovative ways to address the challenges. In this paper, we present a novel QoE-aware management scheme based on SDN/NFV by utilizing Multi-Path-TCP/Segment-Routing (MPTCP/SR) techniques and exploring information from both the network and client. The new scheme, entitled QoEMultiSDN, provides an optimized end-to-end QoE for multimedia service delivery. QoEMultiSDN employs two novel strategies to achieve this: (a) QoE-based multisource routing and QoE optimization, and (b) multipath protection and dynamic link-failure-free mechanisms. We implemented the proposed scheme over SDN through Dynamic Adaptive Streaming over HTTP (DASH) experiments using Mininet, POX and OpenDaylight controllers, and compared its performance with MPTCP and regular TCP. Experimental results indicate that the QoEMultiSDN outperforms others in terms of system throughput, failure recovery time and the QoE of the end-user.

Broadband Millimeter-Wave Textile-Based Flexible Rectenna for Wearable Energy Harvesting

Millimeter-wave (mmWave) bands, a key part of future 5G networks, represent a potential channel for RF energy harvesting, where the high-gain antenna arrays offer improved end-to-end efficiency compared with sub-6-GHz networks. This article presents a broadband mmWave rectenna, the first rectenna realized on a flexible textile substrate for wearable applications. The proposed novel antenna’s bandwidth extends from 23 to 40 GHz, with a minimum radiation efficiency of 67% up to 30 GHz, over 3-dB improvement compared with a standard patch. A stable gain of more than 8 dB is achieved based on a textile reflector plane. The antenna is directly connected to a textile-based microstrip voltage doubler rectifier utilizing commercial Schottky diodes. The rectifier is matched to the antenna using a tapered line feed for high-impedance matching, achieving broadband high voltage sensitivity. The rectifier has a peak RF–dc efficiency of 12% and a 9.5-dBm 1-V sensitivity from 23 to 24.25 GHz. The integrated rectenna is demonstrated with more than 1.3-V dc output from 12 dBm of mmWave wireless power across a 28% fractional bandwidth from 20 to 26.5 GHz, a 15% half-power fractional bandwidth, and a peak output of 6.5 V from 20 dBm at 24 GHz.

Protecting Physical Communications in 5G C-RAN Architectures through Resonant Mechanisms in Optical Media.

Future 5G networks are characterized by three basic ideas: enhanced mobile broadband communications, massive machine-type communications, and ultra-low-latency communications. Any of these requirements needs, to be fulfilled, the implementation of high-efficiency technologies at all levels. This includes some of the costliest mechanisms in terms of computational time and bitrate: information protection solutions. Typical techniques in this area employ complex algorithms and large protocol headers, which strongly reduces the effective baud rate and latency of future 5G networks and communications. This is especially relevant in the access network, which in 5G networks will follow a cloud-based architecture, where thousands of different devices must communicate, before aggregating all those streams to be sent to the backbone. Then, new and more efficient mechanisms are needed in the cloud radio access networks (C-RAN) for future 5G systems. Therefore, in this paper it is proposed a novel information protection scheme for C-RAN architectures based on resonant phenomena in optical fibers communicating the fronthaul and backhaul in 5G networks. Resonant structures and physical nonlinearities generate a chaotic signal which may encrypt and hide at physical level every communication stream in a very efficient manner. To evaluate the proposed mechanism, an experimental validation based on simulation techniques is also described and results discussed.

An improved water-filling algorithm based on power allocation in network MIMO

Inter-Cell Interference (ICI) could affect the performance of the LTE-Advanced (LTE-A) system and can be evaluated according to the user’s Signal to Interference and Noise Ratio (SINR). Owing to this, the power and frequency allocation schemes can be utilized in order to attain an optimum trade-off within the obtained SINR and the resulting interference. In this study, power allocation was integrated into the Dynamic Fractional Frequency Reuse (DFFR) scheme and Network MIMO (NetMIMO) to reduce ICI and enhance the performance of LTE-A downlink. Moreover, this study focuses on the design and development of a novel power allocation algorithm, which is an extension to the previous Improved Water-Filling algorithm (IWF), namely, Low-Complexity Improved Water-Filling algorithm (LC-IWF). Results indicated a reduction in computations and enhancement in interference rejection performance in comparison to the existing algorithms by removing the Lagrange multiplier and water level. Moreover, simulation results revealed that the proposed algorithm enhances the throughput and fairness of up to 20% and 25%, respectively. Furthermore, the power consumption was reduced by 41% and the running time was 5–10 times faster than the IWF algorithm while attaining the best solution. The proposed power allocation algorithm provided a significant improvement in the performance of the NetMIMO LTE-A system, which can be extended for future 5G networks.