Current 4G Research Articles

A comparative measurement study of cross-layer 5G performance under different mobility scenarios

The 5G technology is expected to revolutionize various applications with stringent latency and throughput requirements, such as augmented reality and cloud gaming. Despite the rapid 5G deployment, it is still a puzzle whether current commercial 5G networks can meet the strict requirements and deliver the expected quality of experience (QoE) of these applications. Especially in mobile scenarios, as user mobility (e.g., walking and driving) plays a critical role in both network performance and application QoE, it becomes more challenging to provide high performance stably and continuously. To solve this puzzle, in this paper, we present a comprehensive cross-layer measurement study of current commercial 5G networks under five mobility scenarios typically seen in our daily lives. Specifically, under these mobility scenarios, we cover (1) the impact of physical layer metrics on network performance, (2) general network performance at the network layer, (3) comparison of four congestion control algorithms at the transport layer, and (4) application QoE at the application layer. Our measurement results show that the achievable network performance and application QoE under current commercial 5G networks falls behind expectations. We further reveal some insights that could be leveraged to improve the QoE of these applications under mobility scenarios.

Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme

Machine learning is taking on a significant role in materializing a new vision of 6G. 6G aspires to provide more use cases, handle high-complexity tasks, and improvise the current 5G and beyond 5G infrastructure. Artificial Intelligence (AI) and machine learning (ML) are the optimal candidates to support and deliver these aspirations. Traffic steering functions encompass many opportunities to help enable new use cases and improve overall performance. The emergence and advancement of the non-terrestrial network is another driving factor for creating an intelligence selection scheme to have a dynamic traffic steering function. With service-based architecture, 5G and 6G are data-driven architectures that use massive transactional data to emerge a new approach to handling highly complex processes. A highly complex process, a massive volume of data, and a short timeframe require a scheme using machine learning techniques to resolve the challenges. In this paper, the study creates a scheme to use the massive historical data and provide a decision scheme that enables dynamic traffic steering functions addressing the future emergence of the heterogeneous transport network and aligns with the Open Radio Access Network (O-RAN). The proposed scheme in this paper gives an inference to be programmed in the telecommunication nodes. It provides a novel scheme to enable dynamic traffic steering functions for the 6G transport network. The study shows an appropriate data size to create a high-performance multi-output classification model that produces more than 90% accuracy for traffic steering functions.

Wixor: Dynamic TDD Policy Adaptation for 5G/xG Networks

In the era of 5G and beyond, dynamic Time Division Duplex (TDD) has become essential for supporting applications that demand high bandwidth and low latency. Emerging uplink-intensive use cases such as real-time video analytics, autonomous vehicles and augmented reality further complicate the balance between uplink and downlink resources. Despite their potential, TDD policies employed by current 5G networks remain underexplored. Our investigation reveals that existing TDD policies are static and predominantly downlink-focused, failing to adapt to fluctuating network demands. We introduce Wixor, a robust dynamic TDD policy adaptation system tailored for 5G and next-generation (xG) networks. It proactively adjusts the allocation of TDD resources between uplink and downlink, addressing various practical challenges. Prototyped on a programmable testbed, Wixor demonstrates substantial performance improvements across diverse applications, achieving up to 96.5% enhancement in Quality of Experience (QoE) compared to existing baselines.

Using 5G Standards for Smart Healthcare Applications and Designing an Artificial Intelligence‐Based Industry 4.0 Communication System

ABSTRACTThe introduction of Industry 4.0, to improve Internet of Things (IoT) standards, has sparked the creation of 5G, or highly sophisticated wireless networks. There are several barriers standing in the way of 5G green communication systems satisfying the expectations for faster networks, more user capacity, lower resource consumption, and cost‐effectiveness. 5G standards implementation would speed up data transmission and increase the reliability of connected devices for Industry 4.0 applications. The demand for intelligent healthcare systems has increased globally as a result of the introduction of the novel COVID‐19. Designing 5G communication systems presents research problems such as optimizing resource usage, managing mobility, ensuring cost‐efficiency, managing interference, and maximizing spectral efficiency. The fast advancement of artificial intelligence (AI) in several domains yields improved performance in contrast to traditional methods. Hence, including AI in 5G standards would enhance performance by catering to diverse end‐user applications. Initially, we provide an overview of concepts such as Industry 4.0, the 5G standard, and recent developments in the sphere of wireless communications in the future. The goal is to use 5G technology to look at current research problems. We present a new architecture for Industry 4.0 and 5G‐compliant smart healthcare systems. We develop and run the proposed model to investigate the current 5G methods using the Network Simulator (NS2). The results of the simulation show that 5G resource management and interference management approaches already in use face challenges including performance trade‐offs.

Ultra-Reliable and Low-Latency Wireless Hierarchical Federated Learning: Performance Analysis.

Wireless hierarchical federated learning (WHFL) is an implementation of wireless federated Learning (WFL) on a cloud-edge-client hierarchical architecture that accelerates model training and achieves more favorable trade-offs between communication and computation. However, due to the broadcast nature of wireless communication, the WHFL is susceptible to eavesdropping during the training process. Apart from this, recently ultra-reliable and low-latency communication (URLLC) has received much attention since it serves as a critical communication service in current 5G and upcoming 6G, and this motivates us to study the URLLC-WHFL in the presence of physical layer security (PLS) issue. In this paper, we propose a secure finite block-length (FBL) approach for the multi-antenna URLLC-WHFL, and characterize the relationship between privacy, utility, and PLS of the proposed scheme. Simulation results show that when the eavesdropper's CSI is perfectly known by the edge server, our proposed FBL approach not only almost achieves perfect secrecy but also does not affect learning performance, and further shows the robustness of our schemes against imperfect CSI of the eavesdropper's channel. This paper provides a new method for the URLLC-WHFL in the presence of PLS.

Lightweight Certificate-Less Anonymous Authentication Key Negotiation Scheme in the 5G Internet of Vehicles

In the current 5G vehicle network system, there are security issues such as wireless intrusion, privacy leakage, and remote control. To address these challenges, an improved lightweight anonymous authentication key negotiation scheme based on certificate-less aggregate signatures is proposed and its security and efficiency are analyzed. The result shows that the scheme can offer security attributes including anonymity, traceability, and revocability, as well as effective identity authentication, and it can resist forgery attacks, man-in-the-middle attacks, tampering attacks, and smart card loss attacks. Moreover, compared with similar schemes, it possesses superior security and more efficient computational efficiency and less communication overhead, thereby being more appropriate for high-speed, large-capacity, low-latency, and resource-constrained 5G vehicle network application scenarios.

5G enabled smart cities: A real-world evaluation and analysis of 5G using a pilot smart city application

Ubiquitous sensing in smart cities is expected to be one of the key beneficiaries of the high bandwidth and low latency capabilities of 5G. However, current 5G deployments still have low population coverage, and are unlikely to reach global coverage of above 80% before 2028. This means that new smart city applications are likely to experience a combination of 4G and 5G, limiting their data-intensive capabilities. Thus, it is necessary to assess the ability of current 5G deployments to support emerging smart city applications, and how they perform in 5G environments. Existing performance evaluations focus either on the 5G core or use instantaneous speed tests, which do not effectively assess the suitability of 5G deployments for smart city applications. In this paper, we present a comprehensive evaluation and analysis of real-world 5G network performance observed through the outcomes of a pilot smart city application, an innovative mobile 5G Internet of Things (IoT) solution to automatically identify and report road assets requiring maintenance. The pilot smart city application was deployed on 11 waste collection service trucks over a 6 month period (June 2022–Dec 2022). We undertook both application-specific and application independent network performance evaluation to assess the ability of the 5G deployment to support uninterrupted smart city services. Our analysis shows that while 5G is capable of supporting mobile video streaming applications, there are significant variations in network performance, which may make it unsuitable for applications that require higher data intensive or near real-time responsiveness.

Machine learning approach of multi‐RAT selection for travelling users in 5G NSA networks

AbstractThe rapid increment of mobile device usage and the corresponding huge data volume generated afterwards, necessitated the utilisation of the 5G network spectrum. This is deployed today in terrestrial communication in a non‐stand‐alone (NSA) architectural mode; where 5G networks are supported by 4G LTE networks. Hence, the current 5G implementation with the gargantuan number of mobile subscribers, poses challenges to the choice of network Radio Access Technology (RAT) selection between 4G and 5G networks, among available multiple base‐stations to mobile (travelling) users, with respect to their location, bandwidth requirement, and mobility style. Hence, to address the scenario presented above, the authors record live signal measurements of 4G and 5G networks by a travelling user, that transversed multiple 5G NSA base stations. RAT selection implementations were carried out with support vector machine (SVM), deep neural network (DNN), and eXtreme Gradient Boosting (XGBoost) algorithms to select an appropriate RAT between 4G and 5G RATs, for effective resource allocation for travelling users’ requirements. Evaluation of results with standard classification metrics shows XGBoost with overall outstanding accuracy performance at 99.64%.

Adaptive radio propagation model for maximizing performance efficiency in smart city disaster management application

Climate change poses several environmental threats like floods to urban environment; thus, effective and reliable communication of emergency information is needed during massive breakdown of network infrastructure. This paper presents a mobile adhoc network (MANETs) based effective information such as calls, image, and videos communication system that is compatible with current 3GPP and 5G communication network. Here in maintaining connectivity the information is communicated between different MANET nodes in a multi-hop manner. However, designing radio propagation is challenging considering higher local emergency request congestion at different terrain with varying speed of users. The current radio propagation model is designed without considering the effect of line-of-sight between communicating device and are not adaptive to different environment considering urban disaster management environment. This paper develops an adaptive radio propagation (ARP) model namely expressway, city and semiurban. Then, in reducing congestion and improving network performance efficiency the work introduced an adaptive medium access control (AMAC) protocol. The MAC incorporates a dynamic network controller (DNC) to optimize the contention window size in dynamic manner according to current traffic demands. The AMAC protocol achieves much improved throughput with lesser packet loss in comparison with existing MAC (EMAC) model considering different radio propagation model introduced in this work.

A series‐fed high‐quality linear array antenna for 5G maritime communications

AbstractA compact series‐fed high‐quality linear array antenna for 5G maritime communication is proposed. Building upon the wideband high‐efficiency electromagnetic radiation structure (WHEMS), a linear integrated series‐fed method is proposed, enhancing applicability in maritime environments. A self‐decoupling structure without the parasitic elements is introduced, resulting in a 7 dB increase in isolation while achieving array spacing of less than half the wavelength. The antenna has an impedance bandwidth of 19.9% (676–825 MHz) to cover the maritime 700 MHz application band, a high gain of 13–14.2 dBi, and a smaller volume compared to classical Yagi arrays with the same gain level. Furthermore, it exhibits characteristics such as low wind load, cost‐effectiveness, and high voltage resistance. The proposed antenna will play a vital role in the construction of the current 5G maritime communication network.

Signal Propagation and Path- Loss in 6G Mobile Telecommunication System

As previous we know about many types of generation like (2G,3G and 4G network), and also it has been developed to the fifth – generation mobile communication system. Compare with 5G, 6G will be faster and lower latency, in 6G era, the theoretical network speed will reach to 1Tbps. That is 100 times the speed of the 5G, which is 10,000 times the speed of the current 4G. 5G is officially commercialized in 2019, but 5G has no yet covered a large area and have problems with high tariffs and incomplete network coverage ,6G network makes up for these problems. The 6G is through the integration of ground base stations and satellite communications, thus covering the whole world, it is also the real coverage. But also, there’s some issues with 6G networks, like here in this article I will explain the propagation signal for 6G and I will talk about path loss signals because it uses sub-millimeter waves or tera-hertz waves to make faster connection but it will cause so much issues in propagation filed. And explained how to address path-loss challenge.

Transforming Vehicular Networks: How 6G can Revolutionize Intelligent Transportation?

Vehicular Ad-hoc Networks (VANETs) have enabled intelligent transportation systems by facilitating communication between vehicles and roadside infrastructure. However, the current 5G and 4G networks that support VANETs have certain limitations that hinder the full potential of VANET applications. These limitations include constraints in bandwidth, latency, connectivity, and security. The upcoming 6G network is expected to revolutionize VANETs by introducing several advancements. 6G will provide ultra-fast communication with significantly reduced latency, enabling real-time and high-bandwidth data exchange between vehicles. The network will also offer highly reliable and secure connectivity, ensuring the integrity and privacy of VANET communications. Precise localization and sensing capabilities will be enhanced in 6G-based VANETs, enabling accurate positioning of vehicles and improved situational awareness. This will facilitate collision avoidance, traffic management, and cooperative driving applications. Moreover, integrating edge computing in 6G networks will bring computing resources closer to the edge, lowering response times and facilitating faster decision-making in time-critical scenarios. This paper explores the key features and capabilities of 6G technology and how it can revolutionize intelligent transportation, addressing challenges and opportunities for adopting 6G in VANETs.

Terahertz Radiation – the Dawn of a New Information Era

This mini-review article explores the evolution of wireless communication technologies, emphasizing the crucial role played by advancements in information transfer over long distances with minimal errors. Tracing developments from submergible cables to the current 5G technology, the review discusses the potential of 6G, offering insights into the transformative applications it could enable, such as holographic real-time communication and autonomous transport infrastructure. Exploring the applications of Terahertz (THz) frequencies, the article presents the limitations of current wireless speeds and proposes solutions, including the use of spintronic terahertz emitters (STEs) and antiferromagnetic materials, concluding on the challenges and prospects of integrating these technologies into practical applications.

Attention-based deep learning approach for CSI feedback under 5G TDL channel

In 5G communication systems, accurate channel state information (CSI) is indispensable for signal detection and regulation at the base station side. However, frequent CSI feedback from users leads to excessive system overhead. To tackle this challenge, this paper puts forward a novel deep learning framework - HCNet based on attention mechanism and autoencoder, aiming to efficiently compress and reconstruct the high-dimensional time-varying CSI matrices in an end-to-end approach. The proposed framework incorporates a self-attention module in the encoder to explicitly capture global dependencies within the CSI matrix. Meanwhile, the decoder adopts gated recurrent unit networks to fully exploit inter-feature correlations and redundancy. To evaluate the performance, simulations are conducted using datasets conforming to current 5G time-varying TDL channel models. Results demonstrate superior performance over existing deep learning based feedback networks. Specifically, the proposed framework can reduce the normalized mean square error of CSI reconstruction by 4 dB under various compression ratios which confirms the effectiveness of the attention-enhanced autoencoder structure for compressive CSI sensing and feedback in practical dynamic communication systems.

Security Vulnerabilities in 5G Non-Stand-Alone Networks: A Systematic Analysis and Attack Taxonomy

5G networks, pivotal for our digital mobile societies, are transitioning from 4G to 5G Stand-Alone (SA) networks. However, during this transition, 5G Non-Stand-Alone (NSA) networks are widely used. This paper examines potential security vulnerabilities in 5G NSA networks. Through an extensive literature review, we identify known 4G attacks that can theoretically be applied to 5G NSA. We organize these attacks into a structured taxonomy. Our findings reveal that 5G NSA networks may offer a false sense of security, as most security and privacy improvements are concentrated in 5G SA networks. To underscore this concern, we implement three attacks with severe consequences and successfully validate them on various commercially available smartphones. Notably, one of these attacks, the IMSI Leak, consistently exposes user information with no apparent security mitigation in 5G NSA networks. This highlights the ease of tracking individuals on current 5G networks.

Artificial Intelligence and Energy Efficiency of 5G Radio Access Network

Purpose: This paper is a pioneering study that investigates the integration of Artificial Intelligence (AI) to enhance energy efficiency in 5G Radio Access Networks (RANs). This paper aims to identify AI-driven strategies that can significantly optimize energy consumption in the rapidly evolving 5G network infrastructure, which is essential for meeting the increasing demand for high-speed connectivity.
 Methodology: The methodology used for this research is a detailed review and analysis of the 5G RAN architecture and its energy dynamics, alongside the exploration of AI applications in optimizing network operations. The study focuses on AI techniques such as resource allocation, traffic prediction, adaptive sleep modes, and fault detection, proposing a holistic approach to energy management in 5G networks. A key contribution of this research is its in-depth examination of AI's role in 5G energy efficiency, highlighting its practical implications and potential for future applications. The paper offers novel insights into the implementation of AI in real-world 5G scenarios and addresses the challenges in transitioning from theoretical models to practical solutions.
 Findings: The findings reveal that AI integration is a vital step towards reducing the environmental footprint of 5G networks, with AI-based solutions showing promise in enhancing efficiency beyond the inherent capabilities of current 5G technologies. Despite many AI applications being in nascent stages, their potential impact on energy efficiency is significant.
 Unique contributor to theory, policy and practice: This paper is a valuable guide for researchers, industry professionals, and policymakers in telecommunications and environmental sustainability. It provides a clear roadmap for leveraging AI in 5G networks, emphasizing the synergy between technological innovation and ecological responsibility.

The 5G revolution: Tackling challenges in smart cities and intelligent transportation systems

<div><p>The use of cutting-edge technology and data systems to increase the effectiveness, safety, and environmental responsibility of transport networks is known as intelligent transportation systems (ITS). In order to improve many elements of transportation, ITS incorporates a wide range of technologies, including sensors, communication networks, data analytics, and robots. By addressing the shortcomings of the current 4G network, the next mobile technology, 5G, challenges the electronic communication environment as it stands. By allowing a large number of concurrent connections and networks ubiquitous, even in high mobility scenarios or densely inhabited places, such Smart trains and smart cities (SC) are made possible by cutting-edge technology a new approach of becoming fully integrated. According to this strategy; 5G will enable the true Internet of Things (IoT) and its related automobiles on the World Wide Web (WWW). This conversation attempts to thoroughly explain how 5G wireless networks will impact urban smart transport networks, including semi-autonomous or self-driving cars, and automotive communication over the coming years, as well as any technical, economical, and regulatory issues.</p></div>

GPU versus FPGA implementation of a digital predistortion linearizer for wideband radiofrequency power amplifiers

This paper presents and compares the implementation of a digital predistortion (DPD) linearizer for radiofrequency power amplifiers (PAs) considering two different hardware acceleration platforms. Both graphics processing unit (GPU)-based and field programmable gate arrays (FPGA)-based implementations of the DPD are capable to meet the high throughput requirements for linearizing the PA with current 5G new radio (NR) wideband signals. A comparison in terms of hardware resources usage, precision, throughput and linearization performance is provided for both GPU and FPGA implementations of a DPD based on the generalized memory polynomial (GMP) behavioral model.

QoS Management for XR Traffic in 5G NR: A Multi-Layer System View and End-to-End Evaluation

Extended reality (XR) applications are gaining a lot of momentum in the industry and consumer market. XR applications are characterized by multiple simultaneous data flows with different Quality-of-Service (QoS) requirements, imposing the need for advanced QoS management in 5G networks. Compared to 4G, the 5G NR technology has been designed with enhanced QoS support, by introducing the concept of QoS flows and an additional layer for QoS handling. However, recent studies show that additional QoS enhancements are needed in 5G to support the massive adoption of XR services. 3GPP envisions to include some enhancements for XR traffic in 5G-Advanced. In this paper, we review the current 5G QoS framework and study potential design improvements to fulfill XR QoS requirements in future cellular networks. We study QoS control procedures for XR traffics at different layers of the 5G NR protocol stack, including the proper configuration of radio access and core networks. In particular, we analyze various architectures for handling the peculiarities of XR, such as time-constraint and multi-flow applications. Also, we present XR traffic adaptation mechanisms at the application layer, to properly adapt XR traffic statistics to the actual 5G radio environment, and QoS schedulers for the MAC layer, leading to cross-layer performance optimization between XR application and 5G radio access network. Finally, we provide a validation study and end-to-end evaluation of QoS control mechanisms by simulating realistic mixed traffic scenarios in a 5G NR system-level simulator based on ns-3.

A 5G multi-gNodeB simulator for ultra-reliable 0.5–100 GHz communication in indoor Industry 4.0 environments

Industry 4.0 is evolving towards new challenging applications with tight reliability constraints. While simulation tools for the characterization of the physical and radio features of production environments would be very useful, there is a lack of awareness about radio propagation in the presence of 5G technologies. In this context, this paper presents a simulator, based on the 3rd Generation Partnership Project (3GPP) TR 38.901 report, able to describe the physical layer, channel model, and channel modulation applied in 5G. The simulator takes as input the physical characteristics of the considered area and the objects inside of it and provides, with a two-layered approach, the time series of Block Error Rate (BLER) values according to the simulated propagation paths in the presence of moving objects followed by a reliability analysis of the gNodeBs with respect to the user position computed through statistical and geometrical calculations. Through our simulator, we have discovered that the current 5G technology systems may not be sufficient to meet the high reliability demands in safety applications in Industry 4.0 systems. This finding emphasizes the need for additional solutions to enhance the reliability of 5G technology in industrial settings.