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The rollout of sixth-generation (6G) wireless networks is likely to transform communication systems in terms of speed, latency, and connectivity. This paper investigates adaptive beamforming schemes for communications in the Terahertz (THz) range. Additionally, it investigates the possibility of resource management that minimizes energy consumption. The problem of communication in the THz range is mitigated by the AMAB approach which varies beam angles and uses Reconfigurable Intelligent Surface (RIS) technology to improve signal delivery. This overcomes the drawback of THz frequencies' large attenuation and short range, which are a hindrance to effective high-rate data transmission needed in 6G applications. The Energy Adaptive Resource Allocation with Predictive Optimization (EARAPO) algorithm applies Machine Learning approaches to resource allocation and management of network demand by forecasting traffic trends. Such a predictive strategy supports enhanced resource utilization through the elasticity of network technology which leads to a reduction of energy cost with zero impact on the quality of service (QoS). The Adaptive Meta-Surface Assisted Beamforming (AMAB) algorithm with RIS consistently improves SINR across various numbers of multiuser equipment (UE), even in network-intensive scenarios. The power consumption efficiency of EARAPO was also superior while adaptation to power-hungry variants of both algorithms resulted in power consumption being moderated until later in the escalation of network requirements. These algorithms are quite effective in enhancing the quality of signals, making better use of resources and reducing energy usage in the upcoming wireless networks. All these studies open up a perspective for the future of sustainable and high-performance wireless technologies. [ABSTRACT FROM AUTHOR]

5G, the fifth-generation mobile network, is predicted to significantly increase the traditional trajectory of energy consumption. It now uses four times as much energy as 4G, the fourth-generation mobile network. As a result, compared to previous generations, 5G's increased cell density makes energy efficiency a top priority. The objective of this paper is to formulate end-to-end power consumption models for three different 5G radio access network (RAN) deployment architectures, namely the 5G distributed RAN, the 5G centralized RAN with dedicated hardware and the 5G Cloud Centralized-RAN. The end-to-end modelling of the power consumption of a complete 5G system is obtained by combining the power models of individual components such as the base station, the core network, front-haul, mid-haul and backhaul links, as applicable for the different architectures. The authors considered the deployment of software-defined networking (SDN) at the 5G Core network and gigabit passive optical network as access technology for the backhaul network. This study examines the end-to-end power consumption of 5G networks across various architectures, focusing on key dependent parameters. The findings indicate that the 5G distributed RAN scenario has the highest power consumption among the three models evaluated. In comparison, the centralized 5G and 5G Cloud C-RAN scenarios consume 12% and 20% less power, respectively, than the Centralized RAN solution. Additionally, calculations reveal that base stations account for 74% to 78% of the total power consumption in 5G networks. These insights helped pioneer the calculation of the end-to-end power requirements of different 5G network architectures, forming a solid foundation for their sustainable implementation. Furthermore, this study lays the groundwork for extending power modeling to future 6G networks. [ABSTRACT FROM AUTHOR]

The mega-low Earth orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the mega-LEO satellite constellation system (MLSCS), which is the spatial distribution of satellites, global users, and their services, along with the utilization of global spectrum resources, significantly impacts resource allocation and scheduling. This paper addresses the challenge of effectively allocating system resources based on service and resource distribution, particularly in hotspot areas where user demand is concentrated, to enhance resource utilization efficiency. We propose a novel three-layer management architecture designed to implement scheduling strategies and alleviate the processing burden on the terrestrial Network Control Center (NCC), while providing real-time scheduling capabilities to adapt to rapid changes in network topology, resource distribution, and service requirements. The three layers of the resource management architecture—NCC, space base station (SBS), and user terminal (UT)—are discussed in detail, along with the functions and responsibilities of each layer. Additionally, we explore various resource scheduling strategies, approaches, and algorithms, including spectrum cognition, interference coordination, beam scheduling, multi-satellite collaboration, and random access. Simulations demonstrate the effectiveness of the proposed approaches and algorithms, indicating significant improvements in resource management in the MLSCS. [ABSTRACT FROM AUTHOR]

Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for the upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has significantly heightened expectations, particularly for Internet of Things (IoT) driven use cases. However, this progress introduces significant security risks, as technologies like O-RAN, terahertz communication, and native AI pose threats such as eavesdropping, supply chain vulnerabilities, model poisoning, and adversarial attacks. The increased exposure of sensitive data in 6G applications further intensifies these challenges. This necessitates a concerted effort from stakeholders including ITU-R, 3GPP, ETSI, OEMs and researchers to embed security and resilience as core components of 6G. While research is advancing, establishing a comprehensive security framework remains a significant challenge. To address these evolving threats, our research proposes a dynamic security framework that emphasizes the integration of explainable AI (XAI) techniques like SHAP and LIME with advanced machine learning models to enhance decision-making transparency, improve security in complex 6G environments, and ensure effective detection and mitigation of emerging cyber threats. By refining model accuracy and ensuring alignment through recursive feature elimination and consistent cross-validation, our approach strengthens the overall security posture of the IoT–6G ecosystem, making it more resilient to adversarial attacks and other vulnerabilities. [ABSTRACT FROM AUTHOR]

In an era where ubiquitous connectivity and escalating data demands are altering the landscape of wireless communications, our paper proposes a pioneering enhancement for the OMNeT++ simulator to support the advanced features of IEEE 802.11ax high efficiency (HE) alongside cell-free massive multiple-input multiple-output (MIMO) systems. Traditional wireless networks face daunting challenges in sustaining elevated quality of service (QoS), primarily due to fluctuating user densities and signal quality. Cell-free massive MIMO serves as a compelling answer to this predicament by decentralizing the cellular architecture. It eradicates conventional cell boundaries, furnishing uniform QoS regardless of user locations. However, these advancements come at the expense of complex backhaul networks and articulated joint signal processing. The 802.11ax standard, touted for its robustness and efficiency, remains underexplored in this new paradigm. Our research not only dissects the architectural elements and constraints of both 802.11ax and cell-free massive MIMO but also elaborates on the adaptations required to extend OMNeT++ functionalities for these technologies. By doing so, we bridge a crucial gap, enabling the simulator to provide a more precise, detailed, and scalable evaluation of emerging 6G scenarios and directional communications also taking into account the impact of the most known routing protocols such as dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV), optimized link state routing (OLSR), and dynamic mobile ad hoc network on-demand (DYMO) that were selected for this comparative study. The proposed extensions promise to revolutionize network simulations and lay the foundation for in-depth analyses of wireless systems in complex and dynamic environments. Through extensive simulations, our study demonstrates that cell-free massive MIMO configurations significantly improve network throughput in high-density mobile ad hoc network (MANET) environments, with results indicating an average throughput gain of up to 30% compared with non-cell-free configurations. This improvement highlights the efficacy of cell-free massive MIMO to take advantage of the spatial and frequency multiplexing capabilities inherent in the 802.11ax standard, making it a promising solution for future wireless systems in densely populated areas. [ABSTRACT FROM AUTHOR]

The evolution towards sixth-generation (6G) networks requires new architecture enhancements to support the broad device ecosystem, comprising users, machines, autonomous vehicles, and Internet-of-things devices. Moreover, high heterogeneity in the desired quality-of-service (QoS) is expected, as 6G networks will offer extremely low-latency and high-throughput services and error-free communication. This complex environment raises significant challenges in resource management while adhering to security and privacy constraints due to the plethora of data generation endpoints. Considering the advances in AI/ML-aided integration in wireless networks and recent efforts on the network data analytics function (NWDAF) by the 3rd generation partnership project (3GPP), this work presents an AI/ML-aided distributed trustable engine (DTE), collecting data from diverse sources of the 6G infrastructure and deploying ML methods for anomaly detection against diverse threat types. Moreover, we present the DTE architecture and its components, providing data management, AI/ML model training, and classification capabilities for anomaly detection. To promote privacy-aware networking, a federated learning (FL) framework to extend the DTE is discussed. Then, the anomaly detection capabilities of the AI/ML-aided DTE are presented in detail, together with the ML model training process, which considers various ML models. For this purpose, we use two open datasets representing attack scenarios in the core and the edge parts of the network. Experimental results, including an ensemble learning method and different supervised learning alternatives, show that the AI/ML-aided DTE can efficiently train ML models with reduced dimensionality and deploy them in diverse cybersecurity scenarios to improve anomaly detection in 6G networks. [ABSTRACT FROM AUTHOR]

The 6G wireless networks utilize terahertz (THz) frequency and intended to tremendously dynamic and diverse applications with deep learning enabled network, harvested significant attention and able to solve complex problems. Efficient resource allocation is a key requirement of next generation wireless networks. This research focuses on the resource allocation optimization challenge which includes storage, computing power, bandwidth and memory in the milieu of 6G wireless networks with device-to-device (D2D) communication enabled. The proposed model uses modified long short-term memory (mLSTM) and feed forward neural network to allocate resources to various tasks as per requirement such as information access, audio/video streaming, information access and productivity activity applications. The proposed work focuses on network parameters like channel noise, signal to noise ratio (SNR), distance from base station and includes D2D communication decisions to improve network performance. This research gives a novelty learning based solution for resource allocation for 6G wireless networks which contributes to the enhancement of next generation wireless communication networks. The lowest computing power utilized is 1%, Bandwidth utilized is 3% of total bandwidth and 2% storage. [ABSTRACT FROM AUTHOR]

The rapid expansion of Mobile Edge Computing (MEC) and the Internet of Things (IoT) has revolutionized technology by enabling real-time data processing at the network edge, which is essential for applications such as autonomous vehicles and smart cities. With the advent of 6G networks, which promise ultra-fast speeds, vast connectivity, and low-latency communication, MEC-IoT systems are becoming more powerful but also face significant security challenges. Existing authentication mechanisms (AMs) are often vulnerable to attacks like impersonation and insider threats. This paper introduces a novel lightweight AM, called RAM-MEN that employs cryptography and physically unclonable functions (PUFs) to secure IoT-enabled MEC environments in the 6G era. It protects against insider threats and fake MEC access points while ensuring efficiency and scalability. Additionally, the proposed RAM-MEN establishes a secure communication channel (session key) between IoT devices and the MEC server, enabling secure offloading of computationally intensive tasks. The security of the session is rigorously evaluated using formal methods, including Scyther and the random or real model, alongside informal approaches. Comparative performance evaluations show that the proposed RAM-MEN reduces communication costs by 21.54% to 45.53% and computational costs by 17.09% to 83.72%, while providing enhanced security features. [ABSTRACT FROM AUTHOR]

Within the framework of 6G networks, the rapid proliferation of Internet of Things (IoT) devices, coupled with their decentralized and heterogeneous characteristics, presents substantial security challenges. Conventional centralized systems face significant challenges in effectively managing the diverse range of IoT devices, and they are inadequate in addressing the requirements for reduced latency and the efficient processing and analysis of large-scale data. To tackle these challenges, this paper introduces a zero-trust access control framework that integrates blockchain technology with inner-product encryption. By using smart contracts for automated access control, a reputation-based trust model for decentralized identity management, and inner-product encryption for fine-grained access control, the framework ensures data security and efficiency. Firstly, smart contracts are employed to automate access control, and software-defined boundaries are defined for different application domains. Secondly, through a trust model based on a consensus algorithm of node reputation values and a registration-based inner-product encryption algorithm supporting fine-grained access control, zero-trust self-sovereign enhanced identity management in the 6G environment of the Internet of Things is achieved. Furthermore, the use of multiple auxiliary chains for storing data across different application domains not only mitigates the risks associated with data expansion but also achieves micro-segmentation, thereby enhancing the efficiency of access control. Finally, empirical evidence demonstrates that, compared with the traditional methods, this paper's scheme improves the encryption efficiency by 14%, reduces the data access latency by 18%, and significantly improves the throughput. This mechanism ensures data security while maintaining system efficiency in environments with large-scale data interactions. [ABSTRACT FROM AUTHOR]

The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks. [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]

Summary: The 6G networks are envisioned to provide Artificial Intelligence (AI) for distributed devices through deploying machine learning on base stations. However, due to the concern of data privacy, devices are not willing to transmit their raw data to base stations (BSs) for machine learning training. Federated learning (FL) is a promising paradigm that can enable distributed machine learning while protecting data privacy by collaboratively training AI models without sharing raw data. But the traditional FL requires a central node to complete global model update which makes the traditional FL facing single point attack. Further, the traditional FL cannot allow the distributed model sharing among untrusted devices which results in low training efficiency on devices. Blockchain is a promising approach to address the above issues since it can establish a secure decentralized and transaction sharing environment for untrusted devices. In this paper, we integrate blockchain into FL to build a distributed model training and sharing platform, where distributed mobile devices execute local model training and base stations maintain blockchain‐based model sharing. Moreover, in order to accelerate the establishment of blockchain, we design a lightweight consensus strategy based on a delegate committee. To select high‐quality nodes to form delegate committee, we design a deep reinforcement learning (DRL)‐based selection algorithm. Numerical results demonstrate that the proposed framework can achieve a higher precision compared with the traditional FL, and the proposed DRL selection algorithm can reduce consensus latency compared with the benchmarks. [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]

6G incorporates Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) as effective solutions to overcome the limitations of terrestrial networks in terms of coverage and resource constraints. Compared to communications with conventional UAV networks which face restricted battery longevity, fluctuating channel conditions, and paucity of resources, IRS-assisted UAV communications is seen as an attractive strategy. In this paper, we present an extensive survey on IRS-assisted UAV communications for 6G networks. We highlight various application scenarios and key technologies for integrating IRS and UAVs in 6G architecture. We discuss primary issues along with their solutions and put forward the open research challenges that could serve as a potential area for further investigation in the related discipline. Key findings encompass an in-depth exploration of diverse application scenarios and pivotal technologies crucial for seamless integration of IRS and UAVs within the 6G architecture, providing valuable insights into optimizing communication efficiency and addressing network challenges. This survey serves as a valuable resource for scholars, practitioners, and policymakers in the fields of integrated UAV and IRS communication. It provides insights for making well-informed decisions and driving advancements to meet the constantly evolving demands of our connected world. [ABSTRACT FROM AUTHOR]

Beyond 5th generation (B5G) mobile communication framework is a paramount research domain in modern networking and message transfer. In 6G communication, the prime objective is ultralow latency communication, which ensures high bandwidth data transfer in real-life and mission-critical situations. In a 6G networking scenario analysis and prediction of network throughput, from the location, trajectory, moving speed, heading of the user equipment, drone base stations, receiver, and relay node informations are highly important for achieving efficiency in the resource utilization. In this work, we analysed the network latency using simulation framework. Furthermore, we propose a neural network model that can intelligently predict network throughput parameters of a 6G communication ecosystem. The system uses an improvisation regarding the prediction optimization technique, coined "3-Musketeer Optimization". The prediction was performed by using an ensemble artificial neural network model on the throughput with the parameters, mobility, trajectory, signal strength, power, radio status of user equipment and moving base stations. The results show that the latency of the node is a maximum of 800 ms and a minimum of 170 ms, and the mean square error (MSE) and mean absolute error (MAE) of the 3-Musk optimizer are approximately 0.0275 and 0.1125, respectively. [ABSTRACT FROM AUTHOR]

Technological innovations in the Internet of Things (IoT) enable the acquisition, distribution, and interpretation of massive data, facilitating the development of numerous versatile and responsive applications in real-world contexts. However, IoT faces challenges in its centralization, privacy preservation, latency, and security. This paper uses prominent decentralized technologies like blockchain and federated learning to provide Quality-of-Services on 6G mobile networks to preserve privacy. In order to protect user privacy, user equipment (UE) at the distributed federated layer is trained using a machine learning model that masks sensitive data from service providers or masqueraders. Consequently, it empowers UE to converge on a global model by executing local gradient updates with network automation. We leverage blockchain as a decentralized sharing model over the federated layer to allow wireless networks to offload computational load in rendering real-time IoT data with an accuracy of 98%. A novel dynamic authentication framework is proposed to enable UE to interact with various base stations in the 6G wireless network with an average computation, aggregation and response time of 105 ms, 3.84 ms and 45.2 s respectively. The Distributed Hash Table at the core Blockchain layer offers decentralized secure storage with an improved throughput of 1890 TPS. [ABSTRACT FROM AUTHOR]

6G networks are envisioned to provide an extremely high quality‐of‐service (QoS). Then, future 6G network must operate in the Ka‐band, where more bandwidth and radio channels are available, and noise and interferences are lower. But even in this context, 6G base stations must adjust the transmission power to ensure the signal‐to‐noise ratio is good enough to enable the expected QoS. However, 6G networks are not the only infrastructure operating in that band. Actually, many scientific instruments are also working on those frequencies. Considering that 6G networks will be transmitting a relevant power level, they can interfere very easily with these scientific instruments. Therefore, in this paper we propose a new solution to enable the interferenceless coexistence between 6G networks and scientific instruments. This solution includes a three‐dimensional model to analyse future positions of user devices. Using this information and an interference model, we design a decision model to adapt the transmitted power, so the QoS achieves the expected level. Besides, when the transmitted power is high enough to interfere with close scientific instruments, a scheduling algorithm based on swarm intelligence is triggered. This algorithm calculates the optimum distribution of time slots and radio channels, so the scientific instruments can operate, and the 6G networks can still provide the required QoS. An experimental validation is provided to analyse the performance of the proposed solution. Results show a complete coexistence may be achieved with an interference level of −26 dBm and a QoS above 95% of the expected level. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient collaborative perspective in converged networks and the trend of increasing bandwidth demand for bandwidth-intensive applications in 5G and 6G networks, this paper proposed a BASA algorithm and actively engaged in collaborative scheduling of resources based on bandwidth awareness. The paper dynamically sensed the link bandwidth and node computation capability to obtain the node mapping policy. Afterward, the paper took bandwidth and distance as sequential considerations and collaboratively selected the optimal link mapping policy based on the node mapping policy. The algorithm ensured the collaborative consistency of node and link mapping strategies and improved the QoS of the network. Experiments demonstrate that the method has better SFC mapping accuracy and efficiency, and outperforms the comparative algorithms in terms of the SFC mapping success rate and bandwidth utilization, and the algorithms bandwidth utilization can reach more than 75% on average. [ABSTRACT FROM AUTHOR]

The advent of 6G networks and beyond calls for innovative paradigms to address the stringent demands of emerging applications, such as extended reality and autonomous vehicles, as well as technological frameworks like digital twin networks. Traditional cloud computing and edge computing architectures fall short in providing their required flexibility, scalability, and ultra-low latency. Cloud computing centralizes resources in distant data centers, leading to high latency and increased network congestion, while edge computing, though closer to data sources, lacks the agility to dynamically adapt to fluctuating workloads, user mobility, and real-time requirements. In-network computing (INC) offers a transformative solution by integrating computational capabilities directly into the network fabric, enabling dynamic and distributed task execution. This paper explores INC through the lens of information-centric networking (ICN), a revolutionary communication paradigm implementing routing-by-name and in-network caching, and thus emerging as a natural enabler for INC. We review state-of-the-art advancements involving INC and ICN, addressing critical topics such as service naming, executor selection strategies, compute reuse, and security. Furthermore, we discuss key challenges and propose research directions for deploying INC via ICN, thereby outlining a cohesive roadmap for future investigation. [ABSTRACT FROM AUTHOR]

The future integration of drones in 6G networks will significantly enhance their capabilities, enabling a wide range of new applications based on autonomous operation. However, drone networks are particularly vulnerable to jamming attacks, a type of availability attack that can disrupt network operation and hinder drone functionality. In this paper, we propose a low complexity unsupervised machine learning approach for the detection of constant and periodic jamming attacks, using the Isolation Forest algorithm. We detail the tuning of the base model as well as the integration with a Majority Rule module which significantly reduced the number of false positives caused by environmental noise, achieving high accuracy and precision. Our approach outperforms the standard Isolation Forest model in the detection of both constant and periodic jamming attacks, while still correctly identifying nominal traffic. Finally, we discuss the potential integration of the proposed solution in 6G-enabled drone networks, as a lightweight edge-based solution for enhancing security against jamming attacks. [ABSTRACT FROM AUTHOR]

A novel frequency regime of the spectrum from 0.1 to 10 THz enables an emerging paradigm of modern wireless communication referred as 6G communication networks. However, to assure the success of 6G communication networks, ever‐growing development and deployment of nano‐machines, leading to ultra‐dense nano‐network, is witnessed. Nevertheless, the potential feature analysis of such ultra‐dense nano‐network in the presence of interference limited scenarios, by virtue of ultra‐dense deployment of nano‐machines, under the performance improvement techniques is missing from the reported literatures. Therefore, in this article, several performance metrics for an ultra‐dense nano‐network under the selection combining (SC) diversity technique is presented. The analytical expressions for error rates of different modulation schemes such as BPSK/BFSK, DPSK/ NFSK, Q‐PSK and M‐QAM under SC diversity technique for the considered nano‐network in the presence of interference limited ecosystems are presented. In addition, analytical expressions of capacity under constant power with optimal rate adaptation (Cora) and capacity under channel inversion with fixed rate (Ccifr) are explored. It is worthy to mention that the proposed analytical expressions are generic in nature for the considered scenarios, in which severity and shaping parameters in both the multipath fading and shadowing are included. Numerically simulated results support mathematical formulation. [ABSTRACT FROM AUTHOR]

In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a notable technology for enhancing spectrum usage in wireless communication. On the other hand, cognitive radio (CR) networks are also renowned technology for increasing spectrum efficiency. However, fifth-generation wireless networks cannot provide a dynamic wireless environment. This barrier is overcome by sixth-generation (6G) wireless networks. In 6G, a dynamic wireless environment can be achieved by an intelligent reflecting surface (IRS). IRS is an eminent technology that enhances the overall quality of experience in wireless systems. This paper presents users' performance analysis in IRS-aided NOMA-based 6G CR networks to capitalize on these technologies. The most popular five machine learning (ML)-based classifiers have been considered to sense the feature of the spectrum and evaluate the performance of the IRS-aided NOMA-based 6G CR network for the probability of detection, throughput, and energy efficiency. The simulation results have been validated for the proposed network with and without ML-based classifiers. Further, the performance of the proposed network has been tested for the different ratios of sensing time to total time, probability of false alarms, and different signal sizes of the CR network. The time complexity of the proposed network has been evaluated and found that the network has satisfactory inference time. The simulation results also suggest that the proposed network may fulfill the spectrum, energy, and reliability requirements of the 6G wireless networks. [ABSTRACT FROM AUTHOR]

Next-generation (NG) optical technologies are expected to offer high data rates, multiple broadband services, expandable bandwidth, and flexible communication options for diverse end users. In optical technologies, free space optical (FSO) technology stands out as a promising component to meet the requirements of terrestrial sixth generation (6G) networks. This is due to its cost-effectiveness, ease of deployment, high bandwidth capacity, and robust security features. However, haze and rain are major challenges to FSO link performance. These adverse weather conditions reduce visibility, causing significant attenuation of the laser signal. The resulting attenuation negatively impacts the performance and availability of the FSO link. This paper assesses the performance of a terrestrial FSO link under tropical climate conditions. Predicted attenuation due to haze is analyzed and compared using two wavelengths: 850 nm and 1550 nm. The predicted attenuation is based on a whole year of visibility data in Malaysia, from January 1, 2023 to December 31, 2023. Additionally, the availability of the two wavelengths is evaluated. The findings show that higher wavelengths experience lower attenuation compared to lower wavelengths. These results provide valuable insights into the feasibility of deploying FSO links in tropical climates. [ABSTRACT FROM AUTHOR]

Collaborative transmission, comprising multiple devices owned by a single user, is progressively evolving into an essential strategy to meet the stringent demands of burgeoning collaborative scenarios in 5G‐advanced and 6G networks. This paper proposes three novel use cases for device collaboration, namely data duplication, data splitting and wireless backup, to address these requirements. To provide dynamic and efficient collaboration, both non‐transparent mode via the medium access control layer collaboration and transparent mode via the physical layer collaboration are proposed. The paper further introduces a comprehensive design framework including protocol stack design, user equipment capability reporting, user equipment pairing, scheduling mechanism and transmission mechanism for different collaborative use cases with different collaborative modes. Evaluation outcomes reveal that the recommended methods could decrease the resources consumed for data duplication while increasing the user perceived throughput for data duplication and data splitting. The proposed methods also augment transmission reliability for both data duplication and wireless backup. [ABSTRACT FROM AUTHOR]

[Display omitted] • Customizing a novel semi-supervised federated learning system for digital twins. • Designing a new Bayesian estimation pseudo-label method for semi-supervised federated learning systems. • Expanding the scope of federated learning in digital twins. In recent years, the proliferation of Industrial Internet of Things (IIoT) devices has resulted in a substantial increase in data generation across various domains, including the nascent 6G networks. Digital Twins (DTs), serving as virtual replicas of physical entities, have gained popularity within the realm of IoT due to their capacity to simulate and optimize physical systems in a cost-effective manner. Nonetheless, the security of DTs and the safeguarding of the sensitive data they generate have emerged as paramount concerns. Fortunately, the Federated Fearning (FL) system has emerged as a promising solution to address the challenge of data privacy within DTs. Nonetheless, the requisite acquisition of a significant volume of labeled data for training purposes poses a formidable challenge, particularly in a DT environment that blends real and virtual data. To tackle this challenge, this study presents an innovative Semi-supervised FL (SSFL) framework designed to overcome the scarcity of labeled data through the strategic utilization of pseudo-labels. Specifically, our proposed SSFL algorithm, named SSFL-MBE , introduces a novel approach by combining M ix data augmentation and B ayesian E stimation consistency regularization loss, thereby integrating robust augmentation techniques to enhance model generalization. Furthermore, we introduce a Bayesian-estimated pseudo-label loss that leverages prior probabilistic knowledge to enhance model performance. Our investigation focuses particularly on a demanding scenario where labeled and unlabeled data are segregated across disparate locations, specifically, the server and various clients. Comprehensive evaluations conducted on CIFAR-10 and MNIST datasets conclusively demonstrate that our proposed algorithm consistently surpasses mainstream SSFL baseline models, exhibiting an enhancement in model performance ranging from 0.5% to 1.5%. Overall, this work contributes to the development of more efficient and secure approaches for model training in DT-empowered FL settings, which is crucial for the deployment of IIoTs in 6G-enabled environments. [ABSTRACT FROM AUTHOR]

The demand for high-efficiency, small-sized ultrawide-band (UWB) antennas has risen due to the need for fast wireless communication systems. In this manuscript, we propose a novel geometry for a 4-port multi-in multi-out (MIMO) antenna with an L-shaped structure that solves the limitations of current designs in UWB applications. The dimensions and thickness of an antenna design are 18×18×1.5875 mm. It operates in multiple bands, such as Ku, K, and millimeter wave (mmWave) spectrum ranging from 17 to 100 GHz. This feature is ideal for small fifth-generation (5G) and sixth-generation (6G) wireless communication devices. We used two separate ground strips at the bottom to minimize interference in the MIMO element design and enhance antenna performance. The antenna performed exceptionally well in all measured parameters. The reflection coefficient (RC) was less than -10 dB, the mutual coupling (MC) coefficient reached -62.4 dB, and the radiation antenna efficiency ranged between 82% and 94%. It achieved the highest gain of 13 dBi at 87 GHz. In addition, the envelope correlation coefficient (ECC) is < 0.02, and the diversity gain (DG) ranges between 9.9 and 10 dB. This antenna surpasses all others in every parameter when compared to proposals by different researchers. This makes it a superior choice for modern wireless devices catering to 5G and 6G networks. [ABSTRACT FROM AUTHOR]

As we are living in a fast-moving dynamic world. Emerging technologies such as artificial intelligence (AI), internet of things (IoT), virtual reality (VR), augmented reality (AR), fourth industrial revolution (Industry 4.0), metaverse, and edge computing are expected to play an essential role in our daily life. These technologies require high-speed, sustainable, and reliable communications networks which are expected by sixth generation (6G) wireless communications networks. 6G will be the backbone for these emerging technologies as well as for the technology-driven digital infrastructure. Governments as well as research and development (R&D) of the technology companies are gearing up to conduct a regulatory framework to standardize 6G networks; studying and conducting experimental setups to examine and evaluate the deployment of 6G networks; both in which they will have opportunities and challenges. This paper provides insights and guidelines for 6G networks in terms of standards, implementations, applications, and research trends. In addition, it provides reliability analysis for terrestrial 6G networks. A carrier class availability could be achieved over a maximum of 4 km link distance. These insights and availability figures may be used as a useful tool for researchers and industry stakeholders for the deployment and rollout of the next generation 6G wireless communications networks. [ABSTRACT FROM AUTHOR]

As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond. [ABSTRACT FROM AUTHOR]

The satellite Internet can cover up to 70% of the surface of our planet Earth to provide network services for nearly 3 billion people. As such, it is promising to become the building block of future 6G networks. The satellite Internet is capable of providing uniform communication capacity to every part of the Earth's surface, due to its uniform and symmetrical constellation structure, while the uneven distribution of ground populations leads to globally uneven traffic delivery requests, incurring a mismatch between the capacity and traffic transmission demands. As such, traditional single-criteria (e.g., shortest delay) routing algorithms can lead to severe network congestion and cannot provision delay-deterministic data delivery. To overcome this bottleneck, we propose a multi-criteria routing and scheduling scheme to redirect time-tolerant data, thus preventing congestion for time-sensitive data, based on the spatiotemporal distribution of data traffic. First, we construct a traffic spatiotemporal distribution model, to indicate the network load status. Next, we model the satellite network multi-criteria routing problem as an integer linear programming one, which is NP-hard and challenging to solve within polynomial time. A novel link weight design based on both the link delay and load is introduced, transforming the mathematical programming problem into a routing optimization problem. The proposed correlation scheduling algorithm fully utilizes idle network link resources, significantly improving network resource utilization and eliminating resource competition between non-time-sensitive and time-sensitive services. Simulation results show that compared with traditional algorithms, the proposed method can increase the throughput of time-sensitive data by up to 20.8% and reduce the packet loss rate of time-sensitive services by up to 76.8%. [ABSTRACT FROM AUTHOR]

In the next-generation communication systems, multiple access (MA) will play a crucial role in achieving high throughput to support future-oriented services. Recently, rate-splitting multiple access (RSMA) has received much attention from both academia and industry due to its ability to flexibly mitigate inter-user interference in a broad range of interference regimes. Further, with the growing emphasis on spectrum resource utilization, integrated sensing and communication (ISAC) technology, which improves spectrum efficiency by merging communication and radar signals, is expected to be one of the key candidate technologies for the sixth-generation (6G) wireless networks. In this paper, we first investigate the evolution of existing MA techniques and basic principles of RSMA-assisted ISAC systems. Moreover, to make the future RSMA-assisted ISAC systems, we highlight prime technologies of 6G such as non-terrestrial networks (NTN), reconfigurable intelligent surfaces (RIS), millimeter wave (mmWave) and terahertz (THz) technologies, and vehicular-to-everything (V2X), along with the main technical challenges and potential benefits to pave the way for RSMA-assisted ISAC systems. [ABSTRACT FROM AUTHOR]

Recently, faced with stagnating subscriber growth, operators of mobile communication services worldwide are actively seeking to revitalize the convergence industry through partnerships with other sectors. South Koreadwhich launched the world's first 5G service in 2019dhad 32 million 5G subscribers by 2023. However, due to issues such as poor service quality, the lack of compelling services, and inadequate network infrastructure relative to initial projections, subscriber growth has been slower than expected. In this paper, we analyze the deployment of 5G and its business models in vital fields like smart factories and digital healthcare, which are expected to play crucial roles in propelling the industry forward. The business model canvas (BMC) framework is employed to identify essential factors for industry revitalization, major challenges, and future strategies. The analysis reveals that the provision of ultrabroadband and low-latency services has been hindered by delays in deploying services at 28 GHz, crucial for advancing the convergence industry. Enhancing the use of the 28 GHz wireless network would enable critical services for smart factories and digital healthcare, such as mobile edge computing, machine vision, telemedicine, and AI-driven medical applications. Furthermore, it is determined that strategies for revitalization at the government level need urgent implementation, contrasting with the current, less effective sandbox-level strategies. The empirical findings of this study allow for an assessment of why 5G subscriber growth lags behind that of 4G, and assist in the formulation of effective policies. Additionally, this data can serve as a foundation for planning strategies to stimulate the convergence industry using future B5G and 6G networks. [ABSTRACT FROM AUTHOR]

In the 6G mobile networks, ensuring low latency and low energy consumption is paramount. This study explores a novel approach for addressing these issues in a backscatter communication (BC) - based multiple user unmanned aerial vehicle (UAV) - enabled mobile edge computing (MEC) Internet of Things (IoT) network. Our proposed framework incorporates a partial offloading strategy, a time division multiple access (TDMA) scheme, and a radio frequency energy harvesting mechanism. We use the channel gains statistical characteristics to derive approximate closed-form expressions for the successful computation and energy outage probabilities. Using these benchmarks, we investigate the impact of critical parameters such as transmit power, number of sensor nodes, task division ratio, the altitude of the UAV, and threshold tolerance. We validate our analysis through computer simulations and provide results to support our findings. The study reveals that selecting an optimal UAV altitude can significantly improve latency and energy consumption performance. [ABSTRACT FROM AUTHOR]

This paper presents an advanced framework for securing 6G communication by integrating deep learning and physical layer security (PLS). The proposed model incorporates multi-stage detection mechanisms to enhance security against various attacks on the 6G air interface. Deep neural networks and a hybrid model are employed for sequential learning to improve classification accuracy and handle complex data patterns. Additionally, spoofing, jamming, and eavesdropping attacks are simulated to refine detection mechanisms. An anomaly detection system is developed to identify unusual signal patterns indicating potential attacks. The results demonstrate that machine learning (ML) and hybrid models outperform conventional approaches, showing improvements of up to 85% in bit error rate (BER) and 24% in accuracy, especially under attack conditions. This research contributes to the advancement of secure 6G communication systems, offering details on effective defence mechanisms against physical layer attacks. [ABSTRACT FROM AUTHOR]

Free Space Optics (FSO)-based UAV-enabled wireless power transfer (WPT) relay systems have emerged as a key technology for 6G networks, efficiently providing continuous power to Internet of Things (IoT) devices even in remote areas such as disaster recovery zones, maritime regions, and military networks, while addressing the limited battery capacity of UAVs through the FSO fronthaul link. However, the harvested power at the ground devices depends on the displacement and diameter of the FSO beam spot reaching the UAV, as well as the UAV trajectory, which affects both the FSO link and the radio-frequency (RF) link simultaneously. In this paper, we propose a joint design of the divergence angle in the FSO link and the UAV trajectory, in order to maximize the power transfer efficiency. Driven by the analysis of the optimal condition for the divergence angle, we develop a hybrid BS-PSO-based method to jointly optimize them while improving optimization performance. Numerical results demonstrate that the proposed method substantially increases power transfer efficiency and improves the optimization capability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the explosion of intelligent network applications and the prosperity of artificial intelligence (AI) technologies, the sixth generation (6G) wireless networks are not only expected to further improve network capacity, but also anticipated to establish a new architecture of “Intelligent Connectivity of Everything”. Semantic communication (SC) is a promising solution for future 6G networks due to its natural capability of integrating application requirements and information meaning into data transmission processes. In this paper, a comprehensive survey that overviews how SC can be applied for 6G networks and the key technologies of SC is presented. For this purpose, we first provide a detailed overview of the concepts of semantic information (SI) and SC, as well as the classifications of SC. Then, we present the vision of mutual support between SC technologies and 6G networks, as well as the potential benefits using SC for 6G applications. To achieve the benefits of SC, the fundamental theories and four important technologies in SC are thoroughly investigated, which are SC system architecture design, SI extraction, SI transmission, and SC performance evaluation. Then, we introduce open problems and potential research directions pertaining to SC. In a nutshell, this paper provides a holistic review of concepts, applications, fundamentals, key technologies, existing challenges, and open issues of SC tailored to the requirements of 6G networks.

This paper investigates the integration of an intelligent reflecting surface (IRS) and a jammer controlled interference to enhance the physical layer security (PLS) of wireless powered communication networks (WPCNs). In 6G networks, where low-power Internet of Things (IoT) devices are prevalent, security and energy efficiency are critical. While existing studies primarily focus on IRS-assisted methods to improve PLS by manipulating signal reflections, such approaches may not fully degrade the reception of eavesdroppers. We address this limitation by proposing an approach that combines IRS-assisted signal reflection with controlled jamming to further degrade the reception of eavesdroppers. Specifically, we jointly optimize the downlink/uplink time allocation, IRS phase shift matrices, and jamming interference to maximize the sum secrecy throughput. To address the non-convex nature of the problem, we develop an alternating optimization algorithm that decouples the problem into subproblems, ensuring efficient convergence to a locally optimal solution. Through detailed performance analysis, we demonstrate the superiority of our proposed scheme in terms of secrecy throughput and energy efficiency. Numerical results reveal that, compared to traditional IRS-assisted or non-jamming schemes, our approach yields substantial performance gains. This highlights the potential of integrating IRS and jamming to provide more secure and energy-efficient communication solutions for WPCNs, particularly in the context of 6G IoT applications.

The 6G network is predicted to provide much faster network speeds, lower latency, and be able to support advanced mobile devices and systems so that it can meet all network requirements in the future. The use of high frequencies on 6G makes the technology sensitive to influences around the sender and receiver, such as the influence of Human Blockage. This research aims to determine the level of influence of human blockage on the 6G communication network on the signals received by users. In this research, a simulation method was carried out to see the effect of human blockage on the 6G channel model with a working frequency of 95 GHz using the NYUSIM (New York University Simulator) channel simulator. Apart from that, supporting data is used in the form of environmental conditions in the city of Banda Aceh. The results of this research show that the 6G channel model under the influence of Human Blockage obtained a received power value of -131.7 dBm with a pathloss value of 161.7 dBm and a delay of 11.9 ns. In general, the received power value is still not good based on the RSRP standard, and the pathloss and propagation delay values are still high.

The IoT ecosystem involves connected smart devices that support massive amount of data for processing and further analysis. The proliferating massive IoT network demands network connectivity, robust communication links and computational resources which puts huge load on the network. To overcome the communication overhead in the IoT landscape while offering optimal fault tolerance is the main challenge. This paper presents an intelligent solution for improving the communication efficiency of IoT network using active IRS technology. An active IRS aided communication framework is proposed which offers enhanced network connectivity by enabling controlled reflection amplitude. An algorithm is proposed which associates optimal active IRS to each AP-node link such that signal-to-interference-plus-noise ratio (SINR) is maximized. It is observed that the proposed association scheme in active IRS system offers an improvement of 19.04% in achievable rate over random association at AP transmit power p t of 20 dBm and IRS amplification power P A of 4 dBm. Furthermore, the system outage probability is carried out with change in p t and amplification gain a. The mean channel power is also evaluated for different IRS densities and AP densities under different IRS reflecting elements N , p t and P A . In the end, the performance comparison with passive IRS system and system with no IRS assistance is carried out. A use case scenario of active IRS-aided system in smart indoor communication is also discussed. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANET) are revolutionizing intelligent transportation systems (ITS), and as a result, research on their security is becoming increasingly important. As the primary security concern for VANET, authentication security is still quite difficult to achieve. Consequently, the prior knowledge of VANET is covered in this survey before outlining the primary security concerns. To set itself apart from previous surveys, this study suggests security properties and challenges among VANET. Next, the essential and significant features of a secure VANET system, such as confidentiality and integrity of data, and the availability of network systems have been reported, the authenticity of nodes and messages, and the refusal to deny data once it has been transmitted is detailed. Later, it outlined the requirement of the ITS which makes the survey unique. More importantly, the report on the most recent developments in VANET concentrates on the authentication schemes that have been proposed recently. The security features and authentication resistance against attacks, along with the overhead and efficiency of these schemes, are thoroughly examined and contrasted. A detailed analysis of V2V, V2I, and V2X authentication is been reported. Various cryptographic schemes have been discussed along with some advanced techniques such as Blockchain and hybrid schemes. An overview of the integration of 5G/6G networks is documented. Applications of VANET have been discussed in detail along with some open challenges in VANET. In summary, this work reviews a few lessons learned and explores different possibilities for future research. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) traffic management (TM) has focused on applications based on line-of-sight (LOS) links, such as aerial mapping, delivery services, farming, and monitoring. In the future dense UAV traffic situation, UAV-TM must properly maintain numerous fully autonomous UAVs beyond the line of sight. The capabilities of current cellular systems are restricted to enabling only terrestrial operations. The existing system, which proposes 6G-enabled UAV positions and relay paths based on IoT networks, does not include UAV-Traffic Management (UAV-TM). Therefore, the proposed method uses Deep Learning Methods to investigate the appropriate communication technology for advanced UAV-TM systems. This research presents a graph problem formulation for optimizing UAV positions and relay pathways in UAV-relayed IoT networks. Afterward, the encoder-decoder LSTM framework and one-dimensional convolutional neural network combined the inherent difficulty measurements with air traffic flow prediction. To handle the complicated issue of UAV traffic management on an abstract level, this study suggests a novel manner of arranging the uncontrolled, low-altitude airspace. Simulation findings demonstrate that, when the network is comparatively tiny, the suggested UAV-TM technique can achieve comparable results to brute-force searching with much less time complexity. The suggested model outperforms other methods in all cases having minimum RMSE value. The prediction error of the suggested model is specifically lower on average than that of shallow neural networks and other models. [ABSTRACT FROM AUTHOR]

As a leader in the global mobile communications scene, NTT DOCOMO is pursuing coexistence of artificial intelligence and humans, construction of sustainable networks, and development of innovative technologies. We spoke to Takaaki Sato, senior executive vice president of NTT DOCOMO, about the company's technology strategy for creating a new world of communication culture and outlook for technological development focused on 6G (the sixth-generation mobile communications system). [ABSTRACT FROM AUTHOR]