Search

The sixth generation (6G) networks demand high security, low latency, and highly dependable standards and capacity. One of the essential components of 6G networks is flexible wireless network slicing. In this paper, we propose a hybrid model that combines a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM). The hybrid model is applied to the Unicauca IP Flow Version2 dataset. The CNN handles the automated feature section, while the BiLSTM is utilized for categorizing the suitable network slices. This hybrid model is capable of offering a reliable and effective network slice to the end user. The proposed hybrid model has an overall recognition rate of 97.21%, which reflects the applicability of the proposed approach. A stratified 10-fold cross-validation is used to assess the applicability of the proposed model. The main challenge for network service providers is to assign slices correctly. A clever method is needed to make a standard for accurately assigning network slices to an unidentified device when it asks for them. For each incoming request for new traffic, the proposed model forecasts the suitable network slice [ABSTRACT FROM AUTHOR]

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis. [ABSTRACT FROM AUTHOR]

Sixth-generation (6G) wireless networks are anticipated to undergo trials and installations as early as 2030, offering unprecedented capacity, dependability, and efficiency. However, attention is shifting towards the development of 6G networks to meet the demands of emerging applications. The transition to 6G brings new challenges, particularly in the realm of intrusion detection, where the sophistication of attacks necessitates advanced security solutions. To eliminate this challenge, a novel Hybrid Intrusion DEtection system for the 6G network (HIDE-6G) has been proposed to detect intrusion in the 6G network. The proposed method leverages advanced techniques such as Principal Component Analysis (PCA) for dimensionality reduction, a Spotted Hyena Optimization Algorithm for feature selection, and a Capsule Network-based Deep Autoencoder (CapsDA) for effective anomaly detection. The performance of the HIDE-6G is estimated using the NSL-KDD and CICIDS 2019 datasets, demonstrating superior results compared to existing techniques such as AD6GN, IDSoft, and LA-HLRW. According to the comparison analysis, the proposed HIDE-6G technique's detection rate is 6.10%, 22.27%, and 20.7% greater than the existing HADES-IoT, H3SC-DLIDS, and F-BIDS techniques respectively. [ABSTRACT FROM AUTHOR]

The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era. [ABSTRACT FROM AUTHOR]

The evolution of science has given rise to many technologies that have changed the world. The upcoming Six-Generation (6G) mobile network indicates a fundamental transformation in wireless technologies, enhancing connectivity and data transmission rates. In this circumstance, Mobile Edge Computing (MEC) is a paradigm technology that emerges as a key major supporter of enhancing mobility awareness. Edge computing offers improved efficiency for service migration from the edge node to the user. However, mobility management in MEC is a complex challenge as seamless handovers between edge nodes must be efficiently executed to ensure uninterrupted service for mobile devices, demanding intricate coordination and low-latency decision-making. To the best of the author's knowledge, there has been no comprehensive work on the most recent developments in mobility awareness using mobile edge computing in 6G. However, this paper aims to present a general overview of the intersection between mobility awareness and MEC over 6G networks. The general concept of MEC in 6G mobile networks is comprehensively introduced. This will highlight the integration between MEC and 6G for bringing more efficient network and service migration to the edge, reducing latency, and enhancing the user experience. Meanwhile, this survey discusses augmented reality with MEC applications. This survey discusses the integration of mobility awareness and mobile edge computing in upcoming mobile applications and emphasizes the need for 6G networks. This integration results in providing seamless communication during handovers between the serving base station and the target base station. This study contributes to understanding the upcoming trends that will enable the operation of mobility awareness and MEC operation in the 6G mobile communication. Furthermore, we delve into a comprehensive overview of the challenges and future research directions for mobility management with MEC in 6G mobile networks, underlining the complexities and potentials of integrating mobility awareness and mobile edge computing.

In response to the diverse business needs of the future in all domains and scenarios, 6G networks need to provide scenario-based and personalized service capabilities. Aiming at the problem of quality assurance of fine-grained business services in the future, a joint optimization algorithm for 6G network task offloading and fine-grained slice resource scheduling was proposed, which jointly considered the calculation offloading of multiple MECs and the resource scheduling of network slices, and minimized the execution delay and energy consumption cost of the task within limited resources. Then the A3C reinforcement learning algorithm of asynchronous training was used to solve it. The simulation results show that, compared with the traditional algorithm, the proposed algorithm can reduce the computing cost while meeting the business needs of users. Additionally, the algorithm converges fast and can realize fast decision-making.

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimization is to maximize the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimization.

The transition to 6th Generation (6G) cellular networks offers significant improvements over 5th Generation (5G), enhancing data transfer, reducing latency, and improving network reliability. Advanced Multiple-Input Multiple-Output (MIMO) technology in 6G boosts network efficiency, particularly benefiting Ultra-Reliable Low-Latency Communications (URLLC). This paper reviews literature on resource management in the Internet of Things (IoT) within the 6G context. We categorize the study into four segments: network-aware resource management, dynamic resource allocation, predictive resource distribution based on traffic and architecture, and energy-centric resource allocation considering IoT device mobility and location. We provide a detailed perspective on current research and highlight future research avenues. Key contributions include a comparative study of IoT resource management techniques, an overview of resource management across LTE, 5G, and 6G networks, insights into applications like Intelligent Transportation Systems (ITS), Industrial IoT (IIoT), and Mobile CrowdSensing (MCS), and an emphasis on upcoming challenges. We emphasize the crucial role of efficient resource management in IoT, particularly in the 6G landscape.

In the realm of wireless communications, accurate Radio Signal Received Power (RSRP) prediction serves as the foundation for improving user experience and optimizing network efficiency and reliability. With the deep integration of Artificial Intelligence (AI) technology and the wireless communication network, Federated Learning (FL) is considered as a promising approach for enhancing RSRP prediction while protecting user data privacy in the upcoming of 6G network. However, in practice, the heterogeneity of User Equipment (UE) environments and the limitations of UE communication bandwidth and computational capabilities can lead to poor model performance and inefficient model interactions in FL. To address these challenges, this paper proposes a Multi-head DNN based FL algorithm for RSRP prediction. The experimental results show that the proposed algorithm can enhance both RSRP prediction performance and communication efficiency.

The exponential growth of the fifth-generation (5G) network gives businesses and universities a chance to turn their attention to the next-generation network. It is widely acknowledged that many IoT devices require more than 5G to send various types of data in real-time. In addition to 5G, several research centres are currently concentrating on 6G, which is expected to produce networks with great quality of service (QoS) and energy efficiency. Future application requirements will necessitate a significant upgrade in mobile network architecture. 6G technologies offer larger networks with lower latency and faster data transmission than 5G networks. This review presents a comprehensive overview of 6G networks which includes the novel architectural changes within 6G networks, recent research insights from diverse institutions, applications within the realm of 6G networks, and the key features associated with them. We also explored various technologies of 6G networks encompassing terahertz, visible light connectivity, blockchain, and symbiotic broadcasting, all of which contribute to the establishment of robust and socially integrated network structures. In this survey, we have focused on 6G network slices and discussed a detailed exploration of security and privacy concerns regarding the potential 6G technologies at the levels of physical infrastructure, connecting protocols, and service provisions, alongside an evaluation of current security strategies.

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sixth generation (6G) represents the next evolution in wireless communication technology and is currently under research and development. It is expected to deliver faster speeds, reduced latency, and greater capacity compared to the current 5G wireless technology. 6G is envisioned as a technology capable of establishing a fully data‐driven network, proficient in analyzing and optimizing end‐to‐end behavior and handling massive volumes of real‐time data at rates of up to terabits per second (Tb/s). Moreover, 6G is designed to accommodate an average of 1000+ substantial connections per person over the course of a decade. The concept of a data‐driven network introduces a new service paradigm, which offers fresh opportunities for applications within 6G wireless communication and network design in the future. This paper aims to provide a survey of existing applications of 6G that are based on deep learning techniques. It also explores the potential, essential technologies, scenarios, challenges, and related topics associated with 6G. These aspects are crucial for meeting the requirements for the development of future intelligent networks. Furthermore, this work delves into various research gaps between deep learning and 6G that remain unexplored. Different potential deep learning applications for 6G networks, including privacy, security, environmentally friendly communication, sustainability, and various wireless applications, are discussed. Additionally, we shed light on the challenges and future trends in this field. This article is categorized under:Technologies > Computational IntelligenceFundamental Concepts of Data and Knowledge > Explainable AITechnologies > Machine Learning [ABSTRACT FROM AUTHOR]

The article generalizes the global experience of research on communication geography with the goal to identify future directions of study of the spatial and temporal deployment and functioning of telecommunications networks. An original algorithm of semantic search for publications in bibliographic databases has returned about 400 articles on communication geography published in 1981–2020 in scientific journals worldwide. An analysis of these publications shows that six types of communications: fiber-optic, mobile, telephone, postal, telegraph, and satellite—have been studied within infrastructural, statistical, impact, streaming, and optimization directions of communication geography. Trends are identified using the methods of the moving average and bi-proportional indices. Linear extrapolation of trends in the number of publications by type of communications and by research directions suggests that in the future preference will be given to geographical study of fiber-optic and mobile networks within infrastructural and statistical approaches with the objective to optimize the line-nodal structure. Based on a comparative analysis of unsolved problems, it is established that the priority in future research should be given to the following meta-tasks: transferring the experience of geographical study of a given type of communications to other types; using big data of telecom operators for geographical analysis of the functioning of networks; transitioning from descriptive to constructive communication geography; identifying geographical patterns for deployment of telecommunication networks, and developing the concept of a territory telecommunications complex. The following research objects are identified as promising: machine-to-machine information exchange networks, 6G mobile communication networks, and multifunctional networks. [ABSTRACT FROM AUTHOR]

The exponential growth of the fifth-generation (5G) network gives businesses and universities a chance to turn their attention to the next-generation network. It is widely acknowledged that many IoT devices require more than 5G to send various types of data in real-time. In addition to 5G, several research centres are currently concentrating on 6G, which is expected to produce networks with great quality of service (QoS) and energy efficiency. Future application requirements will necessitate a significant upgrade in mobile network architecture. 6G technologies offer larger networks with lower latency and faster data transmission than 5G networks. This review presents a comprehensive overview of 6G networks which includes the novel architectural changes within 6G networks, recent research insights from diverse institutions, applications within the realm of 6G networks, and the key features associated with them. We also explored various technologies of 6G networks encompassing terahertz, visible light connectivity, blockchain, and symbiotic broadcasting, all of which contribute to the establishment of robust and socially integrated network structures. In this survey, we have focused on 6G network slices and discussed a detailed exploration of security and privacy concerns regarding the potential 6G technologies at the levels of physical infrastructure, connecting protocols, and service provisions, alongside an evaluation of current security strategies. [ABSTRACT FROM AUTHOR]

One of the most crucial issues in mobile networks is ensuring reliable and stable connectivity during mobility. In recent years, numerous research has examined Fourth Generation (4G) and Fifth Generation (5G) mobile networks to address issues related to handover (HO) self-optimisation. Different approaches have been developed to identify the best Handover Control Parameters (HCPs) settings, including Time-To-Trigger (TTT) and Handover Margin (HOM), since managing HCP values is a key factor in determining the efficiency and accuracy of handover decisions. The purpose of this work is to address the challenges associated with HO management in Sixth Generation (6G) mobile networks, where a massive number of diverse devices, high mobility, and varying network conditions (e.g., Ultra Dense Network (UDN), Heterogeneous Network (HetNet) and Millimetre Waves (mmWaves)) are established. This, in turn, presents complex HO issues which require solutions that can adapt to different deployment settings. To provide a brief background of mobility management, the paper presents the basic HO concept, HO history, and HO procedure. Additionally, a comparison of HO in 4G to 6G is discussed to gain a better understanding of technology development and its effect on HO solutions. Furthermore, the basics of Mobility Robustness Optimisation (MRO) is explained, which involve HCP values. Previous MRO approaches have made significant advancements; however, they often lack adaptability and robustness to dynamic network conditions. By leveraging Artificial Intelligence (AI) algorithms with MRO techniques, this approach offers an improved solution to optimize handover decisions in a self-optimizing manner, thereby enhancing the overall network performance. To achieve this goal, it is necessary to analyze previous MRO and AI-integrated solutions and make comparisons between them. Additionally, the advantages and disadvantages of these solutions are considered. The contribution of this work lies in identifying the directions for HO self-optimization in 6G deployment. It demonstrates that deploying an AI-based solution would benefit future MRO deployments. This survey will aid in the analysis of mobility management challenges, particularly for the future mobile MRO self-optimization implementation in future technologies.

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling.

The sixth-generation (6G) network utilizes state-of-the-art machine learning technology and obtains high attention, while the fifth-generation (5G) industry is still developing globally. Unfortunately, 6G encounters challenges to achieve performance superiority, such as scalability, massive connection, integrity, and trust. As a result, future network technologies are migrating away from centralized management entities and toward decentralized and distributed ledger technology, such as blockchain. However, the security of the blockchain is based on the computational complexity of solving specific mathematical problems that are impossible to solve on existing computers in real-time. On the other hand, quantum computers can effortlessly translate such problems with easy decryption. As a result, this study presents an architecture demonstrating the integration of quantum blockchain (QBC) with 6G networks. To show the quantum advantage, highly entangled/secured QBC of 5-, 6-, and 7-qubits are used to create the above system’s quantum circuits. After circuit optimization, mitigation is executed with the efficiency analysis to show the advantage of the error mitigation approach in recreating the state of the QBC circuit and executing on quantum hardware. Furthermore, quantum algorithms of blockchain smart provenience contracts for the cloud-centric Internet of Things (IoT) are proposed, and corresponding quantum circuits are designed. The possible outcomes from these circuits based on the input transaction information are verified.

One of the most crucial issues in mobile networks is ensuring reliable and stable connectivity during mobility. In recent years, numerous research has examined Fourth Generation (4G) and Fifth Generation (5G) mobile networks to address issues related to handover (HO) self-optimisation. Different approaches have been developed to identify the best Handover Control Parameters (HCPs) settings, including Time-To-Trigger (TTT) and Handover Margin (HOM), since managing HCP values is a key factor in determining the efficiency and accuracy of handover decisions. The purpose of this work is to address the challenges associated with HO management in Sixth Generation (6G) mobile networks, where a massive number of diverse devices, high mobility, and varying network conditions (e.g., Ultra Dense Network (UDN), Heterogeneous Network (HetNet) and Millimetre Waves (mmWaves)) are established. This, in turn, presents complex HO issues which require solutions that can adapt to different deployment settings. To provide a brief background of mobility management, the paper presents the basic HO concept, HO history, and HO procedure. Additionally, a comparison of HO in 4G to 6G is discussed to gain a better understanding of technology development and its effect on HO solutions. Furthermore, the basics of Mobility Robustness Optimisation (MRO) is explained, which involve HCP values. Previous MRO approaches have made significant advancements; however, they often lack adaptability and robustness to dynamic network conditions. By leveraging Artificial Intelligence (AI) algorithms with MRO techniques, this approach offers an improved solution to optimize handover decisions in a self-optimizing manner, thereby enhancing the overall network performance. To achieve this goal, it is necessary to analyze previous MRO and AI-integrated solutions and make comparisons between them. Additionally, the advantages and disadvantages of these solutions are considered. The contribution of this work lies in identifying the directions for HO self-optimization in 6G deployment. It demonstrates that deploying an AI-based solution would benefit future MRO deployments. This survey will aid in the analysis of mobility management challenges, particularly for the future mobile MRO self-optimization implementation in future technologies. [ABSTRACT FROM AUTHOR]

The Sixth Generation (6G) networks have identified the use of frequency range between 95 GHz and 3 THz with a targeted data rate of 1 Terabytes/second at the access network for holographic video applications. As is demands broadening of spectrum at the core network, this paper proposes a Supercontinuum Generation (SCG) through photonic crystal fiber (PCF) as it provides excellent broadening of the optical spectrum. Discussed in the paper is supercontinuum generation at high pumping power as per the standards specified by the International Telecommunications Union. The proposed PCF is designed with silicon nanocrystal core and the cladding microstructures is arranged in a fusion approach to effectively optimize the optical parameters such as dispersion, nonlinearity, birefringence, group‐velocity dispersion, and confinement loss. The fused cladding comprises of a flower‐cladding assembly in which air‐holes arrangement is inspired from petals in a pleated structure. Such arrangement is shown here to provide high nonlinearity and negative dispersion for high power supercontinuum generation. The novel nanocore assembly with improved structural constraints delivers a non‐linearity of 6.37 × 106 W−1 km−1 and a negative dispersion of −142.1 (ps/nm‐km) at 1,550 nm. Moreover, a supercontinuum spectrum is generated using different pulse widths ranging from 350 to 650 ps with 25 kW pump power for PCF lengths of 10 and 15 mm. Key Points: A Supercontinuum Generation through photonic crystal fiber (PCF) is proposed which provides excellent broadening of the optical spectrumPCF designed with silicon nanocrystal core and cladding microstructures is arranged in fusion approach to optimize the optical parametersThe structural analysis of nano‐core PCF with fused cladding provides a baseline for designing the 6G architecture [ABSTRACT FROM AUTHOR]

Wireless technology is expected to undergo considerable transformation because of the numerous services offered by 6G communication networks, which virtually entirely encompass every part of everyday life and use a variety of devices. Channel modeling is an essential factor in designing 6G communication networks. To meet the channel requirements of future 6G communication networks, it is crucial to measure the channel to consider path loss, multi-band, fading, blocking effect, multipath clustering, transmitter, and receiver moving speed/direction/time. The goal of this paper is to design and evaluate a 6G communication network in Banda Aceh City using a statistical channel model. The channel model is associated with environmental conditions such as rainfall and humidity. The method is then based on computer simulation using the NYUSIM simulator to complete the channel modeling using an operating frequency of 95 GHz with a bandwidth of 800 MHz. In the simulator, the designed 6G channel model is evaluated in both line-of-sight (LOS) and non-line-of-sight (NLOS) network environments. In addition, the designated network parameters, such as the coverage area, angle of arrival (AoA), angle of departure (AoD), and power delay profile (PDP), are simulated. The results, at AoA, the value of received power for LOS conditions ranges from -86 dBm to -101 dBm, while the value for NLOS conditions ranges from -91 dBm to -111 dBm. Under LOS conditions, the received power for AoD ranges from -86 dBm to -101 dBm, whereas under NLOS conditions, it ranges from -91 dBm to -111 dBm. In the omnidirectional PDP, the pathloss value for the LOS condition is 99.8 dB and the delay is 17.9 ns, while the pathloss value for the NLOS condition is 104.2 dB with a delay of 28.1 ns. For the directional PDP, the LOS condition yields a path loss of 106.4 dB and a delay of 2.9 ns, while the NLOS condition yields a path loss of 110.5 dB and a delay of 3 ns. Conclusions. The simulation indicated that the AoA, AoD, and PDP in terms of received power, pathloss, and propagation delay are in acceptable conditions for a 6G network in Banda Aceh City in the two observed environments. Therefore, it is conceivable to establish a 6G network in Banda Aceh City in the future.

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling. [ABSTRACT FROM AUTHOR]

Abstract The rapid growth of Internet of Things (IoT) and Internet of Vehicles (IoV) are rapidly moving to the 6G networks, which leads to dramatically raised security issues. Using machine learning, including deep learning, to find out malicious network traffic is one of practical ways. Though much work has been done in this direction, we found little investigating the effect of using fused network conversation datasets to train and test models. Thus, this work proposes to check conversation dataset characteristics and find suitable ones to fuse into one dataset in order to improve the capability of malicious traffic and malware detection performance. The experiments using real data show that conditioned combination of datasets can be used to enhance algorithm performance and improve detection results. For this reason, it is recommended to profile datasets and conduct conditional fusion of network conversation datasets before using machine learning or deep learning. As the characterization is done using general statistical calculation, it is promising to be used for other domains too.

The mobile data traffic has been exponentially growing during the last several decades. This was enabled by the densification of the network infrastructure in terms of increased cell density (i.e., Ultra-Dense Network (UDN)) and/or the increased number of active antennas per Access Point (AP) (i.e., massive Multiple-Input Multiple-Output (mMIMO)). However, neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation (6G) wireless communications due to the inter-cell interference and large quality-of-service variations. Cell-Free (CF) mMIMO, which combines the best aspects of UDN and mMIMO, is viewed as a key solution to this issue. In such systems, each User Equipment (UE) is served by a preferred set of surrounding APs cooperatively. In this paper, we provide a survey of the state-of-the-art literature on CF mMIMO. As a starting point, the significance and the basic properties of CF mMIMO are highlighted. We then present the canonical framework to discuss the essential details (i.e., transmission procedure and mathematical system model). Next, we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and algorithms. After that, we discuss the practical issues in implementing CF mMIMO and point out the potential future directions. Finally, we conclude this paper with a summary of the key lessons learned in this field.

Wireless technology is expected to undergo considerable transformation because of the numerous services offered by 6G communication networks, which virtually entirely encompass every part of everyday life and use a variety of devices. Channel modeling is an essential factor in designing 6G communication networks. To meet the channel requirements of future 6G communication networks, it is crucial to measure the channel to consider path loss, multi-band, fading, blocking effect, multipath clustering, transmitter, and receiver moving speed/direction/time. The goal of this paper is to design and evaluate a 6G communication network in Banda Aceh City using a statistical channel model. The channel model is associated with environmental conditions such as rainfall and humidity. The method is then based on computer simulation using the NYUSIM simulator to complete the channel modeling using an operating frequency of 95 GHz with a bandwidth of 800 MHz. In the simulator, the designed 6G channel model is evaluated in both line-of-sight (LOS) and non-line-of-sight (NLOS) network environments. In addition, the designated network parameters, such as the coverage area, angle of arrival (AoA), angle of departure (AoD), and power delay profile (PDP), are simulated. The results, at AoA, the value of received power for LOS conditions ranges from -86 dBm to -101 dBm, while the value for NLOS conditions ranges from -91 dBm to -111 dBm. Under LOS conditions, the received power for AoD ranges from -86 dBm to -101 dBm, whereas under NLOS conditions, it ranges from -91 dBm to -111 dBm. In the omnidirectional PDP, the pathloss value for the LOS condition is 99.8 dB and the delay is 17.9 ns, while the pathloss value for the NLOS condition is 104.2 dB with a delay of 28.1 ns. For the directional PDP, the LOS condition yields a path loss of 106.4 dB and a delay of 2.9 ns, while the NLOS condition yields a path loss of 110.5 dB and a delay of 3 ns. Conclusions. The simulation indicated that the AoA, AoD, and PDP in terms of received power, pathloss, and propagation delay are in acceptable conditions for a 6G network in Banda Aceh City in the two observed environments. Therefore, it is conceivable to establish a 6G network in Banda Aceh City in the future. [ABSTRACT FROM AUTHOR]

The development and social needs of mobile communication networks were firstly introduced.Then four key technologies of 6G network were respectively introduced from the perspectives of communication spectrum, coverage dimension, communication empowerment, and new paradigm, namely terahertz (THz) communication, space-air-ground-sea integration network, artificial intelligence (AI), and semantic communication.The related researches of the four key technologies in recent years were analyzed, and some typical application scenarios, coverage scheme, technique principles, etc., were summarized.Finally, based on the summary, main problems of the four technologies were proposed for the future development.Besides, other candidate technologies of 6G network, including integrated sensing and communication, reconfigurable intelligence surface and new materials, blockchain, digital twin, and deterministic network technology, etc., were briefly discussed in the conclusion part.

Mobile applications have rapidly grown over the past few decades to offer futuristic applications, such as autonomous vehicles, smart farming, and smart city. Such applications require ubiquitous, real-time, and secure communications to deliver services quickly. Toward this aim, sixth-generation (6G) wireless technology offers superior performance with high reliability, enhanced transmission rate, and low latency. However, managing the resources of the aforementioned applications is highly complex in the precarious network. An adversary can perform various network-related attacks (i.e., data injection or modification) to jeopardize the regular operation of the smart applications. Therefore, incorporating blockchain technology in the smart application can be a prominent solution to tackle security, reliability, and data-sharing privacy concerns. Motivated by the same, we presented a case study on public safety applications that utilizes the essential characteristics of artificial intelligence (AI), blockchain, and a 6G network to handle data integrity attacks on the crime data. The case study is assessed using various performance parameters by considering blockchain scalability, packet drop ratio, and training accuracy. Lastly, we explored different research challenges of adopting blockchain in the 6G wireless network. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and wireless sensors have collaborated with many real-time environments for the collection and processing of physical data. Mobile networks with sixth-generation (6G) technologies provide support for emerging applications using Connected and Autonomous Vehicles (CAV) and observe critical conditions. Although, autonomous vehicle-based routing solutions have presented significant development toward reliable and inter-vehicle communications. However, there are numerous research obstacles in terms of data delivery and transmission latency due to the unpredictable environment and changing states of IoT sensors. Therefore, this work presents an efficient and trusted autonomous vehicle routing protocol using 6G networks, which aims to guarantee high quality of service and data coverage. Firstly, the proposed protocol establishes a routing process using a simulated annealing optimization technique and improves energy optimization between IoT-based vehicles, and under difficult circumstances, it statistically guarantees the optimal solution. Secondly, it provides a risk-aware security system due to reliable session-oriented communication with network edges among connected vehicles and avoids uncertainties in the autonomous system. The proposed protocol is verified using simulations for varying vehicles and varying iterations that indicates a green communication system for the autonomous system with authenticity and system intelligence. [ABSTRACT FROM AUTHOR]

Summary The wireless communication is an integral part of the human life, which has been evolved from first generation (1G) to fifth generation (5G) where prime emphasis is on the enhanced mobile broadband (eMBB), ultra‐reliable low latency communication (urLLC), and massive machine type communication (mMTC). Further, we are working towards the sixth generation (6G) network where microtechnology and nanotechnology will play a key role to provide worldwide coverage, increased spectral/energy/cost efficiency, and improved intelligence and protection. This paper emphasizes on the prime enabling technologies that are enabled by the microengineering and nanoengineering in 6G framework to meet these needs. Started with the detailed study and analysis of 6G networks such as evolution from 1G to 6G and prime difference between 5G and 6G. Further, the potential 6G dimensions, architecture of microtechnology and nanotechnology designs, and key techniques required in 6G networks are explored in detail. Moreover, the role of cognitive technologies for development of micro‐ and nano‐perspective in 6G networks is presented. Furthermore, after discussing about the application areas, finally, the prime research challenges and potential future research directions are illustrated. [ABSTRACT FROM AUTHOR]

Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output (CM-MIMO) systems. This introduces the concept of intelligent massive MIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO-enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends.

The rapid growth of Internet of Things (IoT) and Internet of Vehicles (IoV) are rapidly moving to the 6G networks, which leads to dramatically raised security issues. Using machine learning, including deep learning, to find out malicious network traffic is one of practical ways. Though much work has been done in this direction, we found little investigating the effect of using fused network conversation datasets to train and test models. Thus, this work proposes to check conversation dataset characteristics and find suitable ones to fuse into one dataset in order to improve the capability of malicious traffic and malware detection performance. The experiments using real data show that conditioned combination of datasets can be used to enhance algorithm performance and improve detection results. For this reason, it is recommended to profile datasets and conduct conditional fusion of network conversation datasets before using machine learning or deep learning. As the characterization is done using general statistical calculation, it is promising to be used for other domains too. [ABSTRACT FROM AUTHOR]

Cooperative non-orthogonal multiple access (NOMA) is a technology that addresses many challenges in future wireless generation networks by delivering a large amount of connectivity and huge system capacity. The aim of this paper is to design the varied distances and power location coefficients for far users. In addition, this paper aims to evaluate the outage probability (OP) performance against a signal-to-noise ratio (SNR) for a 6G downlink (DL) NOMA power domain (PD) and DL cooperative NOMA PD networks. We combine a DL cooperative NOMA with a 16 × 16, a 32 × 23, and a 64 × 64 multiple-input multiple-output (MIMO) and a 128 × 128, a 256 × 256, and a 512 × 512 massive MIMO in an innovative method to enhance OP performance rate and mitigate the power location coefficient's effect for remote users. The results were obtained from Rayleigh fading channels using the MATLAB simulation software program. According to the outcomes, increasing the power location coefficients for the far user from 0.6 to 0.8 reduces the OP rate because increasing the power location coefficient for the far user decreases the power location coefficient for the near user, which results in less interference between them. In terms of the OP performance rate, the DL cooperative NOMA outperforms the NOMA. According to the findings, the DL cooperative NOMA OP rate outperforms the DL NOMA by a rate of 10−0.5. Whereas the 16 × 16 MIMO enhances the OP for the far user by 78.0 × 10−4, the 32 × 32 MIMO increases the OP for the far user by 19.0 × 10−4, and the 64 × 64 MIMO decreases the OP rate for the far user by 5.0 × 10−5. At a SNR of 10 dB, the 128 × 128 massive MIMO improves the OP for the far user by 1.0 × 10−5. The 256 × 256 massive MIMO decreases the OP for the far user by 43.0 × 10−5, and the 512 × 512 massive MIMO enhances the OP for the far user by 8.0 × 10−6. The MIMO techniques improve the OP performance, while the massive MIMO technology enhances the OP performance dramatically. [ABSTRACT FROM AUTHOR]

Summary Internet of things (IoT) has massively adopted the market due to the current era demanding fully intelligent and autonomous services. However, efficient network management and message exchange are challenging in a dynamic and energy‐constrained IoT environment. Hence, for efficient message passing in IoT applications, softwarization of the IoT network is essential, wherein the logical control plane is decoupled from the data plane consisting of hardware devices such as routers and switches. Using softwarization, a centralized software‐defined networking (SDN) controller is responsible for routing data packets from source to destination in a dynamic environment. However, to reduce the computational overhead of filtering malicious and nonmalicious packets, artificial intelligence (AI) classifiers prove beneficial. Moreover, such challenges have trade‐offs with network requirements such as ultralow latency, high reliability, and higher data rates. Motivated by this, we propose an AI‐enabled network softwarization scheme for efficient message exchange under the 6G network. Lastly, the performance of the proposed scheme is evaluated with different performance metrics, such as accuracy, precision, f1‐score, packet drop ratio, and latency. The empirical result revealed that the proposed scheme outperforms in terms of accuracy and controller efficiency, that is, 81.64% and 82.2%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)