2018· International Journal of Modern Research in Science & Engineering· 0 citations
TL;DR
This paper conducts a comprehensive literature survey to examine existing optimization techniques and proposes an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing that demonstrates the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability.
Abstract
The rapid advancement of wireless communication has led to the emergence of the fifth-generation (5G) network, which aims to provide ultra-reliable low-latency communication (URLLC) while ensuring high data transmission rates. Optimizing data transmission in 5G networks is critical for supporting real-time applications such as autonomous vehicles, telemedicine, industrial automation, and smart cities. This paper explores various techniques and strategies to enhance data transmission efficiency, minimize latency, and improve reliability in 5G networks. We analyze the key performance indicators (KPIs) that influence data transmission, including bandwidth utilization, network slicing, and multiple access techniques. Furthermore, we discuss the role of edge computing, artificial intelligence (AI)-driven network management, and adaptive modulation techniques in optimizing data transmission. The paper also highlights the impact of interference management, energy efficiency considerations, and security protocols on 5G network performance. We conduct a comprehensive literature survey to examine existing optimization techniques and propose an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing. Through simulation and analytical results, we demonstrate the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability. The findings contribute to the ongoing efforts in optimizing 5G networks and lay the foundation for future research in beyond-5G (B5G) and sixth-generation (6G) communication systems.
This paper conducts a comprehensive literature survey to examine existing optimization techniques and proposes an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing that demonstrates the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability.
William Hughes, Marta Silva· International Journal of Dat...· 0 citations
Wireless Sensor Networks (WSNs) have gained significant attention due to their wide range of applications, including environmental monitoring, healthcare, industrial automation, and military operations. The primary challenge in WSNs is to ensure reliable data transmission while maintaining energy efficiency and network longevity. Multi-path data transmission has emerged as a promising technique to enhance the reliability of WSNs by mitigating data loss, reducing congestion, and improving fault tolerance. This paper presents a comprehensive study on multi-path data transmission mechanisms in WSNs, analyzing their impact on network performance, energy consumption, and data reliability. We explore various multi-path routing protocols, including Disjoint Path Routing, Braided Path Routing, and Hybrid Approaches, and assess their effectiveness in different network scenarios. Additionally, we discuss the challenges associated with multi-path data transmission, such as path redundancy, interference, and increased computational overhead. Through extensive simulations and comparative analysis, we demonstrate that multi-path data transmission significantly enhances network reliability while ensuring optimal resource utilization. The findings of this study provide valuable insights for designing robust and efficient WSNs, thereby contributing to advancements in the field of wireless communication.
Anatoly Kitov· International Journal of Dat...· 0 citations
Wireless Sensor Networks (WSNs) have gained significant attention due to their wide range of applications, including environmental monitoring, healthcare, industrial automation, and military operations. The primary challenge in WSNs is to ensure reliable data transmission while maintaining energy efficiency and network longevity. Multi-path data transmission has emerged as a promising technique to enhance the reliability of WSNs by mitigating data loss, reducing congestion, and improving fault tolerance. This paper presents a comprehensive study on multi-path data transmission mechanisms in WSNs, analyzing their impact on network performance, energy consumption, and data reliability. We explore various multi-path routing protocols, including Disjoint Path Routing, Braided Path Routing, and Hybrid Approaches, and assess their effectiveness in different network scenarios. Additionally, we discuss the challenges associated with multi-path data transmission, such as path redundancy, interference, and increased computational overhead. Through extensive simulations and comparative analysis, we demonstrate that multi-path data transmission significantly enhances network reliability while ensuring optimal resource utilization. The findings of this study provide valuable insights for designing robust and efficient WSNs, thereby contributing to advancements in the field of wireless communication.
Hari A. Patel, Geetha Ramasamy· International Journal of Mod...· 0 citations
This research Paper focuses on an Ultra-Dense Network (UDN) is a core enabling technology for 5G and 6G wireless systems, proposed to meet escalating capacity demands and support new high-rate, low-latency services. The fundamental principle is network densification, achieved by deploying a massive number of low-power Access Points (APs) and communication links per unit area, dramatically shortening the distance between transmitters and receivers to improve signal quality and spatial frequency reuse. Ultimately, the successful operation of UDNs relies heavily on advanced, AI-driven management systems to dynamically optimize resources, manage interference, and ensure seamless, high-performance connectivity in an inherently complex environment: Our key objectives are improving the Massive Capacity and Data Rates, Enhanced Coverage and Reliability, Ultra-Low Latency, Massive Connectivity Internet of Things (IoT) to maintaining the advanced resilient communication system all the time and every times.
P. Pradhan, Pramod D Gangejar· Journal of Ad-hoc Network an...· 0 citations
The proposed framework achieves 20-30% reduced latency, a 15-35% reduction in energy consumption, and an 18-28% throughput enhancement compared to existing methods, and ensures a wide improvement in reliability and adaptability in 5G V2X communication networks.
A. Sangeetha, R. Krishnan, T. Sathya et al.· Scientific Reports· 0 citations
—To reduce power consumption and extend network lifespan, academic and industrial groups have focused on energy-efficiency approaches for Next Generation Networks (NGNs). Fifth-generation (5G) networks offer a large number of services at high data rates, low latency, and massive connectivity. Increasing volumes of heterogeneous traffic from billions of devices, ranging from smartphones to intelligent transport systems, significantly challenge network resource utilization, particularly power consumption. This study targets energy-efficient resource allocation in sliced 5G systems, ensuring service-level guarantees for heterogeneous applications through intelligent optimization. This work proposes a novel hybrid optimization framework for energy-aware resource provisioning in 5G sliced networks using Hybrid Grey Wolf–Tasmanian Devil Optimization (HGWTDO) with a Linear Pattern Search (LPS) refinement technique. While HGWTDO combines the global search ability of Grey Wolf Optimization (GWO) and the exploitation abilities of the Tasmanian Devil Optimizer, the addition of LPS provides accurate local convergence. LPS has been integrated into the proposed solution to enhance optimization results. The solution is augmented with a Classification Tree-based classification that assigns users to their corresponding slices for Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and massive Machine-Type Communication (mMTC) based on quality of service (QoS) requirements. The suggested system provides improved power efficiency under QoS constraints and is an intelligent, scalable solution for energy-aware 5G network slicing compared with existing techniques.
P. Raddy, Sudhanva A M, Arathi R. Shankar· Journal of Communications So...· 0 citations