Generation Mobile Networks Research Articles

Systematic analysis of the principles and application scenarios of Low-Noise Amplifier

As society advances, new ideas and products are increasingly integrated into our daily lives. 5th generation mobile networks tech enables faster, lag-free connectivity, raising performance standards for components like Low-Noise Amplifiers. Summarizing Low-Noise Amplifier tech and applications helps researchers and firms leverage past knowledge and explore innovations. This paper analyzes Low-Noise Amplifier development and application, discussing types, principles, key performance indicators, and circuit structures of basic amplifiers. Five vital Low-Noise Amplifier characteristicsnoise figure, impedance matching, linearity, stability, and gainare examined. The author introduces design and application cases in radio astronomy, navigation systems, and 5th generation mobile networks communications based on Low-Noise Amplifier characteristics and performance requirements. The author concludes that advancing radio frequency tech requires improved Low-Noise Amplifiers, especially for high-frequency signals in 5th generation mobile networks. Promising noise performance enhancements come from technologies like modified high electron mobility transistors on GaAs and InP substrates. Monolithic Microwave Integrated Circuit integration reduces size while maintaining cost-effective performance. Future advancements in these areas could further boost Low-Noise Amplifier performance.

PAPR reduction using polynomial based compressing with Rapp model in GFDM

Generalized Frequency Division Multiplexing (GFDM) has been considered as an attractive candidate to replace Orthogonal Frequency Division Multiplexing (OFDM) for the fifth generation (5 G) mobile networks. GFDM system has better spectral characteristics compared to the OFDM system due to the usage of properly selected pulse shaping filters. Non-causal ideal filters, such as the raised cosine (RC), are commonly used in the GFDM systems. Here, Generalized frequency division multiplexing is a promising candidate to deploy. However, as an intrinsic property of multi-carrier modulation, the high signal amplitude fluctuation can cause several degradation effects when the signal is affected by nonlinearities. The pulse shaping filters are designed using the computationally efficient quadratic programming (QP) approach. Therefore, the aim of this paper is to evaluate the performance of Generalized frequency division multiplexing systems undergoing nonlinear amplification. We evaluate the bit error ratio using high power amplifier, namely the Rapp model. The results show that the fault rate is apparently affected according to the particular high power amplifier model and its transmission parameters.

Emerging Directions for Blockchainized 6G

The next generation of mobile networks, i.e., sixth generation (6G), is expected by 2030, with already burgeoning research efforts towards this goal. Along with various other candidate technologies, blockchain is envisioned to enable and enhance numerous key functionalities of 6G. Thus, the main objective of this paper is threefold: 1) to categorize the different aspects of 6G into four emerging directions that anticipate significant advancements leveraging blockchain, 2) to discuss the potential role of blockchainized 6G under each key emerging direction, 3) to expound on the technical challenges in blockchaining 6G along with possible solutions.

Data-driven decision-making framework for optical fronthaul slice resizing in 6G networks

The third-generation partnership introduced three main types of slices: enhanced mobile broadband, massive machine-type communication, and ultrareliable low-latency communication. To accommodate these services, the next generation of mobile networks will require architecture with distinct requirements and network slices. To implement these services on an optical fronthaul, the slices will be hosted using lightpaths. Such lightpaths will have to accommodate latency and bandwidth constraints to keep radio units (RUs) and baseband units (BBUs) synchronized. However, the traffic in a slice may vary, and the resources allocated to a long-established lightpath could be out of date, leading to waste or lack of resources. For example, a lack of bandwidth can cause desynchronization between BBUs and RUs. Therefore, the slice must be resized regularly to meet the variable demands. This work proposes a data-driven decision-making (DDDM) framework to resize the fronthaul slices while mitigating the consequences of a lack of bandwidth. The framework uses long short-term memory to implement its analytical stage and integer linear programming (ILP) to reconfigure the entire network when it is required. The results show that the DDDM-based framework outperforms the state-of-the-art ILP-based heuristic by up to 15% in terms of radio blocking mitigation.

Energy-Efficient BWP Configuration for Multi-Slice Users.

Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users' battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users' energy efficiency and quality of service.

Remote Fault Diagnosis for the Powertrain System of Fuel Cell Vehicles Based on Random Forest Optimized with a Genetic Algorithm.

To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eight typical malfunctions of its powertrain system was incorporated. Firstly, the information on the powertrain system was obtained through a 5G-based monitoring terminal, and the Alibaba Cloud IoT platform was utilized for data storage and remote monitoring. Secondly, a fault diagnosis model based on the RF algorithm was constructed for fault classification; its parameters were optimized with a genetic algorithm (GA), and it was applied on the Alibaba Cloud PAI platform. Finally, the performance of the proposed RF fault diagnosis model was evaluated by comparing it with three other classification models: random search conditioning, grid search conditioning, and Bayesian optimization. Results show that the model accuracy, F1 score, and kappa value of the optimized RF fault classification model are higher than the other three. The model achieves an F1 value of 97.77% in identifying multiple typical faults of the powertrain system, as validated by vehicle malfunction data. The method demonstrates the feasibility of remote monitoring and fault diagnosis for the powertrain system of fuel cell vehicles.

Satellite-Terrestrial Integrated 6G: An Ultra-Dense LEO Networking Management Architecture

With the rapid development of low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated networking has become a promising paradigm to provide wide coverage, high capacity and flexible services for the sixth generation (6G) mobile communication networks. However, the natural heterogeneity, high dynamics, and large scale of ultradense LEO satellite networks pose new challenges to network control and management. To this end, in this article, we propose a medium earth orbit (MEO), low earth orbit (LEO) and satellite Earth stations (SESs) integrated multi-layered management architecture for the ultra-dense LEO satellite network, where global controllers and local controllers are introduced to reduce the network management complexity. With the aid of this hybrid multi-layered management framework, we can efficiently implement the network status control, mobility management, resource management, and service management in the satelliteterrestrial integrated network. To obtain proper global and local controllers with high management efficiency in the hybrid multilayered network management architecture, we further propose a novel and efficient grouping and clustering method for the ultradense LEO satellite network, where each group manager MEO satellite and cluster head (CH) LEO satellites are respectively considered as the global and local controllers of each LEO satellite group. Numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed hybrid multilayered ultra-dense LEO networking management architecture.

Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms.

A Comprehensive Analysis of 5G Technology: Advancements, Challenges, and Implications

The advent of 5G technology marks a transformative era in telecommunications, promising unparalleled connectivity and unlocking a myriad of opportunities across various industries. This research report delves into the significance of 5G technology, exploring its potential impact on communication, industries, and society as a whole. The scope of this research encompasses a comprehensive examination of the key components, advancements, challenges, and global deployment of 5G networks. Through an in-depth analysis of use cases, applications, and emerging trends, this report aims to provide valuable insights into the future of 5G technology. The key findings of this research shed light on the revolutionary advancements in data transfer rates, low- latency communication, and the integration of Internet of Things (IoT) devices. Furthermore, the report addresses the challenges and limitations associated with 5G deployment, offering a balanced perspective on the regulatory, security, and infrastructure considerations. By exploring the implications of 5G on society, industry, and emerging technologies, this research aims to contribute to a holistic understanding of the profound changes brought forth by the fifth generation of mobile networks.

A deep learning-based authentication protocol for IoT-enabled LTE systems

The connected devices in Internet-of-Things (IoT)-enabled systems are continuously increasing nowadays, and likely to grow exponentially worldwide in near future. Hence, the next generation IoT-enabled mobile networks (e.g., 5G onward) are expected to provide higher system capacity and ultra-low latency to deal with. According to the Third Generation Partnership Project (3GPP), Long-Term Evolution (LTE) technology can serve the purpose efficiently, and also bridge the gap between earlier and future generation mobile networks. However, the network may face problems associated with privacy and security, as the underlying communication is mostly wireless. Thus, a secure and efficient Authentication and Key Agreement (AKA) protocol is desirable. Recently, many protocols have been proposed to address these goals. Unfortunately, the security and efficiency of such protocols are still in doubt. This paper introduces a deep learning-based AKA protocol for IoT-enabled LTE systems. The proposed protocol can address mutual authentication among the communicating entities. It employs a Deep Residual Network (DRN)-based key generation technique, called DRN-KeyGen, to establish a shared secret key on-the-fly among the communicating entities in such environment where the number of IoT devices are excessively large and extremely heterogeneous in nature. The security of DRN-KeyGen is verified considering various active and passive attacks that may occur in wireless-enabled LTE systems. The efficiency of DRN-KeyGen is measured through two different parameters: attack detection rate and attack detection time, where each attack is experimented using Python tool-based simulation with varied key lengths. The empirical results show that the proposed DRN-KeyGen can achieve an average detection rate of 0.924 and an average detection time of 30.634 s considering key lengths of 32 bits, 64 bits, 128 bits, 192 bits, 256 bits, and 512 bits. Finally, we have compared the security and efficiency of DRN-KeyGen with other existing protocols to show its superiority.

Federated learning for green and sustainable 6G IIoT applications

The 6th generation mobile network (6G) is expected to be launched in the early 2030s. The architecture of 6G will be the convergence of space, air, ground, and undersea networks. The power and intelligence of 6G based on advanced AI techniques will realize the concept of the Industrial Internet of Things (IIoT). In this study, we conduct a comprehensive survey of 6G IIoT applications based on Federated Learning (FL), starting from introducing recent advances in FL and IIoT systems to discussing how to integrate them. In particular, we highlight the potential of FL for supporting a range of IIoT systems such as smart medical, intelligent transportation, smart cities, unmanned aerial vehicles, and smart industry. The important discussions to drive FL into IIoT applications are emphasized. Finally, we present challenges and open issues for future research to realize green and suitable FL-based IIoT applications. Index TermsFederated Learning, Artificial Intelligence, Industrial Internet of Things, Smart IoT Applications

NVM-enhanced Machine Learning Inference in 6G Edge Computing

With the increasing popularization of smart terminals and real-time interactive applications, fast growing technical requirements push both academia and industry to look beyond 5G and conceptualize the sixth generation (6G) mobile network. Artificial intelligence (AI) with machine learning capacities at the edge is one crucial component of a 6G mobile network which makes various time-sensitive and high-stake services possible, e.g., smart security, virtual reality, self-driving vehicles. However, resource constraints, especially the memory limitation of edge servers, become major obstacles to deploying machine learning services at the edge. Fortunately, the new generation of non-volatile memory (NVM) provides new affordable memory resources that can be easily attached to existing edge servers. In this paper, we propose a novel machine learning application placement scheme using the NVM technology at the edge to reduce the end-to-end latency. Specifically, the proposed NVM-enhanced placement scheme takes into consideration the latency of various machine learning applications over NVM devices and the network. The corresponding optimization problem is exceedingly challenging, i.e., NP-hard. Therefore, we developed a novel approximation algorithm with both low computational complexity and theoretical guarantees. Experiments and extensive simulations using real-world applications highlight that our scheme provides significantly lower end-to-end latency compared with existing baselines.

Proposal of Digital Twin-Enabled New Product Design Framework

Digital twin (DT) has provided numerous opportunities for product development businesses to leverage data acquired from the physical model. Ideally, many companies are improving their use cases by using the collected physical data in a virtual model. Thanks to emerging communication technologies, 5th generation mobile network (5G), the internet of things (IoT), industrial internet of things (IIoT) sensors, etc., which help to collect data from the physical product and send data to cloud computing, called big data. With the gap in the non-availability of standard architecture, many companies are using big data and building their digital models. This paper proposes a novel DT-enabled new product design framework that has built an interaction between the DT and product design environments. This framework enables the data to be captured from various physical entities, stored in a centralized location, and pushed into the digital entity, which processes the data and converts the raw data into information used for decision-making. In the digital entity, multiple technologies are used to process data into information, such as artificial intelligence (AI) and machine learning (ML) models, mathematical models, and simulation models. The processed data that emerges from these models are called synthetic or DT data; this data is later used for control / monitoring and visualization / interaction purposes. In this paper, the data classified as visualization and interaction is integrated with the new product design framework in all four design stages, viz., concept design, detailed design, design verification, and redesign, with various data contributions to the design. A case study was discussed by applying the DT data processed using the AI / ML model in the concept design phase. Using synthetic data, the medium-duty truck frame assembly�s design concept was validated using ten load cases iteratively, and the design was finalized. The results are encouraging that the overall weight of the frame assembly was optimized by 9% without impacting the strength and factor of safety of the frame assembly.

Enabling Space-Air integration: A Satellite-UAV networking authentication scheme

One of the goals of sixth-generation mobile networks (6G) is to achieve a larger network coverage area. Satellite networks enable global coverage and aerial nodes such as Unmanned Aerial Vehicle (UAV) can serve as a supplement to ground networks in remote environments. Therefore, 6G networks are gradually evolving towards Space-Air-Ground integrated networks. The combination of UAV networks and satellite networks is a research hotspot in the field of Space-Air integrated networks. However, the combination of UAV networks and satellite networks currently faces many challenges in terms of security. The characteristics of large propagation delay and unstable communication links in satellite networks make them vulnerable to various attacks, including eavesdropping, tampering, and impersonation. Meanwhile, existing research on UAV networks mainly focuses on UAV-Ground networking authentication mechanisms, which are not suitable for resource-constrained nodes in the Space-Air integration scenario. Therefore, based on elliptic curve public key cryptography and Chebyshev polynomial, we propose a secure networking authentication scheme for satellite nodes and UAV nodes in the Space-Air integration scenario. The security analysis indicates that our scheme possesses security attributes such as mutual authentication, key agreement, identity anonymity, unlinkability, perfect forward-backward security, and resistance against various protocol attacks, among other security properties. Performance analysis also indicates certain advantages of our scheme over existing schemes in terms of signaling, bandwidth, and computational overhead.

Improved Performance of 5G Based Software Defined Networks

With the growth of processed data for wireless networks and the establishment of new applications and services, mobile operators keep searching for solutions to deal with the issues raised by nextgeneration networks (NGN). The fifth generation (5G) mobile networks, for example, should enhance their performance without consuming a lot of energy. For these reasons, software-defined networks (SDN) appear to be the promising technology that will make achieving the architectural agility required for the upcoming 5G mobile networks easier. SDN is a clever solution for providing innovation and enforcing the primary drivers in 5G mobile networks, such as flexibility, dependability, service-oriented management, and cost reduction through the control and management of the 5G core network. In this paper, the integration of SDN and multiple controllers with the 5G core network is investigated to determine how these two technologies can affect mobile IP network performance. An energy model suitable for the proposed network architecture has been developed. A widely used Diamond network is being considered for the core network. Open-Flow controllers were used to improve the routing performance by considering load balancing of IP traffic. According to such a model, reduced energy consumption is experienced with SDN. The addition of an SDN controller results in a 11% reduction in consumed energy. The results also show that the use of SDN and multi-controller with NGN can improve the performance further. Using SDN reduced the average delay of the network by 14%. A great reduction in packet error rate (PER) is also achieved when SDN is used.

6G – NETWORK OF THE FUTURE

<p class="MsoNormal" style="text-align: justify; line-height: 150%;"><span style="font-size: 12.0pt; line-height: 150%; font-family: 'Times New Roman',serif;">6G stands for the sixth generation of mobile networks, which represents the future of wireless communication. While 5G networks are still being developed and expanded around the world, the industry is already thinking about the next generation to meet the needs of the accelerated digital transformation and the demands of modern society. 6G networks will be even faster, more reliable, with lower latency and higher capacity than their predecessors. It is expected to deliver incredible data transfer speeds of terabytes per second, enabling instant downloads of large files and supporting technologies such as virtual and augmented reality, artificial intelligence, autonomous vehicles, Internet of Things (IoT) devices and other advanced applications.. One key features of 6G networks will be greater connectivity between people, machines and things, creating an intelligent network that will enable highly sophisticated communication scenarios. 6G networks are expected to provide the possibility of continuous connectivity with high quality of service, even in difficult environments such as rural areas, inside buildings or in motion. 6G networks will use multiple frequency bands, including terahertz frequencies, which will allow for greater capacity and higher data transfer speeds. It will also require advanced use of antenna systems, such as massive multiplexed antennas (Massive MIMO), in order to achieve a high degree of wireless efficiency. However, it is important to note that the 6G network is still in the research and development phase. Standardization and commercial implementation are expected only after 2030. Industry,</span></p> <p class="MsoNormal" style="text-align: justify; line-height: normal;"><strong><i><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif;">Keywords</span></i></strong><i><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif;">: 6G, networks, future, IoT, transfer, industry, standardization</span></i></p>

Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

Constrained Network Slicing Games: Achieving Service Guarantees and Network Efficiency

Network slicing is a key capability for next generation mobile networks. It enables infrastructure providers to cost effectively customize logical networks over a shared infrastructure. A critical component of network slicing is resource allocation, which needs to ensure that slices receive the resources needed to support their services while optimizing network efficiency. In this paper, we propose a novel approach to slice-based resource allocation named Guaranteed seRvice Efficient nETwork slicing (GREET). The underlying concept is to set up a constrained resource allocation game, where ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula> ) slices unilaterally optimize their allocations to best meet their (dynamic) customer loads, while ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$</tex-math> </inline-formula> ) constraints are imposed to guarantee that, if they wish so, slices receive a pre-agreed share of the network resources. The resulting game is a variation of the well-known Fisher market, where slices are provided a budget to contend for network resources (as in a traditional Fisher market), but (unlike a Fisher market) prices are constrained for some resources to ensure that the pre-agreed guarantees are met for each slice. In this way, GREET combines the advantages of a share-based approach (high efficiency by flexible sharing) and reservation-based ones (which provide guarantees by assigning a fixed amount of resources). We characterize the Nash equilibrium, best response dynamics, and propose a practical slice strategy with provable convergence properties. Extensive simulations exhibit substantial improvements over network slicing state-of-the-art benchmarks.

Base Station Switching and Resource Allocation for 5G Heterogenous Networks

Enhanced 4th Generation Wireless Network (4G) is called 5th Generation Wireless Network (5G) as it helps in increasing the data rate, capacity and therefore energy efficiency and spectral efficiency of 5G network, in 5G massive MIMO, multiple numbers of antennas are used to transmit the signal with same timefrequency to maximise the number of users, who can communicate with less number of channels. Energy conception is the most dangerous issue in all the generations of wireless networks such as traditional firstgeneration to fifth-generation because of interference, eco signals, fading, packet loss, wastage of bandwidth, remaining energy and security like malicious attacks, blocking whole attacks and wormhole attacks. This efficient research work focused energy-efficient resource allocation scheme based on the shortest job first scheduling algorithm in wireless network (SJF) for the downlink orthogonal frequency division multiple accesses (OFDMA) heterogeneous networks (HetNets) developed. To maximize the spectrum allocation efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G HetNets. In this paper, the energy-efficient optimization problem based on coordinated scheduling is formulated, which is a shortest path problem and link breakage is intractable to solve directly. The above proposed model was analysed using different parameters energy, bandwidth, Quality of Service (QoS) and interference.

A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links

AbstractCommercial microwave link (CML) has become one of the most widely used opportunistic sensors for rainfall monitoring. The high density and coverage of CMLs enable us to monitor near‐ground rainfall in high spatial resolution. The empirical power‐law (PL) model proposed in 1978 is the leading approach in relating CML attenuation with rain rate. However, while giving good rain rate estimates for CMLs longer than 1 km, in short CMLs, large errors are observed when the PL approach is applied. In this paper, we studied a statistical approach to convert CML attenuation measurement to rainfall intensity. The proposed approach calculates the exceedance probability, p, of rain‐induced attenuation and derives the corresponding rainfall intensity based on the rain rate cumulative distribution curve. Data from two cities in two countries were used to validate the approach. The results of the proposed approach were compared with that of the PL model. Results show that the models derived using our approach outperform PL for short CMLs, while showing similar performance for long ones. Since in the next generation mobile networks short CMLs will become more and more dominant, our work provides a way to derive retrieval models for the future generation CML networks.