5G Cellular Networks Research Articles

Power Optimization in Device to Device Communications Underlying 5G Cellular Networks

Power Optimization in Device to Device Communications Underlying 5G Cellular Networks

Design and optimization of structured cables systems for automation of production processes of the enterprise

The object of research is the process of improving the network architecture of enterprises in order to further optimize production processes. The subject of research is the design and optimization of structured cabling systems to automate the production processes of the enterprise. The aim of the work is to improve the method of designing and optimizing structured cabling systems for the transport needs of the cellular network for the needs of the enterprise. The study determined that the process of planning a radio network to automate production processes of the enterprise must be performed in accordance with the following sequence of steps: design of radio network coverage to determine the location of each base station (5G - gNb) and construction of communication transport segment with location cross-country premises. In this paper, research is conducted on the construction of a communication transport segment with the location of cross-country premises. For this purpose, the scope of the statistical method is determined and the choice of the location of the technical premises of the cross floor is substantiated. Conclusions. The research conducted in this paper allowed to develop methods for improving the network architecture of enterprises in order to further optimize production processes. As part of this, a 5G network planning method was developed to automate the production processes of the enterprise, which is to consistently ensure the design of the radio network coverage to determine the location of each base station using an optimized model to estimate the loss of radio signal power. bandwidth, number of connections and reliability and construction of the communication transport segment with the determination of the optimal location of cross-country premises. The developed method makes it possible to plan the optimal structure of the 5G cellular network to optimize production processes, evaluate and reduce the total cost of building the network, while providing the necessary indicators of service quality of network nodes and its reliability.

Energy and Delay Guaranteed Joint Beam and User Scheduling Policy in 5G CoMP Networks

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.

Hardware impairments aware full-duplex non-orthogonal multiple access networks over Nakagami-m channels

Non-orthogonal multiple access (NOMA) and full-duplex (FD) relaying communications are promising candidates for 5G cellular networks. In this paper, by exploiting the impact of hardware impairment, we study FD NOMA communications with a downlink scheme. In a group of two users, we find that the target rates and power allocation strategies are main factors affecting the system performance metric. We derive the closed-form formula of outage probability for two users. As main contribution, numerical results are considered to illustrate the performance of the FD NOMA. We also study the base station (BS) can adjust its transmit signal to noise ratio (SNR) to achieve relevant outage probability in several scenarios.

Quality of Service based resource allocation in D2D enabled 5G-CNs with network slicing

With the rapid new advancements in technology, there is an enormous increase in devices and their versatile need for services. Fifth-generation (5G) cellular networks (5G-CNs) with network slicing (NS) have emerged as a necessity for future mobile communication. The available network is partitioned logically into multiple virtual networks to provide an enormous range of users’ specific services. Efficient resource allocation methods are critical to delivering the customers with their required Quality of Service (QoS) priorities. In this work, we have investigated a QoS based resource allocation (RA) scheme considering two types of 5G slices with different service requirements; (1) enhanced Mobile Broadband (eMBB) slice that requires a very high data rate and (2) massive Machine Type Communication (mMTC) slice that requires extremely low latency. We investigated the device-to-device (D2D) enabled 5G-CN model with NS to assign resources to users based on their QoS needs while considering the cellular and D2D user’s data rate requirements. We have proposed a Distributed Algorithm (DA) with edge computation to solve the optimization problem, which is novel as edge routers will solve the problem locally using the augmented Lagrange method. They then send this information to the central server to find the global optimum solution utilizing a consensus algorithm. Simulation analysis proves that this scheme is efficient as it assigns resources based on their QoS requirements. This scheme is excellent in reducing the central load and computational time.

Cognitive radio based spectrum sharing models for multicasting in 5G cellular networks: A survey

The dramatic growth of smart devices catering content-centric data traffic has fueled the need for group communication services in cellular networks, among which multicasting is of particular interest. The unique potential of multicasting to fulfill the vision of a hyper-connected society in 5G networks is underlined by its ability to manage resources efficiently, while supporting concurrent transmission of common data to a large number of users. The bandwidth intensive communication requirements of multimedia multicast applications demand optimal usage of the spectral resources. Moreover, massive capacity, connectivity and ultra low latency requirements of the content centric applications have triggered the urge to get the content nearer to the users and employ short range communication, thus leading to densification of cellular networks. However, the limited availability of radio resources coupled with elevated levels of interference pose a major hindrance towards enabling spectrum reuse in dense networks. Cognitive radio (CR) is a viable technology to enhance the spectral efficiency, by allowing low priority unlicensed users to share the spectrum of high priority licensed users. The ability of CR to adapt its transmission parameters according to the application demands makes it a promising candidate for efficient spectrum management and interference mitigation in 5G cellular networks. This paper presents a survey on the state-of-the art techniques proposed for catering multicast services in CR networks. The study provides a detailed analysis on the utility of various spectrum sharing models of CR networks in fulfilling the end-to-end communication goals of multicast services. We particularly emphasize on the nature of multicast traffic, the key enabling techniques and methodologies employed for enhancing the efficacy of such services and approaches for mitigating interference experienced by the licensed users. By carefully analyzing the existing works, we provide possible design guidelines for multicast services in future 5G networks. Finally, we identify few open issues and discuss the future research directions.

The Open Challenge of Integrating Satellites into (Beyond-) 5G Cellular Networks

The 3rd Generation Partnership Program (3GPP) standardization has recognized the relevance of integrating a non-terrestrial (i.e., sat-ellite-based) segment into the ubiquitous 5G (terrestrial) cellular network. Butsuch integration has actually proven considerably slower than expected. This article starts with a review of the recent history of satellite mobile communications and surveys the current status of satellite Networks and the related technologies, including high throughput satellites with large antenna arrays and mega constellations. Such discussion is used as the basis for the identification of technological gaps that are making the cited integration an open challenge. Since terrestrial networks are already targeting 6G, this article tries to project the current 5G gaps toward the beyond 5G horizon of 2030.

Toward Advanced UAV Communications: Properties, Research Challenges, and Future Potential

In recent times, communication and connectivity requirements have been evolving rapidly, and it is evident with the recent deployment of fifth-generation (5G) cellular networks worldwide. Similarly, unmanned aerial vehicles (UAVs) have gained interest in several applications, including surveillance, monitoring, geographic mapping, and logistics, among others. Therein, 5G cellular network support for UAVs is gaining both attention and momentum within various regulatory bodies and consortiums. While 5G is gaining momentum to establish necessary communication technologies for UAV operations, we provide a state-of-the-art overview emphasizing the technical challenges of applying millimeter-wave technology to support UAV communication. This article provides a closer look at the key enabling technologies and their potential issues impacting UAV operation under a unifying 5G network and beyond. In addition, this article also discusses the security issues impacting network privacy and safety vulnerabilities of UAVs. To conclude, we identify several open research issues that drive UAV deployment and operations and demand further study.

Harvest-on-Sky: An AoI-Driven UAV-Assisted Wireless Communication System

The 5G cellular network provides a sufficient data rate to support the ever growing number of devices that will be connected to the Internet. To enable Internet of Things (IoT) applications in remote locations, it is very crucial to increase the coverage of the cellular networks. This article proposes a wireless communication system that exploits a full-duplex enabled unmanned aerial vehicle (FD-UAV) integrated into a communication network as a mobile relay to expand network coverage. With the deployment of IoT devices, a large volume of data is introduced into the network, causing a delay in data delivery due to scarcity of resources. Real-time information updates play an important role in modern IoT applications including connected vehicles in intelligent transportation systems, digital healthcare, real-time tracking, and so on. Decision making based on the latest fresh information is critically important for these applications. We quantify the freshness of data in IoT applications using a new performance metric, the age of information (AoI). This work proposes a new mathematical model to estimate the average AoI (AAoI) in the FD-UAV assisted cooperative wireless communication system. Here, sensors located in a transport infrastructure harvest energy from radio frequency (RF) signals transmitted by the FD-UAV via a wireless power transmission RF energy harvesting (EH) technique. Then sensors consume this energy to transmit their sensed real-time observations to the access point with the aid of FD-UAV. A closed-form expression for the AAoI is derived as a function of time allocated for energy harvesting. The optimal time allocation for EH that minimizes the AAoI is identified together with the effect of self-interference experienced by the UAV.

DeepSecure: Detection of Distributed Denial of Service Attacks on 5G Network Slicing—Deep Learning Approach

Network slicing is one of the main enablers of the fifth-generation (5G) cellular network. However, it is susceptible to security threats such as distributed denial of service (DDoS) attacks. A DDoS attack on a slice could lead to the exhaustion of available common resources and a breach of the availability of resources on the slices. Recent works such as statistical, machine learning and cryptography techniques are limited by the requirement to define thresholds, feature engineering constraints and computation overload, respectively. In this letter, we propose DeepSecure, a framework based on a Long Short Term Memory deep learning technique that detects user equipment (UE) network traffic as DDoS attack or normal traffic and assigns an appropriate slice to a legitimate UE request. We compared our work with existing machine learning and deep learning techniques used in the literature. Experiment results showed that our proposed framework performed better in detecting DDoS attacks with an accuracy of 99.970% and predicting the appropriate slice requested by legitimate UE with an accuracy of 98.798%.

Combined Communication and Computing Resource Scheduling in Sliced 5G Multi-Access Edge Computing Systems

The fifth generation (5G) cellular networks aim to deliver data with low/ultra-low latency to users and support diverse services which require two main types of resources: communication and computing resources. These motivated the development of the Multi-access Edge Computing (MEC) paradigm because it can execute tasks’ computation closer to users to reduce latency. To satisfy quality of services (QoS) requirements of different applications, both communication and computing resources in 5G MEC systems can be hard partitioned or soft partitioned among multiple network slices where each slice supports one or more services. A typical soft partition approach is using scheduling. Furthermore, packets in the multi-resource system can either be scheduled individually for each resource using a discrete resource scheduling approach or scheduled collectively only once by combined resource scheduling. In this paper, we propose a combined resource scheduler, called Extended Weighted Fair Queueing with Latency Constraint (EWFQ/LC), to schedule packets among network slices subject to system fairness and their latency constraints. By exploiting the virtual finish time feature inherent in weight fair queueing, EWFQ/LC schedules packets according to the fairness when their latency constraints can be met while according to these latency constraints when they are possibly violated. Based on simulations of realistic heterogeneous traffic scenarios, we show that EWFQ/LC reduces latency by as much as 73% compared to scheduling each resource discretely and maintains latency satisfaction ratio above 90%. More importantly, EWFQ/LC is able to accomplish these improvements with greater fairness in resource consumption especially under heavy traffic conditions, i.e., traffic with stricter latency constraints do not excessively over-consume resources of other traffic classes.

Efficient Seamless Handover Mechanism and Mobility Management for D2D Communication in 5G Cellular Networks

Efficient Seamless Handover Mechanism and Mobility Management for D2D Communication in 5G Cellular Networks

Multi-tier delay-aware load balancing strategy for 5G HC-RAN architecture

The fifth-generation (5G) cellular network aim to provide ultra-low latency and enhance data rate service to its associated users. The centralized baseband processing of incoming traffic increases the computational complexity and end-to-end delays in a conventional C-RAN architecture. This paper proposes a multi-tier HC-RAN architecture using the edge computing concept at the Remote Radio Head (RRH) to overcome the above limitations. Further, to reduce load at the C-BBU and fronthaul, we have proposed a multi-tier delay-aware load balancing (MDALB) algorithm. The proposed algorithm is run over the EO to strategically distribute incoming flow between eRRH and C-BBU based on a load distribution ratio. The efficiency of the proposed strategy is analyzed through mathematical models and simulations based on various performance matrices. The performance parameters’ results show that the use of multi-tier processing in HC-RAN minimizes end-to-end delay and packet loss whereas increases the system throughput and packet delivery ratio.

Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications.

One of the main targets of future 5G cellular networks is enlarging the Internet of Things (IoT) devices’ connectivity while facing the challenging requirements of the available bandwidth, power and the restricted delay limits. Unmanned aerial vehicles (UAVs) have been recently used as aerial base stations (BSs) to empower the line of sight (LoS), throughput and coverage of wireless networks. Moreover, non-orthogonal multiple access (NOMA) has become a bright multiple access technology. In this paper, NOMA is combined with UAV for establishing a high-capacity IoT uplink multi-application network, where the resource allocation problem is formulated with the objective of maximizing the system throughput while minimizing the delay of IoT applications. Moreover, power allocation was investigated to achieve fairness between users. The results show the superiority of the proposed algorithm, which achieves 31.8% delay improvement, 99.7% reliability increase and 50.8% fairness enhancement when compared to the maximum channel quality indicator (max CQI) algorithm in addition to preserving the system sum rate, spectral efficiency and complexity. Consequently, the proposed algorithm can be efficiently used in ultra-reliable low-latency communication (URLLC).

Flexible Liquid Crystal Polymer Technologies from Microwave to Terahertz Frequencies.

With the emergence of fifth-generation (5G) cellular networks, millimeter-wave (mmW) and terahertz (THz) frequencies have attracted ever-growing interest for advanced wireless applications. The traditional printed circuit board materials have become uncompetitive at such high frequencies due to their high dielectric loss and large water absorption rates. As a promising high-frequency alternative, liquid crystal polymers (LCPs) have been widely investigated for use in circuit devices, chip integration, and module packaging over the last decade due to their low loss tangent up to 1.8 THz and good hermeticity. The previous review articles have summarized the chemical properties of LCP films, flexible LCP antennas, and LCP-based antenna-in-package and system-in-package technologies for 5G applications, although these articles did not discuss synthetic LCP technologies. In addition to wireless applications, the attractive mechanical, chemical, and thermal properties of LCP films enable interesting applications in micro-electro-mechanical systems (MEMS), biomedical electronics, and microfluidics, which have not been summarized to date. Here, a comprehensive review of flexible LCP technologies covering electric circuits, antennas, integration and packaging technologies, front-end modules, MEMS, biomedical devices, and microfluidics from microwave to THz frequencies is presented for the first time, which gives a broad introduction for those outside or just entering the field and provides perspective and breadth for those who are well established in the field.

Transceiver Design and Power Control for Full-Duplex Ultra-Reliable Low-Latency Communication Systems

Ultra-reliable low-latency communication (URLLC) is one of the most important components in the fifth generation (5G) cellular networks for realizing mission-critical applications. In this paper, we jointly optimize the transceiver design and decoding error probability (DEP) of a full-duplex (FD) URLLC system, where the base station (BS) operates in FD mode, while the uplink (UL) and downlink (DL) users work in half-duplex (HD) mode. Accordingly, an optimization problem is formulated to maximize the achievable total (UL plus DL) rate for an FD URLLC system under finite blocklength, subject to the end-to-end (E2E) reliability constraint from the UL user to each DL user and the total transmission power constraint at the UL user and at the BS. We analyze the problem structure and convexify the problem by approximating the channel dispersion in scenarios of high and mid-to-high signal-to-interference plus noise ratio (SINR) regimes, respectively. Next, efficient iterative algorithms are proposed to find the near-optimal power allocation for the UL user and transceiver weights for the BS. Furthermore, closed-form expressions of the transceiver weights are derived, and the convergence of the proposed algorithms is proved. Simulation examples demonstrate the impact of the code blocklength, number of DL users, transmitter/receiver distortion and DEP threshold on the system performance.

HiveMind: Towards Cellular Native Machine Learning Model Splitting

The increasing processing load of today’s mobile machine learning (ML) application challenges the stringent computation budget of mobile user equipment (UE). With the wide deployment of 5G edge-cloud, a new ML offloading scheme called split ML is provisioned to enable computation-intensive mobile ML applications by splitting an ML model across mobile UE, edge, and cloud. However, the complex split assignment problems pose new challenges for split ML system design. In this paper, we introduce HiveMind, the first practical multi-split ML system tailored for 5G cellular networks. HiveMind reformulates the complicated multi-split problem to a min-cost graph search and optimizes the distributed algorithm to drastically reduce the signaling overhead. Benefit from its low overhead property, HiveMind makes the optimal split decision on multiple computing nodes in real-time and adapts the split decisions to the instantaneous network dynamics. HiveMind also incorporates a multi-objective mechanism that accommodates heterogeneous objectives for a single ML task. HiveMind adapts to a wide range of ML frameworks, including non-linear models like Recurrent Neural Network (RNN), Federated Learning (FL), and Multi-agent Reinforcement Learning (MARL). We evaluate HiveMind on 5G MEC network simulators with realistic traffic patterns and real-life MEC computation/communication profiles. Our experiments demonstrate that HiveMind achieves the optimal efficiency comparing to state-of-art split ML designs.

Password-Guessing Attack-Aware Authentication Scheme Based on Chinese Remainder Theorem for 5G-Enabled Vehicular Networks

The new fifth-generation (5G) cellular networks dramatically improve the speed of message transmissions. Most existing authentication schemes that secure 5G communication rely heavily on the vehicle’s tamper-proof device (TPD) and roadside units (RSUs) to store the system’s master key. However, it only takes a single compromised TPD to render the whole system insecure. We propose a password-guessing attack-aware authentication scheme based on the Chinese Remainder Theorem (CRT) to secure inter-vehicle communication on 5G-enabled vehicular networks to address this issue. The trusted authorities (TAs) in the proposed scheme generate and broadcast new group keys to the vehicles assisted by CRT. Moreover, since the system’s master key does not need to be preloaded, the proposed scheme only requires realistic TPDs. The proposed scheme overcomes password-guessing attacks and guarantees top-level security for entire 5G-enabled vehicular networks. The security analysis indicates that the proposed scheme is secure against adaptive chosen-message attacks under the random oracle model and meets the security requirements of a 5G-enabled vehicular network. Since cryptographic operations based on elliptic curve cryptography are employed, the performance evaluation shows that the proposed scheme outperforms the eight existing schemes in terms of computation and communication costs.

Introducing a Novel Data Over Voice Technique for Secure Voice Communication

The current increasing need for privacy-preserving voice communications is leading to new ideas for securing voice transmission. This paper refers to a relatively new concept of sending encrypted data or speech as pseudo-speech in the audio domain over existing voice communication infrastructures, like 3G cellular network and Voice over IP (VoIP). The distinctive characteristic of such a communication system is that it relies on the robust transmission of binary information in the form of audio signal. This work presents a novel Data over Voice (DoV) technique based on codebooks of short harmonic waveforms. The technique provides a sufficiently fast and reliable data rate over cellular networks and many VoIP applications. The new method relies on general principles of Linear Predictive Coding for voice compression (LPC voice coding) and is more versatile compared to solutions trained on exact channel models. The technique gives by design a high control over the desired rate of transmission and provides robustness to channel distortion. In addition, an efficient codebook design approach inspired by quaternary error correcting codes is proposed. The usability of the proposed DoV technique for secure voice communication over cellular networks and VoIP has been successfully validated by empirical experiments. The paper details the system parameters, putting a special emphasis on system's security and technical challenges.

5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication

In the past few years, mobile data traffic has seen exponential growth due to the emergence of smart applications. Although throughput enhancement techniques such as macro- and femtocells reduce cell size, they are relatively expensive to implement. Mobile device-to-device (D2D) communication has emerged as a solution to support the growing popularity of multimedia content for local service in next-generation 5G cellular networks. Content sharing is the prominent feature, which helps D2D communication in reducing offload traffic on the network, improving the energy efficiency of the device, and reducing backhaul connectivity costs. In traditional mapping approaches such as one to one or one to many, a massive amount of traffic is distributed among the devices resulting in high-energy consumption. In this paper, we propose a novel energy-efficient content sharing scheme called Energy-Efficient Collaboration-based Content (EECC) sharing strategy in D2D communication that shares content equally across devices based on their capacities and battery life under mobility. The proposed work includes cluster formation, cluster head selection, and helper node selection. In addition, we relied on a cooperative caching policy to ensure that content is distributed efficiently. The simulation results indicate a 12.05% reduction in energy compared to the state-of-the-art technique with a 2-gigabyte video file. To evaluate scalability, we increased the file size from 3 to 4 gigabytes, yet the performance in terms of energy consumption remained the same.