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
