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The fifth-generation mobile network (5G), as the fundamental enabler of Industry 4.0, has facilitated digital transformation and smart manufacturing through AI and cloud computing (CC). However, B5G is viewed as a turning point that will fundamentally transform existing global trends in wireless communication practices as well as in the lives of masses. B5G foresees a world where physical–digital confluence takes place. This study intends to see the world beyond 5G with the transition to 6G assuming the lead as future wireless communication technology. However, despite several developments, the dream of an era without latency, unprecedented speed internet, and extraterrestrial communication has yet to become a reality. This article explores main impediments and challenges that the 5G–6G transition may face in achieving these greater ideals. This article furnishes the vision for 6G, facilitating technology infrastructures, challenges, and research leads towards the ultimate achievement of “technology for humanity” objective and better service to underprivileged people.

Increasing spectral bandwidth in 5G networks improves capacity but cannot fully address the heterogeneous and rapidly growing traffic demands. Heterogeneous ultra-dense networks (HUDNs) play a key role in offloading traffic across multi-tier deployments; however, their diverse base-station characteristics and diverse quality-of-service (QoS) requirements make resource allocation highly challenging. Traditional static resource-allocation approaches lack flexibility and often lead to inefficient spectrum utilization in such complex environments. This study aims to develop a joint user association-resource allocation (UA-RA) framework for 5G HUDNs that dynamically adapts to real-time network conditions to improve spectral efficiency and service ratio under high traffic loads. A software-defined networking controller centrally manages the UA-RA process by coordinating inter-cell resource redistribution through the lending of underutilized resource blocks between macro and small cells, mitigating repeated congestion. To further enhance adaptability, a neural network-adaptive resource allocation (NN-ARA) model is trained on UA-RA-driven simulation data to approximate efficient allocation decisions with low computational cost. A real-world evaluation is conducted using the downtown Los Angeles deployment. For performance validation, the proposed NN-ARA approach is compared with two representative baselines from the literature (Bouras et al. and Al-Ali et al.). Results show that NN-ARA achieves up to 20.8% and 11% higher downlink data rates in the macro and small tiers, respectively, and improves spectral efficiency by approximately 20.7% and 11.1%. It additionally reduces the average blocking ratio by up to 55%. These findings demonstrate that NN-ARA provides an adaptive, scalable, and SDN-coordinated solution for efficient spectrum utilization and service continuity in 5G and future 6G HUDNs.

The evolution of the fifth-generation (5G) new radio (NR) has progressed swiftly since the third generation partnership project (3GPP) standardized the first NR version (Release 15) in mid-2018. Nowadays, the world’s leading carriers are competing to provide various commercial services over 5G networks. Looking ahead to 2025 and beyond, it is expected that over 6.5 million 5G base stations will be installed to offer services to over 58% of the world’s population via over 100 billion 5G connections. Following the rapid development of 5G, an increasing number of commercialization use cases will drive the 5G network to continuously improve performance and expand capabilities. Hence, it is the right time to consider a well-defined framework and standardization for 5G NR evolution (5G-Advanced) to support commercialization between 2025 and 2030. First, this study addresses the key driving forces, requirements, usage scenarios, and capabilities of 5G-Advanced; then, it highlights the main technological challenges and introduces the top 10 promising technological directions in detail. Finally, other fascinating technological directions in 5G-Advanced are shortly mentioned.

Some critical applications of emergency, Active Safe Driving (ASD), eV2X, and LEO communications require ultra-low delay and highly reliable transmission according to beyond 5G-Advanced (5G-A), 6G, and LEO specifications. Related studies proposed various scheduling algorithms in terms of single and multiple QoS requirements. However, these approaches tend to prioritize traditional QoS requirements while neglecting crucial considerations such as bearer costs and associated benefits. Moreover, most scheduling neglects the carrying cost according to the radio resource state and the bringing reward from different types of flows. Thus, this paper proposes a novel cost-based flow scheduling (eSCFS) framework that utilizes an extended sigmoid function to dynamically prioritize flows, taking into account all relevant key factors. The principal objective is to reduce latency while optimizing the utilization of radio RB and maximizing the net benefits of 5G-A NR networks. The eSCFS method has been validated through numerical simulations, which demonstrated superior key performance metrics, including network latency, resource utilization, and overall profitability. Consequently, several objectives are thus achieved: 1) analyzing the QoS requirements of various services within limited radio resources, 2) proposing a novel vRB state-dependent dynamic flow scheduling and adaptive virtual radio RB management to maximize network performance.

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device’s signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.

This study presents the architectural design and implementation of a multi-RAT gateway (MRGW) supporting dual satellite and terrestrial connectivity that enables moving maritime vessels, such as autonomous surface ships, to be connected to multiple radio access networks in the maritime communication environment. We developed an MRGW combining LTE and very-small-aperture terminal (VSAT) access networks to realize access traffic steering, switching, and splitting functionalities between them. In addition, we developed communication interfaces between the MRGW and end-devices connecting to their corresponding radio access networks, as well as between the MRGW and the digital bridge system of an autonomous surface ship, enabling the MRGW to collect wireless channel information from each RAT end-device and provide the collected data to the digital bridge system to determine the optimal navigation route for the autonomous surface ship. Experiments on the MRGW with LTE and VSAT end-devices are conducted at sea near Ulsan city and the Kumsan satellite service center in Korea. Through validation experiments on a real maritime communication testbed, we demonstrate the feasibility of future maritime communication technologies capable of providing the minimum performance necessary for autonomous surface ships or digitized aids to navigation (A to N) systems.

The open nature and heterogeneous architecture of Open Radio Access Network (Open RAN) undermine the consistency of security policies and broaden the attack surface, thereby increasing the risk of security vulnerabilities. The dynamic nature of network performance and traffic patterns in Open RAN necessitates advanced detection models that can overcome the constraints of traditional techniques and adapt to evolving behaviors. This study presents a methodology for effectively detecting malicious traffic in Open RAN by utilizing an Artificial-Intelligence/Machine-Learning (AI/ML) Framework. A hybrid Transformer–Convolutional-Neural-Network (Transformer-CNN) ensemble model is employed for anomaly detection. The proposed model generates final predictions through a soft-voting technique based on the predictive outputs of the two models with distinct features. This approach improves accuracy by up to 1.06% and F1 score by 1.48% compared with a hard-voting technique to determine the final prediction. Furthermore, the proposed model achieves an average accuracy of approximately 98.3% depending on the time step, exhibiting a 1.43% increase in accuracy over single-model approaches. Unlike single-model approaches, which are prone to overfitting, the ensemble model resolves the overfitting problem by reducing the deviation in validation loss.

Recent 5G-Advanced cellular specifications introduce several positioning enhancements, including carrier phase measurements that enable high-precision 3D localization at the accuracy scale of used radio frequency (RF) wavelength. Besides localization, in numerous use cases from extended reality (XR) headsets to industrial automation and heavy machines, accurate knowledge of the device 3D orientation is of paramount importance together with low-latency operation. In this paper, utilizing multi-sensor carrier phase and Doppler measurements, we address high-precision joint tracking of device 3D location and orientation, where the full device state can be directly estimated at the network side, thus enabling very low latency and response times for possible device-related network actions. The proposed tracking approach builds on extended Kalman filter framework and is supplemented with a particle filter solution which handles the challenging integer ambiguity problem and maintains synchronization between the network and device under clock drifting. Besides tracking the user device orientation with conventional yaw, pitch and roll angles, also quaternion-based tracking approach is considered. Furthermore, multi-frequency carrier phase and Doppler measurements through carrier aggregation are addressed to further improve the performance. Based on numerical evaluations, we show that the proposed 3D location and orientation tracking achieves the derived theoretical performance bounds and reaches sub-centimeter and sub-degree accuracy for position and orientation estimation, respectively.

Positioning based on wireless communication networks has great application potential. In this paper, we propose a positioning method for the 5G-Advanced (5GA) or 6G network. Firstly, we establish the communication link and generate the map-based hybrid channel model based on 3GPP standards and open-source maps, where each multipath channel is expanded into a cluster that contains 20 rays. Then, we improve the Orthogonal-Matching-Pursuit (OMP) algorithm, which can estimate Angle-Of-Arrival (AOA) through only one OFDM symbol and does not require the signal to have a very narrow bandwidth, a high Signal-to-Noise-Ratio (SNR) or multiple snapshots like the classical OMP algorithm. Finally, we propose a positioning algorithm, which locates the target through the estimated AOA and the open-source map. The proposed method can locate the target with a single Base Station and has the advantages of lower delay, lower cost, and higher accuracy. The simulation results show that the positioning error of the proposed algorithm is submeter in 63% of the cases and less than 2.2 m in 80% of the cases.

With the deepening integration of 5G-Advanced (5G-A) technology into smart manufacturing, the large-scale deployment of dynamic terminals—such as mobile robots and automated guided vehicles (AGVs)—within industrial private networks introduces complex, time-varying penetration and path losses. This significantly degrades the accuracy of conventional signal quality and capacity estimation methods, which were primarily designed for static terminal scenarios, thereby posing substantial challenges to coverage and deployment planning of industrial 5G access points, with downstream implications for power capacity dimensioning. To address this problem, this paper proposes a coverage-driven base station deployment optimization method formulated as a combinatorial optimization problem. The study constructs a signal strength assessment and network throughput calculation model tailored for dynamic industrial environments. This model captures the joint impact of terminal mobility and environmental obstacles on signal propagation, thereby enabling more reliable estimation of coverage performance and power consumption. Furthermore, by formulating the base station placement optimization as a combinatorial optimization problem, and by introducing mechanisms for equivalent transformation of the objective function and data preprocessing, the proposed method substantially reduces redundant computations during heuristic iterations. Simulation results verify that, compared with conventional static planning approaches, the proposed scheme enhances both the accuracy and computational efficiency of deployment planning while maintaining coverage quality. This work provides a theoretical foundation and a practical methodology for deploying reliable and energy-efficient industrial 5G-A private networks.