This study explores the nascent frontier of connectivity technologies beyond the current 5G era, examining a range of innovative solutions primed to define future communication networks. Through a meticulous investigation of emerging approaches such as Li-Fi, Terahertz Communication, Visible Light Communication (VLC), and Massive Internet of Things (IoT), this research paper provides clarity on their capabilities, advantages, drawbacks, and use cases. By bringing these alternatives to light, this analysis aims to develop a well-rounded perspective of the post-5G wireless landscape, offering perspective into potential pathways connectivity technologies might take as they progress beyond existing 5G paradigms.
With the in-depth advancement of "Internet + Government Services," the interconnection and interoperability of electronic certificates are steadily accelerating. However, as business operations become increasingly complex, traditional access control mechanisms are no longer sufficient to meet the current security requirements for accessing electronic certificates. The Attribute-Based Access Control (ABAC) model has garnered widespread attention from academia and industry due to its flexible policy expression and adaptability to complex business environments. Nonetheless, migrating policies from existing heterogeneous systems to the ABAC model and optimizing them for compatibility remain significant technical challenges. To address the issue of heterogeneous policy migration in access control, this paper proposes an attribute-based access control policy mining method that integrates decision trees with clustering. By combining the advantages of decision trees and deep learning, a deep embedded clustering method based on a multi-layer encoder driven by decision trees is introduced. This approach effectively handles the complex structures and nonlinear relationships in raw data, thereby improving clustering quality. Additionally, to address the issue of policy inaccuracy caused by redundant or incomplete rules, a policy pruning and optimization method is proposed to enhance the accuracy and usability of the extracted policies. Experimental results demonstrate that the proposed method effectively improves the accuracy of access control policy extraction.
This paper examines Advanced Television System Committee (ATSC) 3.0 diversity reception in mobile broadcasting environments. Link-level simulations are conducted to assess error performance under vehicular reception scenarios. Considering practical real-world conditions, two distinct physical layer pipes (PLPs) are evaluated, representing services targeted for mobile and stationary services. These PLPs are characterized by their sub-carrier spacing, which determines resilience to Doppler effects. Additionally, this study highlights the performance gain achieved through maximal ratio combining (MRC) in ATSC 3.0 applications over the other diversity techniques, such as equal gain combining (EGC) and selection combining (SC). The results demonstrate the superiority of 4-antenna MRC, which is applicable to commercial vehicular receivers, across various channel conditions, including single-frequency network (SFN) and harsh mobile environments.
In the past year, Geographically Segmented Localcasting (GSL) has garnered significant attention from the broadcast industry. This is due to its implementation of Local Content Insertion (LCI) within a Single Frequency Network (SFN) environment, which aids local multimedia and data broadcasting services. LCI is achieved through Layered Division Multiplexing (LDM). The Core Layer (CL) of LDM provides wide-area broadcasting, forming a large-scale SFN, while the Enhanced Layer (EL) facilitates localcasting services. Since the Advanced Television Systems Committee (ATSC) 3.0 standard was established over a decade ago, LCI/GSL represents a new business model. Originally designed for channel estimation in SFN, the pilot signals are not suitable for estimating the localcasting channels in ELs. This can lead to Local Channel Profile Mismatch (LCPM) in the ELs, significantly impacting the performance of GSL. Therefore, this paper models and analyzes the impact of LCPM on GSL scenario reception under various conditions. A method based on the Denoising Diffusion Probabilistic Model (DDPM) is proposed to reduce LCPM and co-channel interference. This approach can achieve effective performance gains solely through algorithmic changes at the receiver, without altering the ATSC 3.0 protocol framework. It is also applicable to similar scenarios in cellular wireless networks (5G/6G) for enhancement
With the development of technology, the need for a sustainable and resilient energy system is becoming more and more prominent. Contemporary energy infrastructure relies heavily on carbon-intensive fuels, which significantly slows the progress of carbon peaking plans. However, artificial intelligence(AI) technology can help catalyze the transformation needed in the energy industry. This study explores how AI can drive the implementation of low-carbon solutions. The study proposes a comprehensive approach to energy construction. Firstly, we designed an integrated energy curriculum centered on topics like renewable technologies, intelligent optimization, data analytics and their interdisciplinary intersections. The aim is to cultivate a talent pool equipped with cross-disciplinary skills essential for tackling complex sustainability challenges. Secondly, industry-academia collaboration platforms were established to pilot AI applications promoting enterprise-level reforms. Production planning, supply chain management and demand-side flexibility were optimized using cloud-based algorithms. Initial case studies indicate AI yields cost savings of 15-20% and 10-15% carbon reductions. Additionally, a digital twin modeling framework of city-scale energy systems was developed. An AI-enabled "energy internet" concept was explored to realize benefits such as peak shaving, load balancing and failure forecasting through interconnected infrastructure and smart end-use devices. Finally, further research directions were discussed to address data availability, computing power and policy support issues limiting full realization of AI's decarbonization potential.
The increasing demand for multi-UAV collaborative systems in real-world scenarios highlight the limitations of conventional approaches, which mostly assumes centralized coordination or interference-free communication. This paper investigates the problem of decentralized multi-UAV target encirclement in a battlefield-like environment characterized by communication constraints, such as limited range and the absence of a central controller. To address these challenges, we propose an approach based on Multi-Agent Deep Reinforcement Learning (MADRL) with LSTM networks, enabling UAVs to achieve effective path planning and state prediction in obstacle-rich environments. Each UAV is equipped with exploration capabilities, utilizing simulated multi-line LiDAR sensors to perceive the environment and navigate in real time. Simulation experiments are conducted in an environment with obstacles; and they also validate the effectiveness of the proposed algorithm in achieving coordinated target encirclement under constrained conditions.
This paper proposes a continual learning strategy that combines Convolutional Neural Networks (CNNs) with the Elastic Weight Consolidation (EWC) mechanism. The objective is to optimize a single LED-based Visible Light Positioning (VLP) system, enhancing its stability and generalization in dynamic environments. The innovation lies in the weighted feedback mechanism of EWC, which enables the neural network to retain previously learned knowledge while acquiring new tasks, thereby improving generalization and reducing the need for extensive training data. Laboratory experiments demonstrated that the EWC-enhanced ResNet50 model maintained a positioning accuracy within 4 cm at a height of 160 cm, even under scenarios with human movement and sunlight interference, requiring only 27% of the data from the new environment to achieve a positioning accuracy of 5 cm.
This paper presents two techniques for distributing ATSC 3.0 STLTP signals over VPNs that do not support multicast, based on their successful implementation in the KBS main broadcast network. As of March 2025, KBS has achieved 80% population coverage for ATSC 3.0, while facing challenges such as seasonal fading in long-distance microwave transmissions and the high cost of leased lines. Although public Internet networks offer a cost-effective alternative, they lack multicast support and QoS guarantees, making them unsuitable for ATSC 3.0 distribution. To address this, we propose two solutions: 1) SRT (Secure Reliable Transport), which encapsulates multicast signals into unicast packets with error correction, and 2) GRE (Generic Routing Encapsulation) tunneling, which enables multicast transmission using Layer 3 switches without additional equipment. Both methods were successfully implemented, improving network stability and reducing costs. This work provides key technical solutions for reliable ATSC 3.0 signal distribution, supporting the expansion and stability of ATSC 3.0 broadcast networks.
The swift expansion of digital networks has resulted in an eruption of accessible information and accumulated understanding online. This paper explores the obstacles and developments pertaining to the handling of interconnected information and familiarity. The discussion comprises a range of relevant topics, including information sourcing, natural language decoding, data scrutiny, and conceptual portrayal. Through an inspection of prevailing directions and developments, this paper underscores the significance of proficient handling approaches to glean useful discernments from the immense ocean of digital material.
The rapid growth of multimedia applications and the rising expectations for an enhanced Quality of Experience (QoE) among users have emphasised the need for innovative approaches to ensure efficient delivery of video content in 5G and Beyond 5G (B5G) networks. Machine Learning (ML) techniques are increasingly being explored to address these challenges by enabling intelligent traffic management and QoE optimisation. Within this landscape, Software-Defined Networking (SDN) plays a pivotal role as a facilitator of dynamic resource allocation and QoE-centric network management. This paper introduces AIMTWIN, a reinforcement learning (RL)-driven Digital Twin framework designed to optimise multimedia traffic management in B5G SDN environments. AIMTWIN integrates real-time telemetry from physical networks with a dynamic virtual model to provide adaptive and efficient traffic routing. By prioritising static paths for QoS-critical multimedia flows and dynamically managing background traffic, the framework delivers superior network performance and user satisfaction. Experimental results on small and medium scale topologies highlight AIMTWIN’s ability to achieve consistently Excellent QoE compared to state-of-the-art routing methods, positioning it as a scalable and robust solution for next-generation networks.
Energy-efficient routing protocols are needed to fully utilize the benefits of directional antennas in wireless sensor networks. We investigate the Antenna Orientation (AO) problem for optimizing directional antenna orientations to minimize transmit energy while maintaining symmetric connectivity. Consider a wireless sensor network where each node has k directional antennas (3 <= k <= 4) with beamwidth theta. Given an initial omni-directional topology, the goal is to orient the directional antennas to achieve a symmetrically connected communication graph using the minimum possible transmit range r. We develop an efficient O(n log n) algorithm that provably achieves optimal range r=2sin(180/ k) for this AO problem. The algorithm jointly considers directional link quality, antenna geometry, and transmit energy when computing optimal antenna orientations. Simulation results demonstrate our AO algorithm establishes energy-efficient route paths by exploiting directional communications. The proposed techniques provide fundamental advances towards realizing the benefits of directional antennas for energy-constrained wireless sensor networking applications. Our future work includes implementing and evaluating the AO algorithms on wireless sensor testbeds with directional antennas.
This study provides a comprehensive experimental analysis of router performance when processing full-scale Resource Public Key Infrastructure (RPKI) data versus IPv6-specific RPKI implementations, addressing critical operational challenges posed by IPv4 exhaustion and accelerating IPv6 adoption. Through a controlled testbed utilizing EVE-NG network emulation and modified rpstir2 software with enhanced RPKI-to-Router (RTR) protocol capabilities, we quantitatively evaluated five key metrics: storage, synchronization time, CPU utilization, bandwidth efficiency, and packet loss. Our findings reveal that IPv6-only RPKI configurations achieve 73.8% memory reduction (11 MB vs 42 MB), 81.6% faster synchronization time (4.222 s vs 22.959 s), and 78.5% lower bandwidth consumption (0.246 Mbps vs 1.145 Mbps) compared to full-scale RPKI implementations. These improvements stem from processing 81,333 IPv6 Route Origin Authorizations (ROAs) versus 452,650 total ROA in full deployments. The results demonstrate a significant reduction in memory space and shortening of synchronization time when using an IPv6-only cache compared to a full-scale RPKI data cache. The study aims to provide a clear quantitative analysis of the performance implications for routers handling full-scale versus IPv6-only RPKI data, which is of great significance for the deployment of IPv6-only network in the future.
The advent of sixth-generation (6G) networks promises to unlock previously unexplored opportunities for ultra-low latency communications, massive connectivity, and intelligent edge computing. However, these improvements pose significant security and privacy challenges, especially in data-intensive applications such as digital twins. In this paper, we propose the Secure Homomorphic Encryption and D2D-aided Digital Twin (SHEDT) framework, which integrates device-to-device (D2D) communication, homomorphic encryption, and multi-access edge computing (MEC) to improve security and efficiency in the creation of 6G-oriented digital twins. The SHEDT framework leverages D2D communications to enhance the performance in collecting data useful for the creation of digital twins. In addition, homomorphic encryption is exploited as a solution to enable operations on encrypted data without the need to decrypt it, thus ensuring end-to-end data confidentiality. Finally, integrating the digital twin into the MEC servers allows optimizing access to the data and information obtained. Performance evaluations show that SHEDT ensures an improvement in resource allocation, energy consumption of resource-constrained network nodes, and data transmission times. This paper paves the way for future work on secure, scalable, and efficient digital twin applications in 6G networks.
Biological prompting or human teleoperation have been appealing to many researchers lately. Hands-free and voice-free command or control of vehicles, aircraft, machines, robots, etc. would provide huge convenience to users and allow them for multi-tasking. In this work, we would like to dedicate to a new biological prompting function, namely automatic directional control using instantaneous eye gaze data. In this work, we would like to investigate how to apply advanced machine-learning models to design novel automatic gaze-direction identification techniques. We explore the difficulty, namely infliction with outliers and variations, in dealing with eye-tracking data and propose to transform the original data from the time domain into the (statistical) density domain. Thus, we may obtain robust density features by mitigating outliers and variations. Based on our proposed new input features, we can formulate the gaze-direction identification problem as a multi-class classification problem and then design the corresponding random forest classifier. For identification of five different gaze directions (center, up, down, left, and right), our proposed new approach can register up to a 78% accuracy, which outperforms other existing machine-learning models using the same training and test data.
5G base stations have been widely used. At the same time, 4G macro stations also have irreplaceable convenience. In order to better utilize the existing infrastructure and leverage the characteristics of the 5G base stations, this article proposes a cooperative placement method for 4G&5G base stations that utilizes an improved particle swarm optimization. To fit for the complex terrain of cities and maximize the use of different types of base stations, it also utilizes the DBSCAN algorithm to slice the map according to the density of buildings for clustering, and then places different types of base stations on different types of terrains. An improved particle swarm optimization algorithm is proposed to search for the best placement strategy on the complex terrain for maximizing the coverage of signal.
Currently, automatic driving represents the primary application area for LiDAR point clouds, with 3D object detection being a crucial task for achieving autonomous navigation. However, the substantial volume of point cloud data poses significant challenges for transmission, necessitating the development of efficient compression algorithms. Most existing point cloud compression methods are optimized for human perception, prioritizing signal fidelity over the requirements of downstream machine tasks. Given that current LiDAR sensing applications typically convert point clouds into 2D Bird’s Eye View (BEV) representations for real-time, high-precision sensing, this paper proposes a novel LiDAR point cloud compression framework specifically designed for 3D object detection. Our framework focuses on compressing the 2D BEV features and is driven by the object detection task. Experimental results demonstrate that on the KITTI dataset, compared to the MPEG-standardized G-PCC algorithm, our method achieves BD-rate gains of 68.28%, 49.33%, and 70.87% for vehicle, pedestrian, and cyclist detection, respectively, in the object detection task.
Advancements in power system intelligence and the integration of emergent technologies have significantly transformed grid communication needs. This paper conducts a thorough analysis of developments within this pivotal area. Examined is the function of communicative solutions in facilitating real-time observation, management, and harmonization within smart energy systems. Discussed are how techniques including the Internet of Things, fifth generation cellular technology, artificial intelligence, and distributed ledger systems are integrating to reshape the domain of intelligent grid interconnectivity. Moreover, difficulties such as cyber risks and interworking capabilities are explored. Via a comprehensive evaluation of contemporary works and practical applications, this paper illuminates the present condition and forthcoming path of intelligent system communications within the energy sector.
Point-to-Multipoint (PtM) communications were identified by 3GPP as a requirement during the start of 5G. Despite this, it was not until Release 17 that the New Radio (NR) native PtM mode was standardized, named 5G Multicast Broadcast Systems (MBS). Most notably, it added new Network Functions in the 5G Core while fully reutilizing NR physical layer to ease adoption and uptake amongst manufacturers. In this context, the paper provides the design and goals of an MBS validation pilot, fully end-to-end (from 5G Core to Receiver) based on open-source implementation from the 5G-MAG Reference Tools repositories. Moreover, this experiment leverages the programmability of Software-Defined Radio equipment and experimentation framework provided by the European 6G-SANDBOX SNS project, the Trial Network Lifecycle Manager to orchestrate and log the measurements performed.
As wireless communication systems evolve, new frameworks place increasing demands on terminal performance, including throughput, latency, power consumption, and size. Software-Defined Radio (SDR) systems have the potential to support multiple communication standards and enabling on-demand upgrades due to their flexibility. However, purely software-based implementations for physical layer protocols reduce efficiency, necessitating the use of external accelerators such as FPGA for specialized computation. Specifically, channel decoding contributes a lot to the physical layer’s complexity, and thus is highly demand for FPGA specialized implementation while retaining the universality of the decoding architecture in some level. This paper presents an adaptive quantized and normalized QC-LDPC universal decoder based on a block-parallel architecture with pipeline and instruction-driven approach. The proposed decoder improves throughput and reduces computational complexity while maintaining flexibility for various QC-LDPC codes. Experimental results demonstrate that the adaptive quantization and normalization scheme significantly outperforms existing methods in terms of decoding performance and maximum frequency, offering a more efficient solution for SDR systems.
Introduced a solution that combines 5G public network with low altitude coverage. Analyzed the feasibility and possible problems of adopting a 5G public network solution that takes into account low altitude cover-age, proposed an optimized solution that takes into account low altitude coverage requirements, and con-ducted testing and verification. Finally, based on the analysis of the test results, recommendations are pro-vided for applications that consider both public network and low altitude coverage under different altitude conditions.
