Scholarlink

<p>In recent days, fifth-generation infrastructure based on the Internet of Things has been instrumental in terahertz communication, leading to molecule absorption, blocking, and deep fades. These issues lead to intermittent behaviour in wireless networks, potentially causing delays. The growing user density and application demands are met by sharing machine type and device-to-device information. However, the limit for user support is indefinite due to varying allocations of applications and service channels. Irrespective of the user and application requirements, the scalability management is powered by a novel Dynamicity-assisted Service Assignment scheme. The proposed scheme balances service assignment and user management with scalable computing by following three steps. First, the reliability of this scheme is enhanced through federated learning, which maintains balanced density and service assignment. Second, the proposed scheme categorizes latency-aware and service-aware requests from the user equipment for granting independent and collaborative service access. The requests are classified into distinguished time and service intervals, regardless of density. Third, this alters the service distribution and request satisfaction from the previous to the current processing level, supporting heterogeneous users. Therefore, this scheme’s service access and response ratio are considerably high. The other metrics, such as latency, backlogs, and computational complexity, are verified to validate the performance of the proposed scheme.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dynamicity-assisted service assignment (DaSA) scheme with federated learning for improving Terahertz communication

  • 期刊论文

  • 发表日期：

    2026年2月19日

查看全文
  • Mohd Anjum,
  • Ibtehal Alazman,
  • Ashit Kumar Dutta,
  • Waseem Ahmad,
  • Sana Shahab,
  • Nouf Abdulrahman Alqahtani

摘要

In recent days, fifth-generation infrastructure based on the Internet of Things has been instrumental in terahertz communication, leading to molecule absorption, blocking, and deep fades. These issues lead to intermittent behaviour in wireless networks, potentially causing delays. The growing user density and application demands are met by sharing machine type and device-to-device information. However, the limit for user support is indefinite due to varying allocations of applications and service channels. Irrespective of the user and application requirements, the scalability management is powered by a novel Dynamicity-assisted Service Assignment scheme. The proposed scheme balances service assignment and user management with scalable computing by following three steps. First, the reliability of this scheme is enhanced through federated learning, which maintains balanced density and service assignment. Second, the proposed scheme categorizes latency-aware and service-aware requests from the user equipment for granting independent and collaborative service access. The requests are classified into distinguished time and service intervals, regardless of density. Third, this alters the service distribution and request satisfaction from the previous to the current processing level, supporting heterogeneous users. Therefore, this scheme’s service access and response ratio are considerably high. The other metrics, such as latency, backlogs, and computational complexity, are verified to validate the performance of the proposed scheme.