Cross-Layer Optimized Wireless Architecture for High-Speed Genomic Data Transmission in Next-Generation Healthcare Networks

Abstract

With the exponential growth of genomic data, the need for efficient, high-speed, and reliable wireless transmission systems has become paramount in next-generation healthcare applications. Traditional wireless communication architectures face challenges in handling massive genomic datasets due to network congestion, latency, and energy inefficiency. In this study, we propose a novel Cross-Layer Optimized Wireless Architecture (CLOWA) tailored for high-speed genomic data transmission. Our approach integrates adaptive resource allocation, dynamic packet scheduling, and AI-driven error correction mechanisms across the physical, MAC, and network layers to optimize throughput and energy efficiency.
Leveraging 5G and beyond wireless technologies, our proposed framework employs Quality-Aware Data Aggregation (QADA) techniques to prioritize and compress genomic packets while maintaining data integrity. A machine learning-based Cross-Layer Traffic Prediction Model (CLTPM) further enhances transmission reliability by dynamically adjusting parameters based on network conditions. We validate CLOWA using real genomic datasets in a simulated wireless environment, demonstrating up to 40% improved throughput, 30% reduced transmission latency, and 25% lower energy consumption compared to conventional wireless architectures.
This research paves the way for real-time genomic data sharing in telemedicine, remote genomic diagnostics, and personalized medicine, ensuring efficient and secure wireless communication for next-generation healthcare applications.